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The timely and accurate acquisition of information on the distribution of the crop planting structure in the Loess Plateau of eastern Gansu Province, one of the most important agricultural areas in Western China, is crucial for promoting fine management of agriculture and ensuring food security. This study uses multi-temporal high-resolution remote sensing images to determine optimal segmentation scales for various crops, employing the estimation of scale parameter 2 (ESP2) tool and the Ratio of Mean Absolute Deviation to Standard Deviation (RMAS) model. The Canny edge detection algorithm is then applied for multi-scale image segmentation. By incorporating crop phenological factors and using the L1-regularized logistic regression model, we optimized 39 spatial feature factors—including spectral, textural, geometric, and index features. Within a multi-level classification framework, the Random Forest (RF) classifier and Convolutional Neural Network (CNN) model are used to classify the cropping patterns in four test areas based on the multi-scale segmented images. The results indicate that integrating the Canny edge detection algorithm with the optimal segmentation scales calculated using the ESP2 tool and RMAS model produces crop parcels with more complete boundaries and better separability. Additionally, optimizing spatial features using the L1-regularized logistic regression model, combined with phenological information, enhances classification accuracy. Within the OBIC framework, the RF classifier achieves higher accuracy in classifying cropping patterns. The overall classification accuracies for the four test areas are 91.93%, 94.92%, 89.37%, and 90.68%, respectively. This paper introduced crop phenological factors, effectively improving the extraction precision of the shattered agricultural planting structure in the Loess Plateau of eastern Gansu Province. Its findings have important application value in crop monitoring, management, food security and other related fields. [ABSTRACT FROM AUTHOR]

Urban informal settlements (UISs) are densely populated and poorly developed residential areas in urban areas. The mapping of UISs using remote sensing is crucial for urban planning and management. However, the large-scale extraction of UISs is impeded by the labor-intensive task of collecting numerous training samples and the lack of automatic and effective city partition. To overcome these challenges, we proposed a large-scale extraction framework for UISs based on semantic segmentation of highresolution remote sensing images. Utilizing Deeplab V3 Plus as the foundational extraction model, the proposed framework introduces fast sample collection based on GLCM features. Besides, an automatic city partition approach combined with clustering and fine-tuning was proposed to enhance the performance on extracting a specific category of UISs. The results of the case study conducted in 36 major Chinese cities show that the proposed framework achieved good performance, with an overall F1 score of 85.76%. Furthermore, comparative assessments were performed to demonstrate the effectiveness of automatic city partition. The proposed framework offers a practical approach for the largescale extraction of UISs, which holds great significance for sustainable development, poverty estimation, infrastructure construction, and urban planning. [ABSTRACT FROM AUTHOR]

BACKGROUND: Cardiovascular diseases are the top cause of death in China. Manual segmentation of cardiovascular images, prone to errors, demands an automated, rapid, and precise solution for clinical diagnosis. OBJECTIVE: The paper highlights deep learning in automatic cardiovascular image segmentation, efficiently identifying pixel regions of interest for auxiliary diagnosis and research in cardiovascular diseases. METHODS: In our study, we introduce innovative Region Weighted Fusion (RWF) and Shape Feature Refinement (SFR) modules, utilizing polarized self-attention for significant performance improvement in multiscale feature integration and shape fine-tuning. The RWF module includes reshaping, weight computation, and feature fusion, enhancing high-resolution attention computation and reducing information loss. Model optimization through loss functions offers a more reliable solution for cardiovascular medical image processing. RESULTS: Our method excels in segmentation accuracy, emphasizing the vital role of the RWF module. It demonstrates outstanding performance in cardiovascular image segmentation, potentially raising clinical practice standards. CONCLUSIONS: Our method ensures reliable medical image processing, guiding cardiovascular segmentation for future advancements in practical healthcare and contributing scientifically to enhanced disease diagnosis and treatment. [ABSTRACT FROM AUTHOR]

Due to the capability of multiplexing thousands of sensors on a single optical cable, ultra-weak fiber Bragg grating (UWFBG) vibration sensing technology has been utilized in monitoring the vibration response of large-scale infrastructures, particularly urban railway tracks, and the volume of the collected monitoring data can be huge with the great number of sensors. Even though the train-induced vibration responses of urban railway tracks constitute the most informative and crucial component, they comprised less than 7% of the total operational period. This is mainly attributed to the temporal sparsity of commuting trains. Consequently, the majority of the stored data consisted of low-informative environmental noise and interference excitation data, leading to an inefficient structural health monitoring (SHM) system. To address this issue, this paper introduced an adaptive monitoring strategy for railway track structures, which is capable of identifying train-load patterns by leveraging deep learning techniques. Inspired by image semantic segmentation, a U-net model with one-dimensional convolution layers (U-net-1D) was developed for the pointwise classification of vibration monitoring data. The proposed model was trained and validated using a dataset obtained from an actual urban railway track in China. Results indicated that the proposed method outperforms the traditional dual-threshold method, achieving an Intersection over Union (IoU) of 94.27% on the segmentation task of the test dataset. [ABSTRACT FROM AUTHOR]

In order to solve the problem of image boundary segmentation caused by the irregularity of paddy fields in southern China, a high-precision segmentation method based on the improved MultiResUNet model for paddy field mapping is proposed, combining the characteristics of paddy field scenes. We introduce the attention gate (AG) mechanism at the end of the encoder–decoder skip connections in the MultiResUNet model to generate the weights and highlight the response of the field ridge area, add an atrous spatial pyramid pooling (ASPP) module after the end of the encoder down-sampling, use an appropriate combination of expansion rates to improve the identification of small-scale edge details, use 1 × 1 convolution to improve the range of the sensory field after bilinear interpolation to increase the segmentation accuracy, and, thus, construct the AM-UNet paddy field ridge segmentation model. The experimental results show that the IoU, precision, and F1 value of the AM-UNet model are 88.74%, 93.45%, and 93.95%, respectively, and that inference time for a single image is 168ms, enabling accurate and real-time segmentation of field ridges in a complex paddy field environment. Thus, the AM-UNet model can provide technical support for the development of vision-based automatic navigation systems for agricultural machines. [ABSTRACT FROM AUTHOR]

This study employed a comprehensive approach utilizing X-ray diffraction, scanning electron microscopy (computed), computed tomography (CT) three-dimensional scanning, uniaxial compressive testing, acoustic emission (AE), and digital image correlation to investigate the micromorphology, mechanical properties, macro–microscopic fractal characteristics, failure modes, and mineral composition changes in acid–base coal samples from underground reservoirs. The findings from this study indicate that, in acidic environments, calcite undergoes acidolysis to form calcium chloride (CaCl2), whereas kaolinite reacts with alkaline substances to produce albite (Na2Al2Si2O8). The average elastic modulus of the coal samples treated with strong acids or alkalis decreased by 51.08% and 38.17%, respectively, when compared to the naturally dried samples. After strong acid treatment (pH = 2), the post-peak modulus decreased to its lowest value of 1.78 GPa. The fracture threshold in the plastic phase of the acidic coal samples decreased by approximately 60% when compared to that of the naturally dried samples, with the cumulative AE energy reaching a maximum of 2.46 × 1010 aJ. CT image segmentation revealed that with higher H+ concentrations, the sliced fracture fractal dimension increased from 1.01 to 1.25, indicating enhanced fracture disorder. The evolution of the spatial and energy fractal dimensions of the AE, analyzed using the box-counting method and correlation dimension, showed that in strong acidic and alkaline environments, the spatial fractal dimension before the peak failure was higher, pre-disposing the formation of highly disordered and smaller-scale fractures. During the fracture development stage, the time-series energy correlation dimension of the acid–base coal samples experienced two significant fluctuations, with a rapid decrease before the peak, indicating a transition from disorderly expansion to orderly fracturing. In the final stage of fracturing, the strongly acidic samples predominantly developed microcracks. The stronger the acidity or alkalinity, the more likely it is that high-energy (greater than 104 aJ) AE shear events will occur, with the mixed mode of tensile and shear failure tending to concentrate in the areas of high shear strain, thereby enhancing the macroscopic fragmentation orderliness. This research is crucial in regard to the safety assessment of dam bodies or coal pillars in acidic and alkaline coal mines in Northwestern China, particularly for predicting their stability. [ABSTRACT FROM AUTHOR]

Benggang is a type of erosion landform that commonly occurs in the southern regions of China, posing significant threats to local farmland and human safety. Object-based classification (OBC) can be applied with high-resolution (HR) remote sensing images for detecting Benggang areas on a large spatial scale, offering essential data for aiding in the remediation efforts for these areas. Nevertheless, traditional image segmentation methods may face challenges in accurately delineating Benggang areas. Consequently, the extraction of spatial and textural features from these areas can be susceptible to inaccuracies, potentially compromising the detection accuracy of Benggang areas. To address this issue, this study proposed a novel approach that integrates Segment Anything Model (SAM) and OBC for Benggang detection. The SAM was used to segment HR remote sensing imagery to delineate the boundaries of Benggang areas. After that, the OBC was employed to identify Benggang areas based on spectral, geometrical, and textural features. In comparison to traditional pixel-based classification using the random forest classifier (RFC-PBC) and OBC based on the multi-resolution segmentation (MRS-OBC), the proposed SAM-OBC exhibited superior performance, achieving a detection accuracy of 85.46%, a false alarm rate of 2.19%, and an overall accuracy of 96.48%. The feature importance analysis conducted with random forests highlighted the GLDV Entropy, GLDV Angular Second Moment (ASM), and GLCM ASM as the most pivotal features for the identification of Benggang areas. Due to its inability to extract and utilize these textural features, the PBC yielded suboptimal results compared to both the SAM-OBC and MRS-OBC. In contrast to the MRS, the SAM demonstrated superior capabilities in the precise delineation of Benggang areas, ensuring the extraction of accurate textural and spatial features. As a result, the SAM-OBC significantly enhanced detection accuracy by 34.12% and reduced the false alarm rate by 2.06% compared to the MRS-OBC. The results indicate that the SAM-OBC performs well in Benggang detection, holding significant implications for the monitoring and remediation of Benggang areas. [ABSTRACT FROM AUTHOR]

