The timely and accurate detection of wheat lodging at a large scale is necessary for loss assessments in agricultural insurance claims. Most existing deep-learning-based methods of wheat lodging detection use data from unmanned aerial vehicles, rendering monitoring wheat lodging at a large scale difficult. Meanwhile, the edge feature is not accurately extracted. In this study, a semantic segmentation network model called the pyramid transposed convolution network (PTCNet) was proposed for large-scale wheat lodging extraction and detection using GaoFen-2 satellite images with high spatial resolutions. Multi-scale high-level features were combined with low-level features to improve the segmentation’s accuracy and to enhance the extraction sensitivity of wheat lodging areas in the proposed model. In addition, four types of vegetation indices and three types of edge features were added into the network and compared to the increment in the segmentation’s accuracy. The F1 score and the intersection over union of wheat lodging extraction reached 85.31% and 74.38% by PTCNet, respectively, outperforming other compared benchmarks, i.e., SegNet, PSPNet, FPN, and DeepLabv3+ networks. PTCNet can achieve accurate and large-scale extraction of wheat lodging, which is significant in the fields of loss assessment and agricultural insurance claims.

It is crucial to detect and classify industrial heat sources for sustainable industrial development. Sustainable Development Science Satellite 1 (SDGSAT-1) thermal infrared spectrometer (TIS) data were first introduced for detecting industrial heat source production areas to address the difficulty in identifying factories with low combustion temperatures and small scales. In this study, a new industrial heat source identification and classification model using SDGSAT-1 TIS and Landsat 8/9 Operational Land Imager (OLI) data was proposed to improve the accuracy and granularity of industrial heat source recognition. First, multiple features (thermal and optical features) were extracted using SDGSAT-1 TIS and Landsat 8/9 OLI data. Second, an industrial heat source identification model based on a support vector machine (SVM) and multiple features was constructed. Then, industrial heat sources were generated and verified based on the topological correlation between the identification results of the production areas and Google Earth images. Finally, the industrial heat sources were classified into six categories based on point-of-interest (POI) data. The new model was applied to the Beijing–Tianjin–Hebei (BTH) region of China. The results showed the following: (1) Multiple features enhance the differentiation and identification accuracy between industrial heat source production areas and the background. (2) Compared to active-fire-point (ACF) data (375 m) and Landsat 8/9 thermal infrared sensor (TIRS) data (100 m), nighttime SDGSAT-1 TIS data (30 m) facilitate the more accurate detection of industrial heat source production areas. (3) Greater than 2~6 times more industrial heat sources were detected in the BTH region using our model than were reported by Ma and Liu. Some industrial heat sources with low heat emissions and small areas (53 thermal power plants) were detected for the first time using TIS data. (4) The production areas of cement plants exhibited the highest brightness temperatures, reaching 301.78 K, while thermal power plants exhibited the lowest brightness temperatures, averaging 277.31 K. The production areas and operational statuses of factories could be more accurately identified and monitored with the proposed approach than with previous methods. A new way to estimate the thermal and air pollution emissions of industrial enterprises is presented.

With rapid urbanization, retrieving information about residential complexes in a timely manner is essential for urban planning. To develop efficiency and accuracy of building extraction in residential complexes, a Segment Anything Model-based residential building instance segmentation method with an automated prompt generator was proposed combining LiDAR data and VHR remote sensing images in this study. Three key steps are included in this method: approximate footprint detection using LiDAR data, automatic prompt generation for the SAM, and residential building footprint extraction. By applying this method, residential building footprints were extracted in Pukou District, Nanjing, Jiangsu Province. Based on this, a comprehensive assessment model was constructed to systematically evaluate the spatial layout of urban complexes using six dimensions of assessment indicators. The results showed the following: (1) The proposed method was used to effectively extract residential building footprints. (2) The residential complexes in the study area were classified into four levels. The numbers of complexes classified as Excellent, Good, Average, and Poor were 10, 29, 16, and 1, respectively. Residential complexes of different levels exhibited varying spatial layouts and building distributions. The results provide a visual representation of the spatial distribution of residential complexes that belong to different levels within the study area, aiding in urban planning.

Industrial heat sources (IHSs) are key contributors to anthropogenic heat, air pollution, and carbon emissions. Accurately and automatically detecting their production areas (IHSPAs) on a large scale is vital for environmental monitoring and decision making, yet this is challenged by the lack of high-resolution thermal data. Sustainable Development Science Satellite 1 (SDGSAT-1) thermal infrared spectrometer (TIS) data with the highest resolution (30 m) in the civilian field and a three-band advantage were first introduced to detect IHSPAs. In this study, an IHSPA identification model using multi-features extracted from SDGSAT-1 TIS and Landsat OLI data and support vector machine (SVM) was proposed. First, three brightness temperatures and four thermal radiation indices using SDGSAT-1 TIS and Landsat OLI data were designed to enlarge the temperature difference between IHSPAs and the background. Then, 10 features combined with three indices from Landsat OLI images with the same spatial resolution (30 m) and stable data were extracted. Second, an IHSPA identification model based on SVM and multi-feature extraction was constructed to identify IHSPAs. Finally, the IHS objects were manually delineated and verified using the identified IHSPAs and Google Earth images. Some conclusions were obtained from different comparisons in Wuhai, China: (1) IHSPA identification based on SVM using thermal and optical features can detect IHSPAs and obtain the best results compared with different features and identification models. (2) The importance of using thermal features from the SDGSAT-1 TIS to detect IHSPAs was demonstrated by different importance analysis methods. (3) Our proposed method can detect more IHSs, with greater spatial coverage and smaller areas, compared with the methods of Ma and Liu. This new way to detect IHSPAs can obtain higher-spatial-resolution emissions of IHSs on a large scale and help decision makers target environmental monitoring, management, and decision making in industrial plant processing.

Clouds in optical remote sensing images are an unavoidable existence that greatly affect the utilization of these images. Therefore, accurate and effective cloud detection is an indispensable step in image preprocessing. To date, most researchers have tried to use deep-learning methods for cloud detection. However, these studies generally use computer vision technology to improve the performances of the models, without considering the unique spectral feature information in remote sensing images. Moreover, due to the complex and changeable shapes of clouds, accurate cloud-edge detection is also a difficult problem. In order to solve these problems, we propose a deep-learning cloud detection network that uses the haze-optimized transformation (HOT) index and the edge feature extraction module for optical remote sensing images (CD_HIEFNet). In our model, the HOT index feature image is used to add the unique spectral feature information from clouds into the network for accurate detection, and the edge feature extraction (EFE) module is employed to refine cloud edges. In addition, we use ConvNeXt as the backbone network, and we improved the decoder to enhance the details of the detection results. We validated CD_HIEFNet using the Landsat-8 (L8) Biome dataset and compared it with the Fmask, FCN8s, U-Net, SegNet, DeepLabv3+ and CloudNet methods. The experimental results showed that our model has excellent performance, even in complex cloud scenarios. Moreover, according to the extended experimental results for the other L8 dataset and the Gaofen-1 data, CD_HIEFNet has strong performance in terms of robustness and generalization, thus helping to provide new ideas for cloud detection-related work.