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Aerial imagesemantic segmentation is crucial for various operations such as military observation, land classification, and disaster impact assessments involving unmanned aerial vehicles. Although existing system is unsuited for aerial applications, these algorithms are mostly trained on human-centric datasets like "Cityscapes and Cam Vid". High-resolution aerial image semantic segmentation is a basic and difficult task with several applications. Even though numerous Convolution Neural Network (CNN) segmentation techniques have shown impressive results, it is still challenging to discriminate semantic parts among regions with comparable spectral properties employing only high-resolution data. Additionally, the typical data-independent up-sampling techniques could produce poor outcomes. Thus, a novel semantic segmentation technique is introduced to resolve the complication presented in the classical segmentation framework in aerial images by utilizing deep learning techniques. Here, an Adaptive and Attentive based TransDeeplabv3 + (AA-TransDeeplabv3 +)-based semantic segmentation model for input images is designed with a novel Hybridized Fire Hawk with Electric Fish Optimization (HFH-EFO). The parameters of Attentive-based TransDeeplabv3 + are tuned by developed HFH-EFO to attain the multi-objective function. The model is implemented using Python, which generates the empirical results. Therefore, the developed method achieves a dice coefficient of 93.02% and an accuracy value of 93.01%, outperforming traditionalapproaches. Hence, the proposedframework secures anexcellent result than the classical technique based on experimental analysis. [ABSTRACT FROM AUTHOR]

Small object detection in aerial imagery remains challenging due to sparse feature representation, limited spatial resolution, and complex background interference. Current deep learning approaches enhance detection performance through multi-scale feature fusion, leveraging convolutional operations to expand the receptive field or self-attention mechanisms for global context modeling. However, these methods primarily rely on spatial-domain features, while self-attention introduces high computational costs, and conventional fusion strategies (e.g., concatenation or addition) often result in weak feature correlation or boundary misalignment. To address these challenges, we propose a unified spatial-frequency modeling and multi-scale alignment fusion framework, termed USF-DETR, for small object detection. The framework comprises three key modules: the Spatial-Frequency Interaction Backbone (SFIB), the Dual Alignment and Balance Fusion FPN (DABF-FPN), and the Efficient Attention-AIFI (EA-AIFI). The SFIB integrates the Scharr operator for spatial edge and detail extraction and FFT/IFFT for capturing frequency-domain patterns, achieving a balanced fusion of global semantics and local details. The DABF-FPN employs bidirectional geometric alignment and adaptive attention to enhance the significance expression of the target area, suppress background noise, and improve feature asymmetry across scales. The EA-AIFI streamlines the Transformer attention mechanism by removing key-value interactions and encoding query relationships via linear projections, significantly boosting inference speed and contextual modeling. Experiments on the VisDrone and TinyPerson datasets demonstrate the effectiveness of USF-DETR, achieving improvements of 2.3% and 1.4% mAP over baselines, respectively, while balancing accuracy and computational efficiency. The framework outperforms state-of-the-art methods in small object detection. [ABSTRACT FROM AUTHOR]

Object detection holds significant importance in remote sensing applications. However, the view angle and dynamic platform increase complexity compared to traditional tasks, such as limited feature representation, complex backgrounds, and data transmission issues. To address these challenges, ESOD-YOLO, an efficient detector based on YOLOv8 that optimally balances accuracy and efficiency is proposed in this work. ESOD-YOLO integrates three lightweight and plug-and-play modules: Deformable and Global–Local Feature Guidance (DGLFG), Simple Cross-scale and Cross-layer Feature Fusion(SCCFF), and Inverse-residual Shared Parameter based on a Partial convolution (ISPP). These modules improve the model's capabilities to sense target deformation, local and global correlations in spatial and channel dimensions, and multi-scale features fusion, respectively, avoiding increasing model complexity. Experiments are performed on three typical small-target datasets: VisDrone2019, AI-TOD, and UAVDT. The m A P 50 of ESOD-YOLO reaches 40.6%, 48.3%, 51.7%, meanwhile, results in VisDrone2019 reveal significant improvements over the baseline: the mAP improved by 3.6% and 2.7%, respectively, while the parameter count is reduced by 60%. These results demonstrate the efficiency of the proposed innovative approach. [ABSTRACT FROM AUTHOR]