Background: Breast fibroadenoma poses a significant health concern, particularly for young women. Computer-aided diagnosis has emerged as an effective and efficient method for the early and accurate detection of various solid tumors. Automatic segmentation of the breast fibroadenoma is important and potentially reduces unnecessary biopsies, but challenging due to the low image quality and presence of various artifacts in sonography. Methods: Human learning involves modularizing complete information and then integrating it through dense contextual connections in an intuitive and efficient way. Here, a human learning paradigm was introduced to guide the neural network by using two consecutive phases: the feature fragmentation stage and the information aggregation stage. To optimize this paradigm, three fragmentation attention mechanisms and information aggregation mechanisms were adapted according to the characteristics of sonography. The evaluation was conducted using a local dataset comprising 600 breast ultrasound images from 30 patients at Suining Central Hospital in China. Additionally, a public dataset consisting of 246 breast ultrasound images from Dataset_BUSI and DatasetB was used to further validate the robustness of the proposed network. Segmentation performance and inference speed were assessed by Dice similarity coefficient (DSC), Hausdorff distance (HD), and training time and then compared with those of the baseline model (TransUNet) and other state-of-the-art methods. Results: Most models guided by the human learning paradigm demonstrated improved segmentation on the local dataset with the best one (incorporating C3ECA and LogSparse Attention modules) outperforming the baseline model by 0.76% in DSC and 3.14 mm in HD and reducing the training time by 31.25%. Its robustness and efficiency on the public dataset are also confirmed, surpassing TransUNet by 0.42% in DSC and 5.13 mm in HD. Conclusions: Our proposed human learning paradigm has demonstrated the superiority and efficiency of ultrasound breast fibroadenoma segmentation across both public and local datasets. This intuitive and efficient learning paradigm as the core of neural networks holds immense potential in medical image processing. [ABSTRACT FROM AUTHOR]

Change detection in high resolution (HR) remote sensing images faces more challenges than in low resolution images because of the variations of land features, which prompts this research on faster and more accurate change detection methods. We propose a pixel-level semantic change detection method to solve the fine-grained semantic change detection for HR remote sensing image pairs, which takes one lightweight semantic segmentation network (LightNet), using the parameter-sharing SiameseNet, as the architecture to carry out pixel-level semantic segmentations for the dual-temporal image pairs and achieve pixel-level change detection based directly on semantic comparison. LightNet consists of four long–short branches, each including lightweight dilated residual blocks and an information enhancement module. The feature information is transmitted, fused, and enhanced among the four branches, where two large-scale feature maps are fused and then enhanced via the channel information enhancement module. The two small-scale feature maps are fused and then enhanced via a spatial information enhancement module, and the four upsampling feature maps are finally concatenated to form the input of the Softmax. We used high resolution remote sensing images of Lake Erhai in Yunnan Province in China, collected by GF-2, to make one dataset with a fine-grained semantic label and a dual-temporal image-pair label to train our model, and the experiments demonstrate the superiority of our method and the accuracy of LightNet; the pixel-level semantic change detection methods are up to 89% and 86%, respectively. [ABSTRACT FROM AUTHOR]

Weather forecasting requires a comprehensive analysis of various types of meteorology data, and with the wide application of deep learning in various fields, deep learning has proved to have powerful feature extraction capabilities. In this paper, from the viewpoint of an image semantic segmentation problem, a deep learning framework based on semantic segmentation is proposed to nowcast Cloud-to-Ground and Intra-Cloud lightning simultaneously within an hour. First, a dataset with spatiotemporal features is constructed using radar echo reflectivity data and lightning observation data. More specifically, each sample in the dataset consists of the past half hour of observations. Then, a Light3DUnet is presented based on 3D U-Net. The three-dimensional structured network can extract spatiotemporal features, and the encoder–decoder structure and the skip connection can handle small targets and recover more details. Due to the sparsity of lightning observations, a weighted cross-loss function was used to evaluate network performance. Finally, Light3DUnet was trained using the dataset to predict Cloud-to-Ground and Intra-Cloud lightning in the next hour. We evaluated the prediction performance of the network using a real-world dataset from middle China. The results show that Light3DUnet has a good ability to nowcast IC and CG lightning. Meanwhile, due to the spatial position coupling of IC and CG on a two-dimensional plane, predictions from summing the probabilistic prediction matrices will be augmented to obtain accurate prediction results for total flashes. [ABSTRACT FROM AUTHOR]

Forest floor dead fuel moisture content (DFMC) is an important factor in the occurrence of forest fires, and predicting DFMC is important for accurate fire risk forecasting. Large areas of forest surface DFMC are difficult to predict via manual methods. In this paper, we propose an unmanned aerial vehicle (UAV)-based forest surface DFMC prediction method, in which a UAV is equipped with a multispectral camera to collect multispectral images of dead combustible material on the forest surface over a large area, combined with a deep-learning algorithm to achieve the large-scale prediction of DFMC on the forest surface. From 9 March to 23 March 2023, 5945 multispectral images and 480 sets of dead combustible samples were collected from an urban forestry demonstration site in Harbin, China, using an M300 RTK UAV with an MS600Pro multispectral camera. The multispectral images were segmented by a K-means clustering algorithm to obtain multispectral images containing only dead combustibles on the ground surface. The segmented multispectral images were then trained with the actual moisture content measured by the weighing method through the ConvNeXt deep-learning model, with 3985 images as the training set, 504 images as the validation set, and 498 images as the test set. The results showed that the MAE and RMSE of the test set are 1.54% and 5.45%, respectively, and the accuracy is 92.26% with high precision, achieving the accurate prediction of DFMC over a large range. The proposed new method for predicting DFMC via UAV multispectral cameras is expected to solve the real-time large-range accurate prediction of the moisture content of dead combustible material on the forest surface during the spring fire-prevention period in northeast China, thus providing technical support for improving the accuracy of forest fire risk-level forecasting and forest fire spread trend prediction. [ABSTRACT FROM AUTHOR]

Maize is one of the most important food crops in the field of agricultural development and food security in China. Among them, maize fruit and ear can directly determine the yield and quality of maize. Their phenotypic parameters (such as size and shape) can also be crucial indicators for the growth state of the plant. Fortunately, machine vision can be expected to serve as the maize phenotypic parameter acquisition and trait analysis, due to its objectivity, accuracy and speed, particularly with the application of artificial intelligence technology in agricultural production. Field inspection robots can be utilized to monitor the maize growth status in large-scale planting mode during this stage. This study aims to realize the high-throughput and automated acquisition of maize phenotypic parameters by unmanned inspection robots. A high-precision-speed balanced maize ear segmentation model, SwinT-YOLACT was proposed using YOLACT (you only look at coefficients) algorithm. Three optimization strategies were designed, according to the characteristics of the maize ear segmentation task. Firstly, SwinTransformer was used as the backbone feature extraction network of the improved model, where the self-attention mechanism of Transformer structure was integrated to enhance the global feature extraction capability; Secondly, a 3-layer effective channel attention mechanism was introduced before the feature pyramid network to eliminate the redundant feature information, in order to enhance the fusion of key features for the high accuracy of the improved model; Finally, the Mish activation function with better smoothing was used to replace the original Relu activation function, in order to further improve the segmentation accuracy at the original inference speed. In addition, the maize plant data was collected with the different environmental backgrounds and various maturity stages of maize fruit and maize ears in the field. Labelme annotation software was then adopted to manually label the data, according to the COCO dataset format. The number of samples was also expanded using data augmentation. A segmentation dataset of the maize fruit and ear was constructed for the model training on a deep learning network. The self-built segmentation dataset of maize ear was used to train and test the improved model. The experimental results show that the mask mean average precision was improved by 2.11 percentage points after introducing Swin-Transformer as the backbone feature extraction network, compared with the original YOLACT model. There was no influence on the segmentation speed. On this basis, the mask mean average precision was improved by 0.65 percentage points after introducing efficient channel attention before the feature pyramid network. The inference speed of the model was basically unchanged. The original model Relu activation function was replaced by Mish activation function, according to the first two experiments. The mask mean average precision was improved by 0.75 percentage points than before the replacement, whereas, the model inference speed was improved by 2.74 frames per second. SwinT-YOLACT was also used to compare with the YOLACT, YOLACT++, YOLACT-Edge, and Mask R-CNN segmentation models, all of which used the same experimental environment and training strategy. The verification results show that the mask mean average precision of SwinT-YOLACT reached 79.43%, which was 3.51, 3.38, and 7.88 percentage points higher than those of the original YOLACT, YOLACT++, and YOLACT-Edge, respectively, while only slightly lower than that of the Mask R-CNN model. The better performance of the improved models was then achieved in the segmentation task. In terms of segmentation speed, the inference speed of SwinTYOLACT was 35.44 frames per second, which was much better than that of Mask R-CNN model at 6.80 frames per second, and also improved by 3.39 and 2.58 frames per second, compared with the YOLACT and YOLACT++, respectively. In summary, SwinT-YOLACT can be expected for better segmentation of the maize fruit and ear in the unmanned inspection robot vision system. The finding can provide technical support for maize growth status monitoring. [ABSTRACT FROM AUTHOR]

