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Natural hazards are considered to have a strong link with climate change and human activities. With the rapid advancements in remote sensing technology, real-time monitoring and high-resolution remote-sensing images have become increasingly available, which provide precise details about the Earth's surface and enable prompt updates to support risk identification and management. This paper proposes a new network framework with Transformer architecture and a Residual network for detecting the changes in high-resolution remote-sensing images. The proposed model is trained using remote-sensing images from Shandong and Anhui Provinces of China in 2021 and 2022 while one district in 2023 is used to test the prediction accuracy. The performance of the proposed model is evaluated by using five matrices and further compared to both convention-based and attention-based models. The results demonstrated that the proposed structure integrates the great capability of conventional neural networks for image feature extraction with the ability to obtain global context from the attention mechanism, resulting in significant improvements in balancing positive sample identification while avoiding false positives in complex image change detection. Additionally, a toolkit supporting image preprocessing is developed for practical applications. [ABSTRACT FROM AUTHOR]

Obtaining accurate and timely crop area information is crucial for crop yield estimates and food security. Because most existing crop mapping models based on remote sensing data have poor generalizability, they cannot be rapidly deployed for crop identification tasks in different regions. Based on a priori knowledge of phenology, we designed an off-center Bayesian deep learning remote sensing crop classification method that can highlight phenological features, combined with an attention mechanism and residual connectivity. In this paper, we first optimize the input image and input features based on a phenology analysis. Then, a convolutional neural network (CNN), recurrent neural network (RNN), and random forest classifier (RFC) were built based on farm data in northeastern Inner Mongolia and applied to perform comparisons with the method proposed here. Then, classification tests were performed on soybean, maize, and rice from four measurement areas in northeastern China to verify the accuracy of the above methods. To further explore the reliability of the method proposed in this paper, an uncertainty analysis was conducted by Bayesian deep learning to analyze the model's learning process and model structure for interpretability. Finally, statistical data collected in Suibin County, Heilongjiang Province, over many years, and Shandong Province in 2020 were used as reference data to verify the applicability of the methods. The experimental results show that the classification accuracy of the three crops reached 90.73% overall and the average F1 and IOU were 89.57% and 81.48%, respectively. Furthermore, the proposed method can be directly applied to crop area estimations in different years in other regions based on its good correlation with official statistics. [ABSTRACT FROM AUTHOR]

Simple Summary: Most management methods for poultry farming currently rely on human labor. Such management is labor-intensive and inefficient, especially in identifying poultry growth stages. Given the lack of a high-precision artificial method for chick's day-age detection, a high-performance day-age classification and detection model for chicks was proposed based on artificial intelligent techniques. This method can detect and classify chicks in six different living stages from 1 to 32 day-ages, and the accuracy is 95.2%, superior to other current ones. In order to apply this method in practical scenarios, it has been deployed into an application based on the IOS system, which can recoganize the day-age of chicks by capturing real-time photos. The system is currently deployed in Rizhao City, Shandong Province, China. It helps chicken farm staff automatically detect the behavior of chickens, whose excellent working effect proves the robust availibility of the proposed method. Thanks to the boom of computer vision techniques and artificial intelligence algorithms, it is more available to achieve artificial rearing for animals in real production scenarios. Improving the accuracy of chicken day-age detection is one of the instances, which is of great importance for chicken rearing. To solve this problem, we proposed an attention encoder structure to extract chicken image features, trying to improve the detection accuracy. To cope with the imbalance of the dataset, various data enhancement schemes such as Cutout, CutMix, and MixUp were proposed to verify the effectiveness of the proposed attention encoder. This paper put the structure into various mainstream CNN networks for comparison and multiple ablation experiments. The final experimental results show that by applying the attention encoder structure, ResNet-50 can improve the accuracy of chicken age detection to 95.2%. Finally, this paper also designed a complete image acquisition system for chicken houses and a detection application configured for mobile devices. [ABSTRACT FROM AUTHOR]