Object detection is a major area of computer vision work, particularly for aerial surveillance and traffic control applications, where detecting vehicles from aerial images is essential. However, such images often lack semantic detail and struggle to identify small, densely packed objects accurately. This paper proposes improvements to the You Only Look Once version 5 (YOLOv5) model to enhance small object detection. Key modifications include adding a new prediction head with a 160×160 feature map, replacing the Sigmoid Linear Unit (SiLU) activation function with the Exponential Linear Unit (ELU), and swapping the Spatial Pyramid Pooling – Fast (SPPF) module with the Spatial Pyramid Pooling (SPP) module. The enhanced model was tested on two datasets: Dataset for Object Detection in Aerial Images (DOTA) v1.5 and CarJet, which focused on vehicle and plane detection. Results showed a 7.1% increase in mean Average Precision (mAP) on the DOTA dataset and a 2.3% improvement on the CarJet dataset, measured with an Intersection over Union (IoU) threshold of 0.5. These architectural changes to YOLOv5 notably improve small object detection accuracy, offering valuable potential for aerial surveillance and traffic control tasks. [ABSTRACT FROM AUTHOR]

Unmanned aerial vehicle (UAV) aerial images have characteristics such as small target scale and complex backgrounds, making it difficult to achieve satisfactory recognition accuracy using generic object detection methods directly on these types of images. Based on YOLOv8, this paper proposes a small object detection model called CFE-YOLO (cross-level feature-fusion enhanced-YOLO), which incorporates a feature enhancement network and a localized focal loss. Firstly, a cross-level feature-fusion enhanced pyramid network (CFEPN) is designed to improve the traditional feature pyramid structure by fusing attention feature maps. This is achieved by adding high-resolution feature maps from shallow networks and removing deep detection heads to adapt to the requirements of small object detection. Secondly, a focus loss function based on area intersection over union is designed by combining Complete-IOU and Focal loss function ideas. It is used to further improve the detection of small objects. Finally, a lightweight spatial pyramid pooling layer module is implemented by introducing depth-wise separable convolutions, maintaining the detection accuracy of the model while reducing the parameter count. Extensive experiments conducted on the UAV datasets VisDrone and Tinyperson show that CFE-YOLO improves the mAP0.50 by 4.72 and 5.58 percentage points respectively compared with the baseline, while reducing the parameter count by 37.74%. Furthermore, it achieves higher accuracy compared with other advanced algorithms.

Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Accurate image segmentation is essential for image-based estimation of vegetation canopy traits, as it minimizes background interference. However, existing segmentation models often lack the generalization ability to effectively tackle both ground-based and aerial images across a wide range of spatial resolutions. To address this limitation, a cross-spatial-resolution image segmentation model for rice crop was trained using the integration of in-situ and in silico multi-resolution images. We collected more than 3,000 RGB images (real set) covering 17 different resolutions reflecting diverse canopy structures, illumination conditions and background in rice fields, with vegetation pixels annotated manually. Using the previously developed Digital Plant Phenotyping Platform, we created a simulated dataset (sim set) including 10,000 RGB images with resolutions ranging from 0.5 to 3.5 mm/pixel, accompanied by corresponding mask labels. By employing a domain adaptation technique, the simulated images were further transformed into visually realistic images while preserving the original labels, creating a simulated-to-realistic dataset (sim2real set). Building upon a SegFormer deep learning model, we demonstrated that training with multi-resolution samples led to more generalized segmentation results than single-resolution training on the real dataset. Our exploration of various integration strategies revealed that a training set of 9,600 sim2real images combined with only 60 real images achieved the same segmentation accuracy as 2,400 real images (IoU = 0.819, F1 = 0.901). Moreover, combining 2,400 real images and 1,200 sim2real images resulted in the best performing model, effective against six challenging situations, such as specular reflections and shadows. Compared with models trained with single-resolution samples and an established model (i.e., VegANN), our model effectively improved the estimation of both green fraction and green area index across spatial resoultions. The strategy of bridging real and simulated data for cross-resolution deep learning model is expected to be applicable to other crops. The best trained model is available at https://github.com/PheniX-Lab/crossGSD-seg. [ABSTRACT FROM AUTHOR]

In real world datasets of aerial images, the objects of interest are often missing, hard to annotate and of varying aspects. The framework of unsupervised Anomaly Detection (AD) is highly relevant in this context, and Variational Autoencoders (VAEs), a family of popular probabilistic models, are often used. We develop on the literature of VAEs for AD in order to take advantage of the particular textures that appear in natural aerial images. More precisely we propose a new VAE model with a Gaussian Random Field (GRF) prior (VAE-GRF), which generalizes the classical VAE model, and we provide the necessary procedures and hypotheses required for the model to be tractable. We show that, under some assumptions, the VAE-GRF largely outperforms the traditional VAE and some other probabilistic models developed for AD. Our results suggest that the VAE-GRF could be used as a relevant VAE baseline in place of the traditional VAE with very limited additional computational cost. We provide competitive results on the MVTec reference dataset for visual inspection, and two other datasets dedicated to the task of unsupervised animal detection in aerial images. [ABSTRACT FROM AUTHOR]