Automatic weed identification and detection are crucial for precision weeding operations. In recent years, deep learning (DL) has gained widespread attention for its potential in crop weed identification. This paper provides a review of the current research status and development trends of weed identification in crop fields based on DL. Through an analysis of relevant literature from both within and outside of China, the author summarizes the development history, research progress, and identification and detection methods of DL-based weed identification technology. Emphasis is placed on data sources and DL models applied to different technical tasks. Additionally, the paper discusses the challenges of time-consuming and laborious dataset preparation, poor generality, unbalanced data categories, and low accuracy of field identification in DL for weed identification. Corresponding solutions are proposed to provide a reference for future research directions in weed identification. [ABSTRACT FROM AUTHOR]

In recent years, rice seedling raising factories have gradually been promoted in China. The seedlings bred in the factory need to be selected manually and then transplanted to the field. Growth-related traits such as height and biomass are important indicators for quantifying the growth of rice seedlings. Nowadays, the development of image-based plant phenotyping has received increasing attention, however, there is still room for improvement in plant phenotyping methods to meet the demand for rapid, robust and low-cost extraction of phenotypic measurements from images in environmentally-controlled plant factories. In this study, a method based on convolutional neural networks (CNNs) and digital images was applied to estimate the growth of rice seedlings in a controlled environment. Specifically, an end-to-end framework consisting of hybrid CNNs took color images, scaling factor and image acquisition distance as input and directly predicted the shoot height (SH) and shoot fresh weight (SFW) after image segmentation. The results on the rice seedlings dataset collected by different optical sensors demonstrated that the proposed model outperformed compared random forest (RF) and regression CNN models (RCNN). The model achieved R² values of 0.980 and 0.717, and normalized root mean square error (NRMSE) values of 2.64% and 17.23%, respectively. The hybrid CNNs method can learn the relationship between digital images and seedling growth traits, promising to provide a convenient and flexible estimation tool for the nondestructive monitoring of seedling growth in controlled environments. [ABSTRACT FROM AUTHOR]

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Recognizing and classifying natural or artificial geo-objects under complex geo-scenes using remotely sensed data remains a significant challenge due to the heterogeneity in their spatial distribution and sampling bias. In this study, we propose a deep learning method of surface complexity analysis based on multiscale entropy. This method can be used to reduce sampling bias and preserve entropy-based invariance in learning for the semantic segmentation of land use and land cover (LULC) images. Our quantitative models effectively identified and extracted local surface complexity scores, demonstrating their broad applicability. We tested our method using the Gaofen-2 image dataset in mainland China and accurately estimated multiscale complexity. A downstream evaluation revealed that our approach achieved similar or better performance compared to several representative state-of-the-art deep learning methods. This highlights the innovative and significant contribution of our entropy-based complexity analysis and its applicability in improving LULC semantic segmentations through optimal stratified sampling and constrained optimization, which can also potentially be used to enhance semantic segmentation under complex geo-scenes using other machine learning methods. [ABSTRACT FROM AUTHOR]

Potato is one of the most typical shallow root crops. The root distribution characteristics can then dominate the effective water and nutrient management. The accurate segmentation of root system can be the essential prerequisite for the key parameters of root system structure. Taking the potato root images as the research object, this study aims to achieve the non-contact, low-cost, fast, and accurate segmentation of potato root images. A potato root image segmentation was also proposed to monitor the growth state of potato using the improved DeepLabv3+ semantic segmentation network. The root length of the output image was then calculated. The spatial and temporal dynamic distribution characteristics of potato roots were calculated in the northern foothills of Yinshan Mountain in Inner Mongolia, China. The test results show that the training time of the improved MobileNetv2 was only 10.05h, which was 2.27 and 4.1h less than ResNet50, and Xception, respectively. In terms of the image segmentation performance, the MIoU of MobileNetv2 reached 92.26%, which was 1.84 and 2.68 percentage points higher than that of ResNet50 and Xception, respectively. The MPA reached 94.15%, which was 1.78 and 2.69 percentage points higher than that of the ResNet50 and Xception, respectively. The MIoU and MPA of DeepLabv3+ increased by 1.43 and 1.62 percentage points after the introduction of the improved MobileNetv2 backbone network, according to the standard DeepLabv3+. The MIoU and MPA increased by 1.61 and 1.80 percentage points after the introduction of CARAFE upsampling, respectively. The MIoU and MPA were improved by 2.92 and 2.68 percentage points, respectively, after the introduction of the CBAM attention mechanism. Furthermore, the CARAFE upsampling and CBAM attention mechanisms were introduced to improve the MobileNetv2 backbone network for the combination of different modules. After that, the MIoU and MPA increased by 2.30 and 2.61 percentage points, and 3.02 and 2.82 percentage points, respectively. The MIoU and MPA of the CARAFE upsampling increased by 2.98 and 2.23 percentage points, respectively, after combining with the CBAM attention mechanism. The best three improvement strategies were selected to increase the MIoU and MPA by 4.18 and 4.28 percentage points, respectively. The MIoU and MPA of the improved DeepLabv3+ model were 94.05% and 95.72%, respectively. The MIoU increased by 6.67, 4.92, 8.80 and 4.21 percentage points, respectively, compared with the SegNet, PSPNet, U-Net and standard DeepLabv3+, and the MPA increased by 6.7, 4.86, 8.25, and 4.53 percentage points, respectively. The training time was 9.52h, which was shortened by 6.8, 3.99, 4.56, and 3.94h, respectively, compared with the SegNet, PSPNet, U-Net, and standard DeepLabv3+. The FLOPs were reduced by 45×109, 34×109, 29×109, and 18×109, respectively, compared with the SegNet, PSPNet, U-Net and standard DeepLabv3+. The frame rate of image detection increased by 15.3, 11.7, 11.4, and 9fps, respectively. The coefficient of determination reached 0.981 in the regression analysis with the manually measured root length. The 80% of potato roots were distributed in the soil layers of 0-20, 0-30, 0-40, and 0-30cm, respectively, during the seedling, tuber formation, tuber bulking, and starch accumulation stage. The finding can provide a theoretical basis for the high-yield and high-efficiency cultivation techniques of potato in the northern Yinshan Mountain in Inner Mongolia of China. [ABSTRACT FROM AUTHOR]

Semantic image segmentation in computer networks is designed to determine the category to which each pixel in an image belongs. It is a basic computer vision task and has a very wide range of applications in practice. In recent years, semantic image segmentation algorithms in computer networks based on deep learning have attracted widespread attention due to their fast speed and high accuracy. However, due to the large number of downsampling layers in a deep learning model, the segmentation results are usually poor at the edge of an object, and there is currently no universal quantitative evaluation index to measure the performance of segmentation at the edge of an object. Solving these two problems is of great significance to semantic image segmentation algorithms in China. Based on traditional evaluation indicators, this paper proposes a region-based evaluation index to quantitatively measure the performance of segmentation at the edge of an object and proposes an improved loss function to improve model performance. The existing semantic image segmentation methods are summarized. This paper proposes regional-based evaluation indicators. Taking advantage of the particularity of semantic image segmentation tasks, this paper presents an efficient and accurate method for extracting the edges of objects. By defining the distance from pixels to the edges of objects, this paper proposes a fast algorithm for calculating the edge area. Based on this, three methods are proposed as well as an area-based evaluation indicator. The experimental results show that the accuracy of the loss function proposed in this paper, compared with that of the current mainstream cross-entropy loss function, is improved by 1% on the DeepLab model. For area-based evaluation indicators, a 4% accuracy improvement can be achieved, and on other segmentation models, there is also a significant improvement. [ABSTRACT FROM AUTHOR]

Oases are supporters of biological, social, and economic activities in arid regions, which guides their prosperity or decay. Given that China has the largest oases area in the world, determining their evolution is of substantial importance for sustainable development and the ecological protection of these systems. Accordingly, the spatiotemporal evolution of China's oases between 2000 and 2020 was analyzed. This was based on the normalized difference vegetation index derived from moderate imaging spectroradiometer data, combined with superpixel image segmentation, the region adjacency graph merging method, and the genetic algorithm. The results were as follows. Between 2000 and 2020: (1) the oases in China expanded significantly from 207,812 to 289,751 km2; with a total increase of 39.43%; and the number of oases decreased from 135 to 93, which mainly occurred in micro and small oases; (2) the areas of oases in stable, expanding, and shrinking states accounted for 96.77%, 42.65%, and respectively 3.23% of the total oasis area in 2000. Oases have become more concentrated and continuously expanded; (3) the increase in glacial meltwater and precipitation caused by rising temperatures increased the availability of water resources in oases, which provides the substantial basis for expansion; and (4) the primary anthropogenic drivers of oasis expansion and stabilization regions were cultivation and construction, respectively. [ABSTRACT FROM AUTHOR]