Short-term load forecasting plays a significant role in the operation of power systems. Recently, deep learning has been generally employed in short-term load forecasting, primarily in the extraction of the characteristics of digital information in a single dimension without taking into account of the impact of external variables, particularly non-digital elements on load characteristics. In this paper, we propose a joint MFF-SAM-GCN to realize short-term load forecasting. First, we utilize a Bi-directional Long Short-Term Memory (Bi-LSTM) network and One-Dimensional Convolutional Neural Network (1D-CNN) in parallel connection to form a multi-feature fusion (MFF) framework, which can extract spatiotemporal correlation features of the load data. In addition, we introduce a Self-Attention Mechanism (SAM) to further enhance the feature extraction capability of the 1D-CNN network. Then with the deployment of a Graph Convolutional Network (GCN), the external non-digital features such as weather, strength, and direction of wind, etc., are extracted. Moreover, the generated weight matrices are incorporated into the load features to enhance feature recognition ability. Finally, we exploit Bayesian Optimization (BO) to find the optimal hyperparameters of the model to further improve the prediction accuracy. The simulation is taken from our proposed model and six benchmark schemes by using the bus load dataset of the Shandong Open Data Network, China. The results show that the RMSE of our proposed MFF-SAM-GCN model is 0.0284, while the SMAPE is 9.453%,the MBE is 0.025, and R-squared is 0.989, which is better than the selected three traditional machine learning methods and the three deep learning models. [ABSTRACT FROM AUTHOR]

Agricultural greenhouses (AGs) are an important component of modern facility agriculture, and accurately mapping and dynamically monitoring their distribution are necessary for agricultural scientific management and planning. Semantic segmentation can be adopted for AG extraction from remote sensing images. However, the feature maps obtained by traditional deep convolutional neural network (DCNN)-based segmentation algorithms blur spatial details and insufficient attention is usually paid to contextual representation. Meanwhile, the maintenance of the original morphological characteristics, especially the boundaries, is still a challenge for precise identification of AGs. To alleviate these problems, this paper proposes a novel network called high-resolution boundary refined network (HBRNet). In this method, we design a new backbone with multiple paths based on HRNetV2 aiming to preserve high spatial resolution and improve feature extraction capability, in which the Pyramid Cross Channel Attention (PCCA) module is embedded to residual blocks to strengthen the interaction of multiscale information. Moreover, the Spatial Enhancement (SE) module is employed to integrate the contextual information of different scales. In addition, we introduce the Spatial Gradient Variation (SGV) unit in the Boundary Refined (BR) module to couple the segmentation task and boundary learning task, so that they can share latent high-level semantics and interact with each other, and combine this with the joint loss to refine the boundary. In our study, GaoFen-2 remote sensing images in Shouguang City, Shandong Province, China are selected to make the AG dataset. The experimental results show that HBRNet demonstrates a significant improvement in segmentation performance up to an IoU score of 94.89%, implying that this approach has advantages and potential for precise identification of AGs. [ABSTRACT FROM AUTHOR]

The innovation of germplasm resources and the continuous breeding of new varieties of apples (Malus domestica Borkh.) have yielded more than 8000 apple cultivars. The ability to identify apple cultivars with ease and accuracy can solve problems in apple breeding related to property rights protection to promote the healthy development of the global apple industry. However, the existing methods are inconsistent and time-consuming. This paper proposes an efficient and convenient method for the classification of apple cultivars using a deep convolutional neural network with leaf image input, which is the delicate symmetry of a human brain learning. The model was constructed using the TensorFlow framework and trained on a dataset of 12,435 leaf images for the identification of 14 apple cultivars. The proposed method achieved an overall accuracy of 0.9711 and could successfully avoid the over-fitting problem. Tests on an unknown independent testing set resulted in a mean accuracy, mean error, and variance of μ a c c = 0.9685 , μ ε = 0.0315 , and σ 2 = 1.89025 E − 4 , respectively, indicating that the generalization accuracy and stability of the model were very good. Finally, the classification performance for each cultivar was tested. The results show that model had an accuracy of 1.0000 for Ace, Hongrouyouxi, Jazz, and Honey Crisp cultivars, and only one leaf was incorrectly identified for 2001, Ada Red, Jonagold, and Gold Spur cultivars, with accuracies of 0.9787, 0.9800, 0.9773, and 0.9737, respectively. Jingning1 and Pinova cultivars were classified with the lowest accuracies, with 0.8780 and 0.8864, respectively. The results also show that the genetic relationship between cultivars Shoufu 3 and Yanfu 3 is very high, which is mainly because they were both selected from a red mutation of Fuji and bred in Yantai City, Shandong Province, China. Generally, this study indicates that the proposed deep learning model is a novel and improved solution for apple cultivar identification, with high generalization accuracy, stable convergence, and high specificity. [ABSTRACT FROM AUTHOR]