Object detection in aerial images poses significant challenges due to the high dimensions of the images, requiring efficient handling and resizing to fit object detection models. The image-slicing approach for object detection in aerial images can increase detection accuracy by eliminating pixel loss in high-resolution image data. However, determining the proper dimensions to slice is essential for the integrity of the objects and their learning by the model. This study presents an evaluation of the image-slicing approach for alternative sizes of images to optimize efficiency. For this purpose, a dataset of high-resolution images collected with Unmanned Aerial Vehicles (UAV) has been used. The experiments evaluated using alternative YOLO architectures like YOLOv7, YOLOv8, and YOLOv9 show that the image dimensions significantly change the performance results. According to the experiments, the best mAP@05 accuracy was obtained by slicing 1280 × 1280 for YOLOv7 producing 88.2. Results show that edge-related objects are better preserved as the overlap and slicing sizes increase, resulting in improved model performance. [ABSTRACT FROM AUTHOR]

Crowd counting in aerial images presents unique challenges due to varying altitudes, angles, and cluttered backgrounds. Additionally, the small size of targets, often occupying only a few pixels in high-resolution images, further complicates the problem. Current crowd counting models struggle in these complex scenarios, leading to inaccurate counts, which are crucial for crowd management. Moreover, these regression-based models only provide the total count without indicating the location or distribution of people within the environment, limiting their practical utility. While YOLOv8 has achieved significant success in detecting small targets within aerial imagery, it faces challenges when directly applied to crowd counting tasks in such contexts. To overcome these challenges, we propose an improved framework based on YOLOv8, incorporating a context enrichment module (CEM) to capture multiscale contextual information. This enhancement improves the model's ability to detect and localize tiny targets in complex aerial images. We assess the effectiveness of the proposed framework on the challenging VisDrone-CC2021 dataset, and our experimental results demonstrate the effectiveness of this approach. [ABSTRACT FROM AUTHOR]

Aerial images have distinct characteristics, such as varying target scales, complex backgrounds, severe occlusion, small targets, and dense distribution. As a result, object detection in aerial images faces challenges like difficulty in extracting small target information and poor integration of spatial and semantic data. Moreover, existing object detection algorithms have a large number of parameters, posing a challenge for deployment on drones with limited hardware resources. We propose an efficient small-object YOLO detection model (ESOD-YOLO) based on YOLOv8n for Unmanned Aerial Vehicle (UAV) object detection. Firstly, we propose that the Reparameterized Multi-scale Inverted Blocks (RepNIBMS) module is implemented to replace the C2f module of the Yolov8n backbone extraction network to enhance the information extraction capability of small objects. Secondly, a cross-level multi-scale feature fusion structure, wave feature pyramid network (WFPN), is designed to enhance the model's capacity to integrate spatial and semantic information. Meanwhile, a small-object detection head is incorporated to augment the model's ability to identify small objects. Finally, a tri-focal loss function is proposed to address the issue of imbalanced samples in aerial images in a straightforward and effective manner. In the VisDrone2019 test set, when the input size is uniformly 640 × 640 pixels, the parameters of ESOD-YOLO are 4.46 M, and the average mean accuracy of detection reaches 29.3%, which is 3.6% higher than the baseline method YOLOv8n. Compared with other detection methods, it also achieves higher detection accuracy with lower parameters. [ABSTRACT FROM AUTHOR]

Traditional disease severity monitoring is subjective and inefficient. This study employs a Parrot multispectral sensor mounted on an unmanned aerial vehicle (UAV) to apply machine learning algorithms, such as random forest, for categorizing defoliation levels in R7-stage soybean plants. This research assesses the effectiveness of vegetation indices, spectral bands, and relative vegetation cover as input parameters, demonstrating that machine learning approaches combined with multispectral imagery can provide a more accurate and efficient assessment of Asian soybean rust in commercial soybean fields. The random forest algorithm exhibited satisfactory classification performance when compared to recent studies, achieving accuracy, precision, recall, F1-score, specificity, and AUC values of 0.94, 0.92, 0.92, 0.92, 0.97, and 0.97, respectively. The input variables identified as most important for the classification model were the WDRVI and MPRI indices, the red-edge and NIR bands, and relative vegetation cover, with the highest Gini importance index. [ABSTRACT FROM AUTHOR]

Purpose: This paper proposes a low-cost and low-effort solution for determining the area of corn crops damaged by the wildlife facility utilising field images collected by an unmanned aerial vehicle (UAV). The proposed solution allows for the determination of the percentage of the damaged crops and their location. Methods: The method utilises image segmentation models based on deep convolutional neural networks (e.g., UNet family) and transformers (SegFormer) trained on over 300 hectares of diverse corn fields in western Poland. A range of neural network architectures was tested to select the most accurate final solution. Results: The tests show that despite using only easily accessible RGB data available from inexpensive, consumer-grade UAVs, the method achieves sufficient accuracy to be applied in practical solutions for agriculture-related tasks, as the IoU (Intersection over Union) metric for segmentation of healthy and damaged crop reaches 0.88. Conclusion: The proposed method allows for easy calculation of the total percentage and visualisation of the corn crop damages. The processing code and trained model are shared publicly. [ABSTRACT FROM AUTHOR]