This paper proposes a body size measurement system based on deep learning for image segmentation by binocular stereovision for clothing design. A pair of binocular stereo cameras and a precise revolving platform are used to collect the stereo images of the entire human body. A more diverse human body dataset is constructed for deep learning. An optimized girth semantic segmentation model based on PSPNet network is well trained by deep learning to realize the automatic segmentation of more girth regions in human body images taken from multiple angles. Color subspaces with larger spacing in color spaces are designed to perform more effective stereo matching. The space coordinates of 3D point corresponding to each stereo matching point pair are calculated in the same coordinate system. The space coordinates are reversely revolved back to the intial coordinates of the same coordinate system according to the revolving angle of the platform. Girths of bust, waist, hip and thigh are measured by calculating the fitting curve length after girth fitting of the markers thereon. Height of bust, waist, hip, thigh and entire body are estimated according to the ratio of the 3D distance to the number of pixels between two adjacent markers on the same girth. Quantitative experiments on many human subjects demonstrate that the proposed system can measure body size intelligently and accurately for clothing design under the regulation of China national standards GB/T 2664-2017, GA258-2009, GB/T 2665-2017 and textile industry standard FZ/T 73029-2019, FZ/T 73017-2014, FZ/ T 73059-2017, FZ/T 73022-2019. [ABSTRACT FROM AUTHOR]

BACKGROUND: The incidence of liver tumors is among the top three in China. The treatments of benign and malignant tumors are different. Accurate diagnosis plays an important role in guiding the treatment of tumors. OBJECTIVE: The aim of this study is to solve the following: (1) blurred boundary between the liver tumor and other organs causes incorrect segmentation of liver tumor boundaries; (2) large difference in tumor size and the diversity in texture and grayscale are major challenges in liver tumor classification tasks. METHODS: Firstly, the liver tumor is segmented from the original CT images by a tumor segmentation network, UNet++ with fusion loss and atrous spatial pyramid pooling (FLAS-UNet++). The proposed segmentation method can solve the problem of tumor edge segmentation error by learning the tumor edge information. Secondly they are adaptively cropped according to the tumor volume to reduce the over-fitting and over-sensitivity of the deep network. Thirdly an improved Dense Block is designed to pay more attention to the changes in grayscale and texture between benign and malignant tumors. Finally, the features extracted from the network combined with tumor volume, patient's sex and age, are sent to a classifier for diagnosis. RESULT: Liver tumor segmentation results show that the dice, HD95 reached 71.9%, 12.1 mm, respectively. The classification results show that the accuracy, specificity, sensitivity and area under curve reached 82.4%, 79.8%, 84.4%, 87.5%, respectively. The segmentation and classification results are both better than other's methods and mainstream networks. CONCLUSIONS: In order to solve existing problems of liver tumor CT image classification methods, our method realizes the accurate segmentation and classification of liver tumors in CT images and has important clinical application value. [ABSTRACT FROM AUTHOR]

Nowadays, the most common primary bone tumor is osteosarcoma, which mostly occurs in teenagers. A common diagnosis method is currently that doctors manually diagnose osteosarcoma in magnetic resonance imaging (MRI) images because it is nonradioactive and has no biological damage to brain tissue and more obvious performance in soft tissue components such as tumors, blood vessels, and muscles in MRI images. However, this method is labor‐intensive and time‐consuming work, and cannot guarantee the accuracy of the diagnostic results. Existing osteosarcoma MRI image segmentation methods either aim to model the global context to improve the inner consistency of objects, or multiscale feature fusion to refine the detail of objects along their boundaries, which all ignore the interaction between the body of the object and the object boundary. Therefore, this paper proposes a novel segmentation method for osteosarcoma MRI images based on DecoupleSegNet, which explores the relationship between body feature and edge feature. It can assist doctors in diagnosing osteosarcoma and improve their work efficiency. First, we warp the feature of MRI images through learning a flow field so we can make the object more consistent. We then make further work to optimize the resulting body feature and residual edge feature through explicitly sampling pixels from different parts under decoupled supervision. Through these steps, we finally obtain the final feature map with fine boundaries from the MRI image of osteosarcoma. We take a test by using more than 80,000 osteosarcoma MRI images obtained from three hospitals in China. We find that compared with existing osteosarcoma MRI image segmentation methods, our proposed method achieves 90.51 Intersection of Union % with few parameters on the test, outperforming other models. In the test, we prove that our proposed method has better accuracy and lower resource consumption. [ABSTRACT FROM AUTHOR]

Osteosarcoma is a malignant bone tumor commonly found in adolescents or children, with high incidence and poor prognosis. Magnetic resonance imaging (MRI), which is the more common diagnostic method for osteosarcoma, has a very large number of output images with sparse valid data and may not be easily observed due to brightness and contrast problems, which in turn makes manual diagnosis of osteosarcoma MRI images difficult and increases the rate of misdiagnosis. Current image segmentation models for osteosarcoma mostly focus on convolution, whose segmentation performance is limited due to the neglect of global features. In this paper, we propose an intelligent assisted diagnosis system for osteosarcoma, which can reduce the burden of doctors in diagnosing osteosarcoma from three aspects. First, we construct a classification-image enhancement module consisting of resnet18 and DeepUPE to remove redundant images and improve image clarity, which can facilitate doctors' observation. Then, we experimentally compare the performance of serial, parallel, and hybrid fusion transformer and convolution, and propose a Double U-shaped visual transformer with convolution (DUconViT) for automatic segmentation of osteosarcoma to assist doctors' diagnosis. This experiment utilizes more than 80,000 osteosarcoma MRI images from three hospitals in China. The results show that DUconViT can better segment osteosarcoma with DSC 2.6% and 1.8% higher than Unet and Unet++, respectively. Finally, we propose the pixel point quantification method to calculate the area of osteosarcoma, which provides more reference basis for doctors' diagnosis. [ABSTRACT FROM AUTHOR]

Deep learning-based semantic segmentation technology is widely applied in remote sensing and has achieved excellent performance in remote sensing image target extraction. Greenhouses play an important role in the development of agriculture in China. However, the rapid expansion of greenhouses has had a series of impacts on the environment. Therefore, the extraction of large-scale greenhouses is crucial for the sustainable development of agriculture and environmental governance. It is difficult for existing methods to acquire precise boundaries. Therefore, we propose a spatial convolutional long short-term memory structure, which can fully consider the spatial continuity of ground objects. We use multitask learning to improve the network's ability to extract image boundaries and promote convergence through auxiliary loss. We propose a superpixel optimization module to optimize the main-branch results of network semantic segmentation using more precise boundaries obtained by advanced superpixel segmentation techniques. Compared with other mainstream methods, our proposed structure can better consider spatial information and obtain more accurate results. We chose Shandong Province, China, as the study area and used Gaofen-1 satellite remote sensing images to create a new greenhouse dataset. Our method achieved an F1 score of 77%, a significant improvement over mainstream semantic segmentation networks, and it could extract greenhouse results with more precise boundaries. We also completed large-scale greenhouse mapping for Shandong Province, and the results show that our proposed modules have great potential in greenhouse extraction. [ABSTRACT FROM AUTHOR]

Cyanobacterial harmful algal blooms (CyanoHABs) in inland water have emerged as a major global environmental challenge. Although satellite remote sensing technology has been widely used to monitor CyanoHABs, there are also some automatic extraction methods of CyanoHABs based on spectral indices (such as gradient mode, fixed threshold, and the Otsu method, etc.), the accuracy is generally not very high. This study developed a high-precision automatic extraction model for CyanoHABs using a deep learning (DL) network based on Sentinel-2 multi-spectral instrument (MSI) data of Chaohu Lake, China. First, we generated the CyanoHABs "ground truth" dataset based on visual interpretation. Thereafter, we trained the CyanoHABs extraction model based on a DL image segmentation network (U-Net) and extracted CyanoHABs. Then, we compared three previous automatic CyanoHABs extraction methods based on spectral index threshold segmentation and evaluated the accuracy of the results. Based on "ground truth", at the pixel level, the F1 score and relative error (RE) of the DL model extraction results are 0.90 and 3%, respectively, which are better than that of the gradient mode (0.81,40%), the fixed threshold (0.81, 31%), and the Otsu method (0.53, 62%); at CyanoHABs area level, the R2 of the scatter fitting between DL model result and the "ground truth" is 0.99, which is also higher than the other three methods (0.90, 0.92, 0.84, respectively). Finally, we produced the annual CyanoHABs frequency map based on DL model results. The frequency map showed that the CyanoHABs on the northwest bank are significantly higher than in the center and east of Chaohu Lake, and the most serious CyanoHABs occurred in 2018 and 2019. Furthermore, CyanoHAB extraction based on this model did not cause cloud misjudgment and exhibited good promotion ability in Taihu Lake, China. Hence, our findings indicate the high potential of the CyanoHABs extraction model based on DL in further high-precision and automatic extraction of CyanoHABs from large-scale water bodies. [ABSTRACT FROM AUTHOR]

Osteosarcoma is the most common malignant osteosarcoma, and most developing countries face great challenges in the diagnosis due to the lack of medical resources. Magnetic resonance imaging (MRI) has always been an important tool for the detection of osteosarcoma, but it is a time-consuming and labor-intensive task for doctors to manually identify MRI images. It is highly subjective and prone to misdiagnosis. Existing computer-aided diagnosis methods of osteosarcoma MRI images focus only on accuracy, ignoring the lack of computing resources in developing countries. In addition, the large amount of redundant and noisy data generated during imaging should also be considered. To alleviate the inefficiency of osteosarcoma diagnosis faced by developing countries, this paper proposed an artificial intelligence multiprocessing scheme for pre-screening, noise reduction, and segmentation of osteosarcoma MRI images. For pre-screening, we propose the Slide Block Filter to remove useless images. Next, we introduced a fast non-local means algorithm using integral images to denoise noisy images. We then segmented the filtered and denoised MRI images using a U-shaped network (ETUNet) embedded with a transformer layer, which enhances the functionality and robustness of the traditional U-shaped architecture. Finally, we further optimized the segmented tumor boundaries using conditional random fields. This paper conducted experiments on more than 70,000 MRI images of osteosarcoma from three hospitals in China. The experimental results show that our proposed methods have good results and better performance in pre-screening, noise reduction, and segmentation. [ABSTRACT FROM AUTHOR]

