Change detection extracts change areas in bitemporal remote sensing images, and plays an important role in urban construction and coordination. However, due to image offsets and brightness differences in bitemporal remote sensing images, traditional change detection algorithms often have reduced applicability and accuracy. The development of deep learning-based algorithms has improved their applicability and accuracy; however, existing models use either convolutions or transformers in the feature encoding stage. During feature extraction, local fine features and global features in images cannot always be obtained simultaneously. To address these issues, we propose a novel end-to-end change detection network (EGCTNet) with a fusion encoder (FE) that combines convolutional neural network (CNN) and transformer features. An intermediate decoder (IMD) eliminates global noise introduced during the encoding stage. We noted that ground objects have clearer semantic information and improved edge features. Therefore, we propose an edge detection branch (EDB) that uses object edges to guide mask features. We conducted extensive experiments on the LEVIR-CD and WHU-CD datasets, and EGCTNet exhibits good performance in detecting small and large building objects. On the LEVIR-CD dataset, we obtain F1 and IoU scores of 0.9008 and 0.8295. On the WHU-CD dataset, we obtain F1 and IoU scores of 0.9070 and 0.8298. Experimental results show that our model outperforms several previous change detection methods.

Automatically extracting 2D buildings from high-resolution remote sensing images is among the most popular research directions in the area of remote sensing information extraction. Semantic segmentation based on a CNN or transformer has greatly improved building extraction accuracy. A CNN is good at local feature extraction, but its ability to acquire global features is poor, which can lead to incorrect and missed detection of buildings. The advantage of transformer models lies in their global receptive field, but they do not perform well in extracting local features, resulting in poor local detail for building extraction. We propose a CNN-based and transformer-based dual-stream feature extraction network (DSFENet) in this paper, for accurate building extraction. In the encoder, convolution extracts the local features for buildings, and the transformer realizes the global representation of the buildings. The effective combination of local and global features greatly enhances the network’s feature extraction ability. We validated the capability of DSFENet on the Google Image dataset and the ISPRS Vaihingen dataset. DSEFNet achieved the best accuracy performance compared to other state-of-the-art models.

The field of remote sensing information processing places significant research emphasis on object detection (OD) in high-spatial-resolution remote sensing images (HSRIs). The OD task in HSRIs poses additional challenges compared to conventional natural images. These challenges include variations in object scales, complex backgrounds, dense arrangement, and uncertain orientations. These factors contribute to the increased difficulty of OD in HSRIs as compared to conventional images. To tackle the aforementioned challenges, this paper introduces an innovative OD algorithm that builds upon enhancements made to the YOLOv5 framework. The incorporation of RepConv, Transformer Encoder, and BiFPN modules into the original YOLOv5 network leads to improved detection accuracy, particularly for objects of varying scales. The C3GAM module is designed by introducing the GAM attention mechanism to address the interference caused by complex background regions. To achieve precise localization of densely arranged objects, the SIoU loss function is integrated into YOLOv5. The circular smooth label method is used to detect objects with uncertain directions. The effectiveness of the suggested algorithm is confirmed through its application to two commonly utilized datasets, specifically HRSC2016 and UCAS-AOD. The average detection accuracies achieved on these datasets are 90.29% and 90.06% respectively, surpassing the performance of other compared OD algorithms for HSRIs.

Sustainable urban renewal is an important approach to achieving high-quality urban development. The elements of megacities are diverse, and their structures are complex. It is critical to carry out the scientific classification of grassroots governance units based on the concept and needs of urban renewal to promote targeted sustainability evaluation and achieve the precise application of renewal design and planning. This study takes the jurisdiction of Chengdu City as an example and constructs a hierarchical dimension composite classification. For this classification, 128 grassroots governance units are divided into nine types, according to their obvious spatial differences. Based on the properties of these types, suggestions for evaluating and implementing urban renewal are proposed: (1) high-density central areas generally face the dilemma of complex and rigid needs and administrative weaknesses, so the development of public participatory governance is an urgent issue; (2) in transitional suburban zones, areas on and between the development axes are significantly different, indicating that extra attention should be paid to the fairness of the renewal of semi-urbanized areas; (3) outer areas are generally marginalized in urban renewal processes and destructive redevelopment behaviors should be avoided.

Monitoring agricultural abandonment is essential in understanding the effects on the environment and food security. Polarimetric synthetic aperture radar (PolSAR) is an efficient approach for the monitoring of large-scale agricultural land cover in cloudy and rainy areas. However, previous studies have not taken advantage of the valuable phase information and not fully utilized the spatiotemporal features of farmland parcels, which has seriously limited the abandoned land identification accuracy. In this study, we developed a new method for the mapping of abandoned land based on the spatiotemporal features from PolSAR Single Look Complex (SLC) images via deep learning methods. First, backscattering coefficients (σ⁰VV, σ⁰VH) were derived, and the polarimetric parameters (entropy, anisotropy and mean alpha angle) were obtained based on Cloude–Pottier polarimetric decomposition. Then, the VGG16 deep convolutional network was innovatively used to extract spatial features from both the backscattering coefficients and polarimetric parameters. Next, the separability index was calculated to select the most effective spatial features. Finally, LSTM classifications were conducted based on the time series of backscattering features, the polarimetric parameters, the extracted spatial features and their combinations. The results showed that the introduction of multitemporal polarimetric parameters and spatial features both led to an improvement in the abandoned land identification accuracy. The combination of backscattering features, polarimetric parameters and spatial features yielded the best performance in identifying abandoned land, with producer’s accuracy of 88.29% and user’s accuracy of 84.03%. This study demonstrated the potential of polarimetric parameters and validated the effectiveness of spatiotemporal features in abandoned land identification. It provided a practical method for the production of a highly reliable abandoned land mapping in cloudy and rainy areas.

Building segmentation has extensive research value and application prospects in high-resolution remote sensing image (HRSI) processing. However, complex architectural contexts, varied building morphologies, and non-building occlusions make building segmentation challenging. Compared with traditional methods, deep learning-based methods present certain advantages in terms of accuracy and intelligence. At present, the most popular option is to first apply a single neural network to encode an HRSI, then perform a decoding process through up-sampling or using a transposed convolution operation, and then finally obtain the segmented building image with the help of a loss function. Although effective, this approach not only tends to lead to a loss of detail information, but also fails to fully utilize the contextual features. As an alternative, we propose a novel network called NPSFF-Net. First, using an improved pseudo-Siamese network composed of ResNet-34 and ResNet-50, two sets of deep semantic features of buildings are extracted with the support of transfer learning, and four encoded features at different scales are obtained after fusion. Then, information from the deepest encoded feature is enriched using a feature enhancement module, and the resolutions are recovered via the operations of skip connections and transposed convolutions. Finally, the discriminative features of buildings are obtained using the designed feature fusion algorithm, and the optimal segmentation model is obtained by fitting a cross-entropy loss function. Our method obtained intersection-over-union values of 89.45% for the Aerial Imagery Dataset, 71.88% for the Massachusetts Buildings Dataset, and 68.72% for the Satellite Dataset I.