Detailed, accurate information on flow patterns in river channels can improve understanding of habitat conditions, geomorphic processes, and potential hazards to help inform water management. Data describing flow patterns in river channels can be obtained efficiently via image‐based techniques that have become more widely used in recent years as the number of platforms for acquiring images has expanded and the number of algorithms for inferring velocities has grown. Image‐based techniques have been incorporated into various software packages, including the Toolbox for River Velocimetry using Images from Aircraft (TRiVIA). TRiVIA is a freely available, standalone computer program that provides a comprehensive workflow for performing particle image velocimetry (PIV)‐based analyses within a graphical interface. This paper summarizes major enhancements incorporated into the latest release of TRiVIA, version 2.1. For example, a new Tool for Input Parameter Selection (TIPS) provides guidance for specifying key inputs to the PIV algorithm by allowing users to explore relationships between flow velocity, pixel size, output vector spacing, and frame interval. Improved visualization capabilities include the ability to create streamlines and display PIV output on an interactive web map. The program now provides greater flexibility for importing field data in various formats and selecting which observations to use for accuracy assessment. The most substantial additions to TRiVIA 2.1 are the ability to integrate bathymetric information with image‐derived velocity estimates to calculate river discharge and to use images acquired from moving aircraft to efficiently map long segments of large rivers to support habitat assessment, contaminant transport studies, and a range of other applications. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Test & Measurement Technology is the property of Publishing Center of North University of China and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Object detection is a major area of computer vision work, particularly for aerial surveillance and traffic control applications, where detecting vehicles from aerial images is essential. However, such images often lack semantic detail and struggle to identify small, densely packed objects accurately. This paper proposes improvements to the You Only Look Once version 5 (YOLOv5) model to enhance small object detection. Key modifications include adding a new prediction head with a 160×160 feature map, replacing the Sigmoid Linear Unit (SiLU) activation function with the Exponential Linear Unit (ELU), and swapping the Spatial Pyramid Pooling – Fast (SPPF) module with the Spatial Pyramid Pooling (SPP) module. The enhanced model was tested on two datasets: Dataset for Object Detection in Aerial Images (DOTA) v1.5 and CarJet, which focused on vehicle and plane detection. Results showed a 7.1% increase in mean Average Precision (mAP) on the DOTA dataset and a 2.3% improvement on the CarJet dataset, measured with an Intersection over Union (IoU) threshold of 0.5. These architectural changes to YOLOv5 notably improve small object detection accuracy, offering valuable potential for aerial surveillance and traffic control tasks.

UAVs (Unmanned Aerial Vehicles) have become increasingly popular in the agricultural sector, promoting and enabling the application of aerial image monitoring in both the scientific and business contexts. Images captured by UAVs are fundamental for precision farming practices. They enable us do a better crop planning, input estimates, early identification and correction of sowing failures, more efficient irrigation systems, among other tasks. Since all these activities deal with low or medium altitude images, automated identification of crop lines plays a crucial role improving these tasks. We address the problem of detecting and segmenting crop lines. We use a Convolutional Neural Network to segment the images, labeling their regions in crop lines or unplanted soil. We also evaluated three traditional semantic networks: U-Net, LinkNet, and PSPNet. We compared each network in four segmentation datasets provided by an expert. We also assessed whether the network’s output requires a post-processing step to improve the segmentation. Results demonstrate the efficiency and feasibility of these networks in the proposed task.

Retrieved rooftops from satellite images have enormous applications. The diversity and complexity of the building structures is challenging. This work proposes to extract building rooftops using two low-complexity DL models: UNet-AstPPD and UNetVasyPPD. The UNet-AstPPD model enhances feature selection by incorporating Atrous Spatial Pyramidal Pooling into the UNet's decoder. The UNetVasyPPD integrates a VGG-based backbone in the encoder and Asymmetrical Pyramidal-Pooling into the decoder section of the UNet architecture, exhibiting lesser computational complexity. The outcomes demonstrate that Accuracy and Dice Loss of UNet-AstPPD are better. The proposed models training times are just 25.44 minutes and 29.23 minutes respectively. [ABSTRACT FROM AUTHOR]