Apple quality has been ever increasingly required with the improvement of living standards in recent years. The core ratio is one of the most significant factors to determine the quality of apples. But, the manual measurement on the fruit core cannot fully meet the current detection requirements, in terms of cost and accuracy at present. In this study, an automatic segmentation was proposed for the fruit core using a TMU-Net network model. Firstly, three common types of apples were selected in the Xinjiang of China. An acquisition device was then used to capture the 311 cross-sectional images of the fruit core. Secondly, the preprocessing operations were also conducted to enhance the original images, including translation, vertical mirroring, horizontal mirroring, and adding Gaussian noise. Better training was achieved in the expanded dataset, compared with the original. Specifically, the Intersection Over Union (IOU), Precision, Recall, and F1-score of the TMU-Net network increased by 27.28, 36.62, 29.81, and 32.06 percentage points, respectively. It infers that the data enhancement improved the robustness and generalization of the model after training. The Multiple Residual Dilated Convolution (MRDC) module was also constructed with the Cavity convolution in the different void ratios and shortcut connections. Shortcut connections are skipping one layers, they simply perform identity mapping. As such, the information loss was reduced in the jump connection part of the model. There was also less semantic difference between the encoder and the decoder. The MRDC module was finally used to verify the TMU-Net jump connection. The results showed that: 1) The MRDC module was introduced to effectively improve the segmentation performance of the model, in which the IOU, Precision, and F1-score were improved by 1.59, 6.49, and 4.65 percentage points, respectively. 2) The first 13 layers of VGG-16 network were used as the backbone to capture the low-level features. The Transformer encoder was integrated into the network structure to enhance the global extraction of the network, particularly for the locality of convolution operations. The segmentation shows that the TMU-Net network was much more precise to process the sharp corner and edge details of the fruit center, indicating the feasibility of the model in the segmentation task of the fruit center. 3) The TMU-Net model was trained under a variety of transfer learning. Therefore, freezing the training of specific network layers can be expected to effectively improve the indicators of the model. The training curve of the model showed that the training was used to accelerate the convergence speed. Subsequently, the TMU-Net, DeeplabV3+, U-Net, and PSPNet models were trained to verify the test set under the same experimental parameters. The IOU, Precision, Recall, and F1-score of the TMU-Net model increased by 3.96, 7.15, 9.49, and 6.30 percentage points, respectively, compared with the DeeplabV3+ model with better effect. Therefore, this TMU-Net model can be expected to accurately and effectively realize the fruit core segmentation. The finding can also provide a strong reference for the intelligent detection of apple quality. [ABSTRACT FROM AUTHOR]

Among primary bone cancers, osteosarcoma is the most common, peaking between the ages of a child's rapid bone growth and adolescence. The diagnosis of osteosarcoma requires observing the radiological appearance of the infected bones. A common approach is MRI, but the manual diagnosis of MRI images is prone to observer bias and inaccuracy and is rather time consuming. The MRI images of osteosarcoma contain semantic messages in several different resolutions, which are often ignored by current segmentation techniques, leading to low generalizability and accuracy. In the meantime, the boundaries between osteosarcoma and bones or other tissues are sometimes too ambiguous to separate, making it a challenging job for inexperienced doctors to draw a line between them. In this paper, we propose using a multiscale residual fusion network to handle the MRI images. We placed a novel subnetwork after the encoders to exchange information between the feature maps of different resolutions, to fuse the information they contain. The outputs are then directed to both the decoders and a shape flow block, used for improving the spatial accuracy of the segmentation map. We tested over 80,000 osteosarcoma MRI images from the PET-CT center of a well-known hospital in China. Our approach can significantly improve the effectiveness of the semantic segmentation of osteosarcoma images. Our method has higher F1, DSC, and IOU compared with other models while maintaining the number of parameters and FLOPS. [ABSTRACT FROM AUTHOR]

Retinal vasculature provides an opportunity for direct observation of vessel morphology, which is linked to multiple clinical conditions. However, objective and quantitative interpretation of the retinal vasculature relies on precise vessel segmentation, which is time consuming and labor intensive. Artificial intelligence (AI) has demonstrated great promise in retinal vessel segmentation. The development and evaluation of AI-based models require large numbers of annotated retinal images. However, the public datasets that are usable for this task are scarce. In this paper, we collected a color fundus image vessel segmentation (FIVES) dataset. The FIVES dataset consists of 800 high-resolution multi-disease color fundus photographs with pixelwise manual annotation. The annotation process was standardized through crowdsourcing among medical experts. The quality of each image was also evaluated. To the best of our knowledge, this is the largest retinal vessel segmentation dataset for which we believe this work will be beneficial to the further development of retinal vessel segmentation. Measurement(s) retinal image segmentation Technology Type(s) manual segmentation Sample Characteristic - Organism Homo sapiens Sample Characteristic - Environment hospital Sample Characteristic - Location Zhejiang Province, China [ABSTRACT FROM AUTHOR]

Semantic segmentation of the remote sensing images (RSIs) has attracted increasing interest in recent years. However, large-area segmentation of the woodland presents challenges. The wide distribution and diverse tree species of the woodland make feature extraction difficult. For this reason, an infrared attention network (InfAttNet) is proposed to extract woodland from multispectral RSIs. InfAttNet has an extra infrared spectral encoder which makes use of the sensitivity of vegetation to near-infrared and red edge spectrums. This extra encoder applies learning about vegetation to improve woodland segmentation. Several attention blocks are designed to enhance learning about vegetation features and so improve the performance. In addition, a new dataset is built, containing a large number of woodland RSIs and covering several typical woodland distribution regions in China. The experimental results demonstrate that compared with other networks, InfAttNet has the highest accuracy and is capable of rapid extraction of the woodland in RSIs. [ABSTRACT FROM AUTHOR]

Reliable cropland parcel data are vital for agricultural monitoring, yield estimation, and agricultural intensification assessments. However, the inherently high landscape fragmentation and irregularly shaped cropland associated with smallholder farming systems restrict the accuracy of cropland parcels extraction. In this study, we proposed an adaptive image segmentation method with the automated selection of optimal scale (MSAOS) to extract cropland parcels in heterogeneous agricultural landscapes. The MSAOS method includes three major components: (1) coarse segmentation to divide the whole images into homogenous and heterogeneous regions, (2) fine segmentation to determine the optimal segmentation scale based on average local variance function, and (3) region merging to merge and dissolve the over-segmented objects with small area. The potential cropland objects derived from MSAOS were combined with random forest to generate the final cropland parcels. The MSAOS method was evaluated over different agricultural regions in China, and derived results were assessed by benchmark cropland parcels interpreted from high-spatial resolution images. Results showed the texture features of Homogeneity and Entropy are the most important features for MSAOS to extract potential cropland parcels, with the highest separability index of 0.28 and 0.26, respectively. MSAOS-derived cropland parcels had high agreement with the reference dataset over eight tiles in Qichun county, with average F1 scores of 0.839 and 0.779 for the area-based classification evaluation (Fab) and object-based segmentation evaluation (Fob), respectively. The further evaluation of MSAOS on different tiles of four provinces exhibited the similar results (Fab = 0.857 and Fob = 0.775) with that on eight test tiles, suggesting the good transferability of the MSAOS over different agricultural regions. Furthermore, MSAOS outperformed other widely-used approaches in terms of the accuracy and integrity of the extracted cropland parcels. These results indicate the great potential of using MSAOS for image segmentation in conjunction with random forest classification to effectively extract cropland parcels in smallholder farming systems. [ABSTRACT FROM AUTHOR]

Slime mould algorithm (SMA) is extensively used in engineering applications such as the parameter estimation of photovoltaic models, image segmentation, economic emission dispatch, etc. To improve SMA's inherent search stagnation, slow convergence speed and poor transition ability when transferring from exploration to exploitation, especially for optimization problems with high dimensions and many local optimal values, a multi-strategy improved slime mould algorithm (MSMA) is proposed in this paper. The elite chaotic search strategy (ECSS) is adopted to improve the ability of the algorithm to explore near the elite individuals. Moreover, the z parameter of SMA, which originally is a constant, is substituted by a nonlinear convergence factor with chaotic disturbance to enhance the algorithm's transition ability between exploration and exploitation. MSMA is compared with other 9 classical or advanced metaheuristic algorithms by optimizing 12 benchmark functions, and the comparative results show that MSMA has better performance in solving accuracy, convergence speed and robustness. Finally, to verify the effectiveness of MSMA, MSMA and the other 9 algorithms are applied to a real cascade hydropower reservoirs system along Dadu River in China to maximize the annual power generation. The numerical simulation results show that with the proposed MSMA, the average power generation in the wet year, the normal year and the dry year are 1.11%–19.22%, 0.35%–13.21% and 0.84%–12.85% more than with other algorithms respectively, which verifies the superiority and effectiveness of MSMA for cascade reservoirs operation problem. [ABSTRACT FROM AUTHOR]