The progress of object detection technology is crucial for obtaining extensive scene information from aerial perspectives based on computer vision. However, aerial image detection presents many challenges, such as large image background sizes, small object sizes, and dense distributions. This research addresses the specific challenges relating to small object detection in aerial images and proposes an improved YOLOv8s-based detector named Aerial Images Detector-YOLO(AID-YOLO). Specifically, this study adopts the General Efficient Layer Aggregation Network (GELAN) from YOLOv9 as a reference and designs a four-branch skip-layer connection and split operation module Re-parameterization-Net with Cross-Stage Partial CSP and Efficient Layer Aggregation Networks (RepNCSPELAN4) to achieve a lightweight network while capturing richer feature information. To fuse multi-scale features and focus more on the target detection regions, a new multi-channel feature extraction module named Convolutional Block Attention Module with Two Convolutions Efficient Layer Aggregation Net-works (C2FCBAM) is designed in the neck part of the network. In addition, to reduce the sensitivity to position bias of small objects, a new function, Normalized Weighted Distance Complete Intersection over Union (NWD-CIoU_Loss) weight adaptive loss function, was designed in this study. We evaluate the proposed AID-YOLO method through ablation experiments and comparisons with other advanced models on the VEDAI (512, 1024) and DOTAv1.0 datasets. The results show that compared to the Yolov8s baseline model, AID-YOLO improves the mAP@0.5 metric by 7.36% on the VEDAI dataset. Simultaneously, the parameters are reduced by 31.7%, achieving a good balance between accuracy and parameter quantity. The Average Precision (AP) for small objects has improved by 8.9% compared to the baseline model (YOLOv8s), making it one of the top performers among all compared models. Furthermore, the FPS metric is also well-suited for real-time detection in aerial image scenarios. The AID-YOLO method also demonstrates excellent performance on infrared images in the VEDAI1024 (IR) dataset, with a 2.9% improvement in the mAP@0.5 metric. We further validate the superior detection and generalization performance of AID-YOLO in multi-modal and multi-task scenarios through comparisons with other methods on different resolution images, SODA-A and the DOTAv1.0 datasets. In summary, the results of this study confirm that the AID-YOLO method significantly improves model detection performance while maintaining a reduced number of parameters, making it applicable to practical engineering tasks in aerial image object detection. [ABSTRACT FROM AUTHOR]

Object detection in aerial images plays a crucial role across diverse domains such as agriculture, environmental monitoring, and security. Aerial images present several challenges, including dense small objects, intricate backgrounds, and occlusions, necessitating robust detection algorithms. This paper addresses the critical need for accurate and efficient object detection in aerial images using a Transformer-based approach enhanced with specialized methodologies, termed DFS-DETR. The core framework leverages RT-DETR-R18, integrating the Cross Stage Partial Reparam Dilation-wise Residual Module (CSP-RDRM) to optimize feature extraction. Additionally, the introduction of the Detail-Sensitive Pyramid Network (DSPN) enhances sensitivity to local features, complemented by the Dynamic Scale Sequence Feature-Fusion Module (DSSFFM) for comprehensive multi-scale information integration. Moreover, Multi-Attention Add (MAA) is utilized to refine feature processing, which enhances the model's capacity for understanding and representation by integrating various attention mechanisms. To improve bounding box regression, the model employs MPDIoU with normalized Wasserstein distance, which accelerates convergence. Evaluation across the VisDrone2019, AI-TOD, and NWPU VHR-10 datasets demonstrates significant improvements in the mean average precision (mAP) values: 24.1%, 24.0%, and 65.0%, respectively, surpassing RT-DETR-R18 by 2.3%, 4.8%, and 7.0%, respectively. Furthermore, the proposed method achieves real-time inference speeds. This approach can be deployed on drones to perform real-time ground detection. [ABSTRACT FROM AUTHOR]

Object detection in unmanned aerial vehicle (UAV) aerial images has become increasingly important in military and civil applications. General object detection models are not robust enough against interclass similarity and intraclass variability of small objects, and UAV-specific nuisances such as uncontrolled weather conditions. Unlike previous approaches focusing on high-level semantic information, we report the importance of underlying features to improve detection accuracy and robustness from the information-theoretic perspective. Specifically, we propose a robust and discriminative feature learning approach through mutual information maximization (RD-MIM), which can be integrated into numerous object detection methods for aerial images. Firstly, we present the rank sample mining method to reduce underlying feature differences between the natural image domain and the aerial image domain. Then, we design a momentum contrast learning strategy to make object features similar to the same category and dissimilar to different categories. Finally, we construct a transformer-based global attention mechanism to boost object location semantics by leveraging the high interrelation of different receptive fields. We conduct extensive experiments on the VisDrone and Unmanned Aerial Vehicle Benchmark Object Detection and Tracking (UAVDT) datasets to prove the effectiveness of the proposed method. The experimental results show that our approach brings considerable robustness gains to basic detectors and advanced detection methods, achieving relative growth rates of 51.0% and 39.4% in corruption robustness, respectively. Our code is available at (accessed on 2 August 2024). [ABSTRACT FROM AUTHOR]

A software tool was developed to extract the positions of features identified in individual undistorted images acquired by unmanned aircraft systems (UAS), to support operations to locate and control invasive organisms in sensitive natural habitats. The tool determines a feature's position based on selected pixels and metadata in the image. Accuracy tests were performed using test imagery obtained from different camera altitudes (30, 40 and 50 m above ground level) and orientations (0°, 15° and 30° from the vertical) for direct geolocation of features of interest. As an additional case study, the tool was integrated with a deep neural network (DNN) for simultaneous detection and geolocation of the invasive tree Miconia calvescens in natural landscapes on Hawaii Island. For vertical camera orientations, median horizontal position errors were below 5 m. Images from oblique camera views resulted in larger median errors, approaching 10 m. While numerous approaches have been used to improve locational accuracy of objects identified in aerial imagery, the presented tool can be applied directly to individual images collected with commercial off-the-shelf UAS and flight planning software, making it accessible for applications in natural resource management. Furthermore, the tool does not require accurate ground control points, so it is especially suitable for locating invasive plants in dynamic natural landscapes with extensive forest canopy. [ABSTRACT FROM AUTHOR]

The segmentation of remote sensing images has emerged as a compelling undertaking in computer vision owing to its use in the development of several applications. The U-Net style has been extensively utilized in many picture segmentation applications, yielding remarkable achievements. Nevertheless, the U-Net has several constraints in the context of remote sensing picture segmentation, mostly stemming from the limited scope of the convolution kernels. The transformer is a deep learning model specifically developed for sequence-to-sequence translation. It incorporates a self-attention mechanism to efficiently process many inputs, selectively retaining the relevant information and discarding the irrelevant inputs by adjusting the weights. However, it highlights a constraint in the localization capability caused by the absence of fundamental characteristics. This work presents a novel approach called U-Net–transformer, which combines the U-Net and transformer models for the purpose of remote sensing picture segmentation. The suggested solution surpasses individual models, such as U-Net and transformers, by combining and leveraging their characteristics. Initially, the transformer obtains the overall context by encoding tokenized picture patches derived from the feature maps of the convolutional neural network (CNN). Next, the encoded feature maps undergo upsampling through a decoder and are then merged with the high-resolution feature maps of the CNN model. This enables the localization to be more accurate. The transformer serves as an unconventional encoder for segmenting remote sensing images. It enhances the U-Net model by capturing localized spatial data, hence improving the capacity to capture intricate details. The U-Net–transformer, as suggested, has demonstrated exceptional performance in remote sensing picture segmentation across many benchmark datasets. The given findings demonstrated the efficacy of integrating the U-Net and transformer model for the purpose of segmenting remote sensing images. [ABSTRACT FROM AUTHOR]

This paper presents an innovative approach to detect solar panel defects early, leveraging distinct datasets comprising aerial and electroluminescence (EL) images. The decision to employ separate datasets with different models signifies a strategic choice to harness the unique strengths of each imaging modality. Aerial images provide comprehensive surface-level insights, while electroluminescence images offer valuable information on internal defects. By using these datasets with specialized models, the study aims to improve defect detection accuracy and reliability. The research explores the effectiveness of modified deep learning models, including DenseNet121 and MobileNetV3, for analyzing aerial images, and introduces a customized architecture and EfficientNetV2B2 models for electroluminescence image analysis. Results indicate promising accuracies for DenseNet121 (93.75%), MobileNetV3 (93.26%), ELFaultNet (customized architecture) (91.62%), and EfficientNetV2B2 (81.36%). This study's significance lies in its potential to transform solar panel maintenance practices, enabling early defect identification and subsequent optimization of energy production. [ABSTRACT FROM AUTHOR]

The evolution of map development has been shaped by advancing techniques and technologies. Nevertheless, field and remote mapping with cabinet data analysis remains essential in this process. Geological maps are thematic maps that delineate diverse geological features. These maps undergo updates reflecting changes in the mapped area, technological advancements, and the availability of new data. Herein, a geological assessment example focused on enhancing mapped data using digitally processed historical (legacy) aerial images is presented for a case study in the Dinarides karst area in Croatia. The study area of Bribirske Mostine is covered by the Basic Geological Map of Yugoslavia (BGMY) at a 100,000 scale, which was developed during the 1960s. As the BGMY was developed 60+ years ago, one of its segments is further analyzed and discussed, namely, faults. Moreover, applying modern-day technologies and reinterpretation, its data, scale, presentation, and possible areas of improvement are presented. Georeferenced digital historical geological data (legacy), comprising BGMY, archive field maps, and aerial images from 1959 used in BGMY development, are reviewed. Original faults were digitalized and reinterpreted within the geographic information system with the following conclusions: (i) more accurate data (spatial positioning) on faults can be gained by digitally processing aerial photographs taken 60+ years ago with detailed review and analysis; (ii) simultaneously, new data were acquired (additional fault lines were interpreted); (iii) the map scale can be up-scaled to 1:25,000 for the investigated area of Bribirske Mostine; and (iv) a newly developed map for the Bribirske Mostine study area is presented. [ABSTRACT FROM AUTHOR]