Objectives: [18F]9-fluoropropyl-(+)-dihydrotetrabenazine ([18F]-FP-DTBZ) positron emission tomography (PET) provides reliable information for the diagnosis of Parkinson's disease (PD). In this study, we proposed a multi-atlas-based [18F]-FP-DTBZ PET image segmentation method for PD quantification assessment. Methods: A total of 99 subjects from Xuanwu Hospital of Capital Medical University were included in this study, and both brain PET and magnetic resonance (MR) scans were conducted. Data from 20 subjects were used to generate atlases, based on which a multi-atlas-based [18F]-FP-DTBZ PET segmentation method was developed especially for striatum and its subregions. The proposed method was compared with the template-based method through striatal subregion parcellation performance and the standard uptake value ratio (SUVR) quantification accuracy. Discriminant analysis between healthy controls (HCs) and PD patients was further performed. Results: Segmentation results of the multi-atlas-based method showed better consistency than the template-based method with the ground truth, yielding a dice coefficient of 0.81 over 0.73 on the full striatum. The SUVRs calculated by the multi-atlas-based method had an average interclass correlation coefficient (ICC) of 0.953 with the standardized result, whereas the template-based method only reached 0.815. The SUVRs of HCs were generally higher than that of patients with PD and showed significant differences in all of the striatal subregions (all p < 0.001). The median and posterior putamen performed best in discriminating patients with PD from HCs. Conclusion: The proposed multi-atlas-based [18F]-FP-DTBZ PET image segmentation method achieved better performance than the template-based method, indicating great potential in improving accuracy and efficiency for PD diagnosis in clinical routine. [ABSTRACT FROM AUTHOR]

Detecting the terrain surface cracks caused by earthquakes, which are termed coseismic ruptures, has important significance for discovering concealed faults, monitoring their movements, and forecasting possible follow-on earthquakes. On May 22, 2021, Maduo County in Qinghai province, China, suffered an earthquake with a magnitude of 7.4, which created densely distributed cracks. In this study, we designed an automatic crack detection framework based on remote sensing technology. With the use of low-altitude unmanned aerial vehicles (UAVs), we obtained very high-resolution aerial images of the area affected by the earthquake, which were further processed by photogrammetric software to produce digital orthophoto maps (DOMs). We then designed a novel terrain surface crack detection neural network, which differs from the previous methods that focus on detecting cracks in man-made object surfaces such as flat roads. We investigated the spatial property of the sinuous linear cracks and handled this by introducing adaptive deformable convolutions with a context-channel-space boosted mechanism. The feature extraction stage, feature optimization stage, and upsampling stage were embedded with the deformable convolutions to form a compact and powerful crack detector, named Crack-CADNet [the Context-chAnnel-space boosted Deformable convolutional neural network (CNN) for crack detection]. The postprocessing included filtering out the nontectonic cracks, aided by annotations from experts, and grouping and vectorizing the generated binary segmentation map as crack polygons, which were evaluated at the instance level. In addition to the first in-depth investigation of detecting earthquake cracks with aerial remote sensing and a deep learning-based process, the crack detection network we propose outperformed the recent CNN-based methods designed for general semantic segmentation and crack detection. Source code and the Maduo earthquake crack dataset will be available at http://gpcv.whu.edu.cn/data/. [ABSTRACT FROM AUTHOR]

Level set method has been extensively used for image segmentation, which is a key technology of water extraction. However, one of the problems of the level-set method is how to find the appropriate initial surface parameters, which will affect the accuracy and speed of level set evolution. Recently, the semantic segmentation based on deep learning has opened the exciting research possibilities. In addition, the Convolutional Neural Network (CNN) has shown a strong feature representation capability. Therefore, in this paper, the CNN method is used to obtain the initial SAR image segmentation map to provide deep a priori information for the zero-level set curve, which only needs to describe the general outline of the water body, rather than the accurate edges. Compared with the traditional circular and rectangular zero-level set initialization method, this method can converge to the edge of the water body faster and more precisely; it will not fall into the local minimum value and be able to obtain accurate segmentation results. The effectiveness of the proposed method is demonstrated by the experimental results of flood disaster monitoring in South China in 2020. [ABSTRACT FROM AUTHOR]

• This study presents an extensive systematic literature review on WD recognition. • A set of 94 research articles published between 2005–2022 has been selected for analysis. • A framework has been proposed to classify the research articles into four categories. Due to wheat diseases (WD), the global rate of wheat production is decreasing by 3.6% annually. With the help of computer vision technology, WD recognition is not a challenging task but motivates the concepts of image processing, image segmentation, feature extraction, and AI-based recognition models. The objective of this study is to review and systematically analyze studies that have been published between 2005 and 2022. The authors make an effort to determine the important developments in image segmentation models, tools, datasets, and comparative analysis for the accuracy of the recognition model. The current study follows the standard systematic literature review (SLR) approach and selects 638 studies from five different web source databases. Among 638 studies, 544 studies were discarded in the study extraction process. A total number of 94 studies have been published in 45 reputed journals, and 49 conferences that have been identified with evaluation, validation, proposed, and philosophical criteria. After analysis, ten types of image segmentation models were identified. The most prominent clustering-based image segmentation technique (34.78%) is used for powdery mildew and stripe rust WD recognition. During WD recognition, the accuracy performance parameter is found to be most prominent. China and India are the two countries on the Asian continent that contribute to WD recognition. The current study summarizes the findings of WD research and highlights the need for standard datasets and accuracy. It highlights the importance of exploring and developing more precise and hybrid segmentation classification models for WD recognition. [ABSTRACT FROM AUTHOR]

A loess sinkhole is a typical geological hazard that is widely distributed in the Loess Plateau of China. However, some latent damages are difficult to detect in the natural hazard. Particularly, the serious damage caused by loess sinkholes is ever increasing, as the infrastructure rapidly developed in recent years. The resulting water and soil loss can aggravate and even trigger the occurrence, development of mudflows, collapses, and landslides. In addition, the sinkhole collapse can also seriously damage the farmland infrastructure, such as terraces, water storage facilities, and irrigation systems, as well as the infrastructures such as highways, railways, bridges, and oil and gas pipelines. Thus, it is a high demand to detect the loess sinkholes for the soil and water conservation and engineering construction in the loess regions. However, the traditional field surveys are costly and inefficient on the loess sinkholes. In this study, an automatic and object-oriented extraction of loess sinkholes was proposed to determine the influence of integrated terrain features on the extraction accuracy of the Convolutional Neural Networks (CNN) model. The study area was selected as Lanzhou City, Gansu Province in western China. The segmentation scale of the image was then determined by the Moran's I and Gray Level Co-occurrence Matrix entropy using WorldView 3 remote sensing image and ALOS digital elevation model data. An object-oriented extraction was implemented to obtain the spectrum (i.e., Mean_R, Mean_G, Mean_B, Std_R, Std_G, Std_B, Max_diff, and Brightness), shape (i.e., Area, Asymmetry, Border_index, Border_Length, Compactness, Density, Elliptic_Fit, Length/Width, Length, Shape_index, Roundness and Width), texture (i.e., GLCM_Homogeneity, GLCM_Contrast, GLCM_Mean, GLCM_Dissimilarity, GLCM_Entropy, and GLCM_Std), and terrain (i.e., Mean_DEM, Mean_Slope, Mean_Hillshade, Std_DEM, Std_Slope, and Std_Hillshade) features of the loess sinkholes. Two types of training samples were constructed with/without the terrain features, and then to train two CNN models for the extraction of the loess sinkholes in the same area. After that, the extraction accuracy of the model was evaluated, according to the precision rate, recall rate, and F1 score. A Support Vector Machine (SVM) model was also established to compare with the two CNN models. The research results show that the CNN model trained by integrating terrain features presented an accuracy rate of 94.62%, a recall rate of 86.27%, and an F1 score of 90.26%. Specifically, the false positive was significantly reduced, while the accuracy rate and F1 score increased by 18.10 percentage points, and 9.15 percentage points, respectively. The F1 scores of the two CNN models were both over 80%, which were 6.94 percentage points, and 16.09 percentage points higher than that of the SVM model, respectively. Consequently, the integrated terrain features to train the CNN model can effectively reduce the number of False Positive in the loess sinkholes, thus improving the comprehensive performance of the model. The extraction performances of the CNN models were all better than those of the SVM model. Therefore, the CNN model can be widely expected to integrate with the terrain features from the satellite data and object-oriented image analysis for the accurate and efficient extraction of the loess sinkholes. [ABSTRACT FROM AUTHOR]

In today's society, people's lives are increasingly inseparable from computer information. Due to the continuous improvement of technology and the rapid development of internet technology, the network environment is becoming more and more complex, which makes it easy to cause loopholes in the information retrieval system when people use the network. Therefore, it is especially important to search for legal knowledge by computer. In order to adapt to this change and demand, we need a retrieval system to provide the corresponding search function, legal information content, and management and other services, so as to achieve the purpose of computer legal information retrieval. The legal information retrieval system is computer based, draws conclusions from the analysis of relevant data, and then applies them to judicial trial cases, criminal investigations, and other fields to provide a reference for relevant legal issues. The system is designed to combine computer technology with a criminal investigation and other fields, and then analyze the data to draw the corresponding conclusions. The retrieval algorithms used are mainly image and content retrieval algorithms, and image retrieval algorithms mainly use image segmentation technology, while content retrieval algorithms mainly use the cuckoo algorithm. At present, the information construction and economic and social development in China have become one of the issues of common concern and need to be solved by all countries in the world. The study of the legal information retrieval system is of great importance in the construction of information technology and the development of economic society. [ABSTRACT FROM AUTHOR]