In the present study, high resolution drone images were captured and analysed for the determination of soil moisture content by the generation of Normalized Difference Soil Moisture Index (NDSMI). The experimental field comprised of six small blocks of 20 × 20 m, and all samples were collected twice a week from every block from the depth of 5 cm for soil moisture determination using the gravimetric method. Aerial images were taken from an altitude of 30 m from the ground to take a full image of the experimental field. The drone takes composite images comprising of blue, green and red bands of the optical region of the spectrum. There are three different types of soil in agricultural fields: bare soil, soil covered in vegetation, and soil covered in crop waste (Shankar et al. Journal of the Indian Society of Remote Sensing 50(3):435?450, 2022). The temporal variation in the moisture content of the study area with sandy clay loam soil texture was found to be 16–25% during the experimental season based on the gravimetric moisture determination method. Band 1(Blue) and Band 3 (Red) of the visual region were used to generate the Normalized Difference Blue-Red Soil Moisture Index(NDBRSMI) for representing moisture content of the study area. The NDBRSMI values of the generated index were found to be in between 0.3 and 0.6. Scaling factor was found to be 45.56 to convert any pixel value of the generated NDBRSMI to soil moisture content. The generated NDSMI index values displayed a strong correlation on conversion to soil moisture data after multiplying with the scaling factor as displayed by values of coefficient of determination (R2), Nash-Sutcliff Efficiency (ENS), standard deviation ratio (RSR) and Percent Bias (PBIAS)between the observed and the simulated moisture content. [ABSTRACT FROM AUTHOR]

Aerial image semantic segmentation has extensive applications in the fields of land resource management, ecological monitoring, and traffic management. Currently, many convolutional neural networks have been developed, but they do not fully utilize the long-term dependence and multi-scale information in high-resolution images, making it difficult for these models to further enhance their segmentation performance. Therefore, a multiple-attention-based dual branch feature aggregation network is proposed to improve the segmentation accuracy of aerial images. This model includes a contextual feature extraction branch (CFEB), a spatial information extraction branch (SIEB), and a feature aggregation module (FAM). In the CFEB, we designed a SeMask-based dual category attention module to extract semantic category features and utilized the ASPP module to extract multi-scale features, effectively capturing global contextual information with categories and multi-scales. Meanwhile, in the SIEB, a shallow CNN is employed to retain the fine-grained features of images. In the FAM, a dual attention interaction module is designed that includes spatial attention and channel attention, effectively fusing the global contextual and spatial local information extracted by the two branches. Extensive experiments on three freely accessible datasets (the UAVid dataset, the Landcover.ai dataset and the Vaihingen dataset) demonstrate that our method outperforms other state-of-the-art models for aerial images. [ABSTRACT FROM AUTHOR]

Tree species composition is essential information for forest management and remotely sensed (RS) data have proven to be useful for its prediction. In forest management inventories, tree species are commonly interpreted manually from aerial images for each stand, which is time and resource consuming and entails substantial uncertainty. The objective of this study was to evaluate a range of RS data sources comprising airborne laser scanning (ALS) and airborne and satellite-borne multispectral data for model-based prediction of tree species composition. Total volume was predicted using non-linear regression and volume proportions of species were predicted using parametric Dirichlet models. Predicted dominant species was defined as the species with the greatest predicted volume proportion and predicted species-specific volumes were calculated as the product of predicted total volume multiplied by predicted volume proportions. Ground reference data obtained from 1184 sample plots of 250 m2 in eight districts in Norway were used. Combinations of ALS and two multispectral data sources, i.e. aerial images and Sentinel-2 satellite images from different seasons, were compared. The most accurate predictions of tree species composition were obtained by combining ALS and multi-season Sentinel-2 imagery, specifically from summer and fall. Independent validation of predicted species proportions yielded average root mean square differences (RMSD) of 0.15, 0.15 and 0.07 (relative RMSD of 30%, 68% and 128%) and squared Pearson's correlation coefficient (r2) of 0.74, 0.79 and 0.51 for Norway spruce (Picea abies (L.) Karst.), Scots pine (Pinus sylvestris L.) and deciduous species, respectively. The dominant species was predicted with median values of overall accuracy, quantity disagreement and allocation disagreement of 0.90, 0.07 and 0.00, respectively. Predicted species-specific volumes yielded average values of RMSD of 63, 48 and 23 m3/ha (relative RMSD of 39%, 94% and 158%) and r2 of 0.84, 0.60 and 0.53 for spruce, pine and deciduous species, respectively. In one of the districts with independent validation plots of mean size 3700 m2, predictions of the dominant species were compared to results obtained through manual photo-interpretation. The model predictions gave greater accuracy than manual photo-interpretation. This study highlights the utility of RS data for prediction of tree species composition in operational forest inventories, particularly indicating the utility of ALS and multi-season Sentinel-2 imagery.

Vehicle detection in congested urban scenes is essential for traffic control and safety management. However, the dense arrangement and occlusion of multi-scale vehicles in such environments present considerable challenges for detection systems. To tackle these challenges, this paper introduces a novel object detection method, dubbed the triple focus diffusion network (TFDNet). Firstly, the gradient convolution is introduced to construct the C2f-EIRM module, replacing the original C2f module, thereby enhancing the network’s capacity to extract edge information. Secondly, by leveraging the concept of the Asymptotic Feature Pyramid Network on the foundation of the Path Aggregation Network, the triple focus diffusion module structure is proposed to improve the network’s ability to fuse multi-scale features. Finally, the SPPF-ELA module employs an Efficient Local Attention mechanism to integrate multi-scale information, thereby significantly reducing the impact of background noise on detection accuracy. Experiments on the VisDrone 2021 dataset reveal that the average detection accuracy of the TFDNet algorithm reached 38.4%, which represents a 6.5% improvement over the original algorithm; similarly, its mAP50:90 performance has increased by 3.7%. Furthermore, on the UAVDT dataset, the TFDNet achieved a 3.3% enhancement in performance compared to the original algorithm. TFDNet, with a processing speed of 55.4 FPS, satisfies the real-time requirements for vehicle detection.

With the advancement of various aerial platforms, there is an increasing abundance of aerial images captured in various environments. However, the detection of densely packed small objects within complex backgrounds remains a challenge. To address the task of detecting multiple small objects, a multi-object detection algorithm based on distance intersection over union loss non-maximum suppression (DIOU-NMS) integrated with you only look once version 5 (YOLOv5) is proposed. Leveraging the YOLOv5s model as the foundation, the algorithm specifically addresses the detection of abundantly and densely packed targets by incorporating a dedicated small object detection layer within the network architecture, thus effectively enhancing the detection capability for small targets using an additional upsampling operation. Moreover, conventional non-maximum suppression is replaced with DIOU-based non-maximum suppression to alleviate the issue of missed detections caused by target density. Experimental results demonstrate the effectiveness of the proposed method in significantly improving the detection performance of dense small targets in complex backgrounds.

OpenAerialMap (OAM) is a crowdsourcing platform for uploading, hosting, sharing, displaying, searching, and downloading openly licensed Earth image data from around the world. OAM was launched with the primary goal to facilitate rapid disaster response mapping after natural events, such as floods or hurricanes. Images contributed to OAM can be used as a background layer in OpenStreetMap (OSM) editors to edit map features, such as buildings or facilities, which may have been affected by such events. This study analyzes how the provision of OAM images is associated with changes in underlying editing patterns of OSM features by comparing the number of edited OSM objects between 2 weeks before and 2 weeks after an image has been shared through OAM. The comparison also involves other aspects of OSM editing patterns, including geometry types and OSM primary feature types of edited objects, type of feature editing operations, edits per user, and contribution distribution across continents. Results show that the number of point features added to OSM more than quadrupled within the first 2 weeks after OAM image upload compared to that of 2 weeks before, and that the number of ways added almost doubled. This suggests that the OSM community utilizes provided OAM images for OSM map updates in the respective geographic areas. The study provides a showcase which demonstrates how information from one crowdsourcing platform (OAM) can be used to enhance the data quality of another (OSM) with regards to completeness and timeliness.

With the increasing demand for marine monitoring, the use of coverage path planning based on unmanned aerial vehicle (UAV) aerial images to assist multiple unmanned surface vehicles (USVs) has shown great potential in marine applications. However, achieving accurate map modeling and optimal path planning are still key challenges that restrict its widespread application. To this end, an innovative coverage path planning algorithm for UAV-assisted multiple USVs is proposed. First, a semantic segmentation algorithm based on the YOLOv5-assisted prompting segment anything model (SAM) is designed to establish an accurate map model. By refining the axial, length, width, and coordinate information of obstacles, the algorithm enables YOLOv5 to generate accurate object bounding box prompts and then assists SAM in automatically and accurately extracting obstacles and coastlines in complex scenes. Based on this accurate map model, a multi-objective stepwise optimization coverage path planning algorithm is further proposed. The algorithm divides the complete path into two parts, the straight paths and the turning paths, and both the path length and the number of turns is designed, respectively, to optimize each type of path step by step, which significantly improves the coverage effect. Experiments prove that in various complex marine coverage scenarios, the proposed algorithm achieves 100% coverage, the redundancy rate is less than 2%, and it is superior to existing advanced algorithms in path length and number of turns. This research provides a feasible technical solution for efficient and accurate marine coverage tasks and lays the foundation for unmanned marine supervision.