Landslides are catastrophic natural hazards that often lead to loss of life, property damage, and economic disruption. Image-based landslide investigations are crucial for determining landslide susceptibility and risk. In practice, satellite images have been widely utilized for such investigations; however, they still require significant labor and time resources. In this study, we propose an image-based two-phase data-driven framework for detecting and segmenting landslide regions using satellite images. In phase I, an object detection algorithm, Faster-RCNN, is trained to detect the landslide location within the large-scale satellite images. The bounding boxes of each landslide location are proposed and visualized. In phase II, we crop the satellite images into small images using the location information of the bounding boxes. Next, we use a boundary detection algorithm to identify the boundary information of each detected loess landslide to strengthen the segmentation performance. Finally, we improve the architecture of the segmentation U-Net by integrating additional inception blocks with dilation to enhance the landslide segmentation performance. A total of 150 local loess landslide occurrences in northern China are selected as our case study to validate the effectiveness, efficiency, and universality of the proposed two-phase framework. Segmentation of loess landslides is considered a challenging task due to the intrinsic nature of vague boundary information. The proposed framework is compared with the conventional U-Net and other recent benchmarking landslide segmentation algorithms. Computational results indicate that the proposed framework produces more accurate segmentation of loess landslides compared with the other tested benchmarking algorithms. [ABSTRACT FROM AUTHOR]

The mining industry production is an important pillar industry in China, while its extensive production activities have led to several ecological and environmental problems. Earth observation technology using high-resolution satellite imagery can help us efficiently obtain information on surface elements, surveying and monitoring various land occupation issues arising from open-pit mining production activities. Conventional pixel-based interpretation methods for high-resolution remote sensing images are restricted by "salt and pepper" noise caused by environmental factors, making it difficult to meet increasing requirements for monitoring accuracy. With the Jingxiang phosphorus mining area in Jingmen Hubei Province as the studied area, this paper uses a multi-scale segmentation algorithm to extract large-scale main characteristic information using a layered mask method based on the hierarchical structure of the image object. The remaining characteristic elements were classified and extracted in combination with the random forest model and characteristic factors to obtain land occupation information related mining industry production, which was compared with the results of the Classification and Regression Tree model. 23 characteristic factors in three aspects were selected, including spectral, geometric and texture characteristics. The methods employed in this study achieved 86% and 0.78 respectively in overall extraction accuracy analysis and the Kappa coefficient analysis, compared to 79% and 0.68 using the conventional method. [ABSTRACT FROM AUTHOR]

Osteosarcoma is the most common primary malignant bone tumor in children and adolescents. It has a high degree of malignancy and a poor prognosis in developing countries. The doctor manually explained that magnetic resonance imaging (MRI) suffers from subjectivity and fatigue limitations. In addition, the structure, shape, and position of osteosarcoma are complicated, and there is a lot of noise in MRI images. Directly inputting the original data set into the automatic segmentation system will bring noise and cause the model's segmentation accuracy to decrease. Therefore, this paper proposes an osteosarcoma MRI image segmentation system based on a deep convolution neural network, which solves the overfitting problem caused by noisy data and improves the generalization performance of the model. Firstly, we use Mean Teacher to optimize the data set. The noise data is put into the second round of training of the model to improve the robustness of the model. Then, we segment the image using a deep separable U-shaped network (SepUNet) and conditional random field (CRF). SepUnet can segment lesion regions of different sizes at multiple scales; CRF further optimizes the boundary. Finally, this article calculates the area of the tumor area, which provides a more intuitive reference for assisting doctors in diagnosis. More than 80000 MRI images of osteosarcoma from three hospitals in China were tested. The results show that the proposed method guarantees the balance of speed, accuracy, and cost under the premise of improving accuracy. [ABSTRACT FROM AUTHOR]

High-resolution land cover mapping over large areas is a challenging task due to the lack of high-quality labels. A potential solution is to leverage the existing knowledge contained in the freely available lower-resolution land cover products. However, the relatively low resolution and low accuracy of the products lead to numerous inaccurate labels, which harms the performance of the neural network. This article addresses the challenge by jointly optimizing the network parameters and correcting the noisy labels with a novel online noise correction approach and a synergistic noise correction loss. By incorporating the information entropy as a measurement to determine the probable correct labels, the proposed noise correction approach learns to make effective correction of the noisy labels during training and eventually boosts the performance with a training set containing less noisy labels. Experimental results show that the proposed method can effectively correct the noisy labels and reduce their negative impact on network training. By employing the proposed method, we produce a refined high-resolution (3-m) land cover map from a lower-resolution (10-m) product in China and improve the accuracy from 74.96% (10-m) to 81.32% (3-m). Such an approach that can effectively learn from noisy data sets leads to many potential opportunities for using and magnifying existing knowledge and results. [ABSTRACT FROM AUTHOR]

High-quality optical data is difficult to acquire in southwest China, due to the cloudy and rainy weather. The synthetic aperture radar (SAR) can work for all-time and all-weather, but the revisit period is usually long. Therefore, it is a high demand for the synergy of optical and radar data rather than a single remote sensing data source, particularly for the survey and assessment on the land resource, as well as the global change. This study performed an object-oriented guided segmentation on the difference images generated from 2016 and 2019 quad-pol Radarsat-2 data using the 2019 Sentinel-2 optical data of Meishan region. A Fractal Net Evolution Approach (FNEA) was first utilized to segment the 2016 optical image for an initial segmentation. Then, the second segmentation was implemented using the same approach, where the features of the SAR image were provided for the purpose of guided segmentation. As such, the regions of growth were merged from small to large, in order to eventually form a complete geographical object. A final segmentation was obtained to select the changed and unchanged samples. More importantly, an object contained both changed and unchanged pixels, particularly with a single type of pixel inside the samples. Then, the object-level multidimensional features were extracted using the samples. The initial radar parameters included the backward scattering coefficient, Pauli and Freeman-Durden polarization decomposition parameters. The segmentation was utilized to generate the geographic objects. Meanwhile, some parameters were calculated using the initial parameters, including the mean, and standard deviation, whereas, the texture feature parameters using the Gray-level co-occurrence matrix (GLCM) included the mean, standard deviation, homogeneity, contrast, entropy, and the correlation for a total of 72-dimensional features. The redundant feature dataset was filtered to select the extracted feature set. Consequently, an FSO-RF change detection framework was proposed to achieve the feature optimization and the acquisition of the final change detection. The inter-sample distance metric learning was used to optimize the separability of feature space. The optimal features were used to calculate the distance between the changed and unchanged samples in different features. Finally, the change detection task was implemented using an object-oriented random forest classifier. The existing change detection pixel-based methods were selected to verify the improved model, including the change vector analysis (CVA), principal component analysis (PCA), multivariate alteration detection (MAD), Iteration multivariate alteration detection (IR-MAD), support vector machines (SVM), and random forest (RF) change detection using radar difference image segmentation. The change detection framework showed a remarkable improvement, with an overall accuracy of 92.90% in the test area. Two representative areas were selected in the study area for sampling and validation. The experiments demonstrated that better performance was achieved than that of the traditional, although the accuracy indexes were degraded in some areas with the complex features. The overall accuracies of the two sample areas reached 95.08 % and 88.16%. The algorithm in this paper can well meet the needs of detecting changes in different feature categories such as towns and farmlands, and has certain application value in the monitoring of national land resources. [ABSTRACT FROM AUTHOR]

With the further development of the social economy, people pay more attention to spiritual and cultural needs. As the main place of people's daily life, the family is very important to the creation of its cultural atmosphere. In fact, China has fully entered the era of interior decoration, and people are paying more and more attention to decorative effects and the comfort and individual characteristics of decoration. Therefore, it is of practical significance to develop the application of decorative art in interior space design. However, the transfer effect of current interior decoration art design tends to be artistic, which leads to image distortion, and image content transfer errors are easy to occur in the process of transfer. The application of image style transfer in interior decoration art can effectively solve such problems. This paper analyzes the basic theory of image style transfer through image style transfer technology, Gram matrix, and Poisson image editing technology and designs images from several aspects such as image segmentation, content loss, enhanced style loss, and Poisson image editing constrained image spatial gradient. The application process of style transfer in interior decoration art realizes the application of image style transfer in interior decoration art. The experimental results show that the application of image style transmission in interior decoration art design can effectively avoid the contents of the interior decoration errors and distortions and has a good style transfer effect. [ABSTRACT FROM AUTHOR]

Accurate identification of maize stubble row has widely dominated the automatic row-followed seeding using machine vision. However, it is difficult to segment the images of stubble row in the maize stubble field harvested by combine harvesters, due mainly to the indistinct chromaticity difference with naked land surface and maize residues. In this study, image segmentation was presented using a support vector machine (SVM), in order to realize precise and rapid segmentation of the maize stubble row. Firstly, principal component analysis (PCA) was used for dimensionality reduction and feature optimization of the dataset, where the specific features were selected to distinguish standing stubble, naked land surface, and maize residues. Especially, the 1500 sample images of standing stubble, 1500 sample images of the naked land surface, and 1500 sample images of maize residues were collected, while, 2210 features containing 697 color features, and 1513 texture features were obtained using sample images. Then, PCA was used to choose 21 color features of the standing stubble, naked land surface, and maize residues in the R, G, B, L, a, and b components from the datasets. The selected color features were constructed into a 21-dimensional feature vector, which was used as the input of the standing stubble SVM recognition model. Secondly, the region of interest (ROI) was selected in the middle of the image with the integrated maize stubble row for the higher efficiency of image segmentation. Finally, the trained SVM recognition model was used for the slide detection of standing stubble within the ROI with a window of 25×25(pixel). If the currently detected window was standing stubble in slide detection, the grayscale value would be set to 255. The maize stubble row was segmented by a threshold when the slide detection was complete. The segmented binary image was then optimized using the morphological open operation processing with a disc-shaped structural element with a radius of 2 pixels. Furthermore, 100 test images were collected to verify the segmentation performance from the Scientific Observing and Experimental Station of Arable Land Conservation (North Hebei), Ministry of Agriculture in Zhuozhou City, China in October 2019. The capture size was divided into 4 classes, including 0, 1, 2, and 3 kg/m², according to the quality of maize residues between rows. At the same time, each class included the front lighting on a sunny day, direct sunlight, backlight on a sunny and cloudy day. Moreover, the images of the 0 kg/m² class also involved different shapes and surface moisture contents, due to the change of time and weather. The results revealed that the algorithm presented higher accuracy and robustness for the stubble row segmentation under various maize residues quality between rows and different lighting conditions. The average recognition accuracy of standing stubble was 93.8% in the SVM recognition model, whereas, those were 62.76% and 63.71% in the BPNN and ELM model, respectively. The average segmentation accuracy, average recall rate, and F1avr in the SVM recognition model were 93.72%，92.35% and 93.03%, respectively, whereas, those in the BPNN, ELM and Genetic models were 61.88%, 86.94%, 72.3%, 62.92%, 88.75%, 73.63%, 90.13%, 51.36% and 65.43%, respectively. Additionally, the average processing time was 0.06 s for a 640×480(pixel) image using the SVM recognition models, indicating excellent real-time performance. Therefore, the SVM recognition model can widely be expected to realize better performance than others in the segmentation of the maize stubble row after the maize is harvested by the combine harvesters. [ABSTRACT FROM AUTHOR]

Brace sleeve (BS) fasteners, i.e., nut and bolt, are small components but play essential roles in fixing BS and cantilever in railway catenary system. They are commonly inspected by onboard cameras using computer vision to ensure the safety of railway operation. However, most BS fasteners cannot be directly localized because they are too small in the inspection images. Instead, the BS is first localized for detecting the BS fastener. This leads to a new problem that the localized BS boxes may not contain the complete BS fasteners due to low localization accuracy, making it infeasible to further diagnose the fastener conditions. To tackle this problem, this article proposes a novel pipeline for BS fastener looseness diagnosis. First, the competitive deep learning model Faster RCNN ResNet101 is used to coarsely localize BSs. Second, an action-driven reinforcement learning agent is adopted to refine the coarse-localized boxes through a dynamic position searching process. Then, BS fasteners are extracted from the refined localized BS image by the deep segmentation model YOLACT++, which is fast and interpretable. Finally, a looseness diagnosis criterion based on segmented information are proposed. We evaluate the performance of submodels independently and the overall performance of the whole model on a real-life catenary image dataset collected from a high-speed line in China. The test results show that the proposed method is effective for BS looseness detection in railway catenary. [ABSTRACT FROM AUTHOR]

Automatic driving of agricultural machinery has drawn much more attention in recent years, particularly with the development of precision farming and the improvement of sensor technologies. Four parts of autonomous driving are positioning, perception, decision-making, and control system. In perception, the road recognition aims to extract the drivable area for the safe driving of agricultural machinery. However, there are no obvious lane markings or signs for field roads, while the road borders are in irregular shape, often shaded by trees. All of these features make it difficult for field road identification, unlike structured urban road. In road recognition, semantic segmentation on the collected road images is a binary classification task of background and road for each pixel to extract the drivable area. In this study, the data in spring and summer was collected in the Yufa Town, Daxing District, Beijing of China. A stereo camera was fixed on the agricultural machine to collect image data. The fixed position ensured that the camera was firm and reliable without being obscured during driving. The fixed height was set to 1.2 m. The driving speed of agricultural machinery was about 5 km/h during data collection. The field roads included semi-structured and unstructured roads. The sunny day was selected to collect data. The collecting time was about 4 hours, and a total of 1 600 pictures were captured. The training and test set were divided into the ratio of 4:1. The open- source software Labelme was used for image labeling. UNet was selected as the basic network, due to its simplicity and suitability for binary classification. A better performance was achieved when training on a small data set. Three improvements were also proposed for the UNet. 1) An identity mapping channel was established between every two convolutions, and the residual was constructed by adding pixels. The residual connection was used to alleviate the gradient disappearance and explosion during training, while easy the training of deep neural networks. 2) A fusion convolutional structure and the maximum pooling were established to replace the maximum pooling layer in the UNet. The useful information in the original image was maximized when halving feature map, where the segmentation of small area features was improved significantly. The inference time of the model was much longer because much more convolution operation increased the training parameters. 3) An asymmetric convolution structure was used in ACBlock, where the weight of the "skeleton" structure increased to improve the efficiency of feature extraction in the convolution kernel. Inspired by ACBlock, DACBlock was proposed using the dilated convolution, which further expanded the receptive field of the convolution feature map. ACBlock and DACBlock were used to replace the 3×3 convolution kernel in UNet. As such, the segmentation accuracy of road edge shapes was improved significantly. The hierarchical fusion and batch normalization were used in the inference stage to maintain that the number of parameters and inference time were all the same as the original structure. The improved UNet presented an IOU value of 85.03% for the field road segmentation, higher than the original UNet, ResUNet, and UNet3+. The recognition accuracy was relatively lower under cloudy weather in road junctions, due to insufficient light and occlusion. There was always water in the middle of the road after rain, where a certain degree of reflection occurred on the water under the mirror reflection. Therefore, the water increased the error of road segmentation. In the case of good or weak light in the evening and shade, the road segmentation was performed better for the safe driving of agricultural machinery. The segmentation accuracies of remote roads and road edges were also significantly better than those of other networks. Moreover, the average inference time of the model was 163 ms, meeting the time requirements of automatic driving in agricultural machinery. [ABSTRACT FROM AUTHOR]

In the current situation of frequent forest fires, the study of forest burned area mapping is important. However, there is still room for improvement in the accuracy of existing forest burning area mapping methods. Therefore, in this paper, an unsupervised method based on fire index enhancement and GRNN (General Regression Neural Network) is proposed for automated forest burned area mapping from single-date post-fire remote sensing imagery. The proposed method first uses adaptive spatial context information to enhance the generated fire index to improve its ability to indicate the burned areas. Then the uncertainty analysis is performed on the enhanced fire index to extract reliable burned samples and non-burned samples for subsequent classifier training. Finally, the improved GRNN model considering the spatial correlation of pixels is used as a classifier to binarize the enhanced fire index to generate the final burned area map. Based on two commonly used fire indexes, NBR (Normalized Burn Ratio) and BAI (Burned Area Index), this paper conducts burned area mapping experiments on a post-fire image of a forest area in Inner Mongolia, China to test the effectiveness of the proposed method, and two commonly used threshold methods (Otsu and Kmeans clustering) are also used to conduct burned area mapping based on threshold segmentation of fire index for comparison experiments. The experimental results prove the effectiveness and superiority of the proposed method. The proposed method is unsupervised and automated, so it has high application value and potential under the current situation of frequent forest fires. [ABSTRACT FROM AUTHOR]

This study develops and tests an automatic pixel-level image recognition model to reduce the amount of manual labor required to collect data for road maintenance. Firstly, images of six kinds of pavement distresses, namely, transverse cracks, longitudinal cracks, alligator cracks, block cracks, potholes, and patches, are collected from four asphalt highways in three provinces in China to build a labeled pixel-level dataset containing 10,097 images. Secondly, the U-net model, one of the most advanced deep neural networks for image segmentation, is combined with the ResNet neural network as the basic classification network to recognize distressed areas in the images. Data augmentation, batch normalization, momentum, transfer learning, and discriminative learning rates are used to train the model. Thirdly, the trained models are validated on the test dataset, and the results of experiments show the following: if the types of pavement distresses are not distinguished, the pixel accuracy (PA) values of the recognition models using ResNet-34 and ResNet-50 as basic classification networks are 97.336% and 95.772%, respectively, on the validation set. When the types of distresses are distinguished, the PA values of models using the two classification networks are 66.103% and 44.953%, respectively. For the model using ResNet-34, the category pixel accuracy (CPA) and intersection over union (IoU) of the identification of areas with no distress are 99.276% and 99.059%, respectively. For areas featuring distresses in the images, the CPA and IoU of the model are the highest for the identification of patches, at 82.774% and 73.778%, and are the lowest for alligator cracks, at 14.077% and 12.581%, respectively. [ABSTRACT FROM AUTHOR]

Accurate land use/land cover (LULC) mapping over a large area is essential to environmentally sustainable development. Recently, the Chinese government established a new national economic zone called the Xiongan New Area, and along with the upcoming large-scale urban construction, this area will inevitably experience a dramatic LULC change, which will threaten the local ecological balance. In this article, we proposed a two-stage approach for LULC mapping in the Xiongan New Area ahead of the forthcoming dense urban construction. The first stage is to obtain base-class maps through a supervised imagery classification. Specifically, we designed a new object-based framework consisting of automatic image segmentation, pixel-based probabilistic estimation, and area-weighted probability statistics for Sentinel-2 multiresolution imagery classification. The second stage is an LULC map refinement process in which the temporal features of each land use category are extracted to refine the LULC classification. Through the implementation of the proposed two-stage approach, an LULC map containing permanent water, temporal water, natural vegetation, barren land, built-up land, and cropland categories can be produced. Through an accuracy assessment, the proposed multiresolution imagery classification method achieved the highest overall accuracy of 88.58% and an average accuracy (AA) of 87.78% compared with conventional classification methods. After obtaining the refined LULC map, we find that the current Xiongan New Area is in a less developed state, that is, cropland accounts for the highest proportion of 51.59%, which is followed by natural vegetation (22.38%) and built-up land (15.69%). [ABSTRACT FROM AUTHOR]