Aquaculture has important economic and environmental benefits. With the development of remote sensing and deep learning technology, coastline aquaculture extraction has achieved rapid, automated, and high-precision production. However, some problems still exist in extracting large-scale aquaculture based on high-resolution remote sensing images: (1) the generalization of large-scale models caused by the diversity of remote sensing in breeding areas; (2) the confusion of breeding target identification caused by the complex background interference of land and sea; (3) the boundary of the breeding area is difficult to extract accurately. In this paper, we built a comprehensive sample database based on the spatial distribution of aquaculture, and expanded the sample database by using confusing land objects as negative samples. A multi-scale-fusion superpixel segmentation optimization module is designed to solve the problem of inaccurate boundaries, and a coastal aquaculture network is proposed. Based on the coastline aquaculture dataset that we labelled and produced ourselves, we extracted cage culture areas and raft culture areas near the coastline of mainland China based on high-resolution remote sensing images. The overall accuracy reached 94.64% and achieved a state-of-the-art performance.

Deep learning (DL)-based architectures have shown a strong capacity to identify changes. However, existing change detection (CD) networks still suffer from limited applicability when it comes to multi-scale targets and spatially misaligned objects. For the sake of tackling the above problems, a region-based feature fusion network (RFNet) for CD of very high spatial resolution (VHR) remote sensing images is proposed. RFNet uses a fully convolutional Siamese network backbone where a multi-stage feature interaction module (MFIM) is embedded in the dual encoder and a series of region-based feature fusion modules (RFFMs) is used to generate change information. The MFIM fuses features in different stages to enhance the interaction of multi-scale information and help the network better distinguish complex ground objects. The RFFM is built based on region similarity (RSIM), which measures the similarity of bitemporal features with neighborhoods. The RFFM can reduce the impact of spatially offset bitemporal targets and accurately identify changes in bitemporal images. We also design a deep supervise strategy by directly introducing RSIM into loss calculation and shortening the error propagation distance. We validate RFNet with two popular CD datasets: the SECOND dataset and the WHU dataset. The qualitative and quantitative comparison results demonstrate the high capacity and strong robustness of RFNet. We also conduct robustness experiments and the results demonstrate that RFNet can deal with spatially shifted bitemporal images.

Accurate surface water mapping is crucial for rationalizing water resource utilization and maintaining ecosystem sustainability. However, the diverse shapes and scales of water bodies pose challenges in automatically extracting them from remote sensing images. Existing methods suffer from inaccurate lake boundary extraction, inconsistent results, and failure to detect small rivers. In this study, we propose a dual-stream parallel feature aggregation network to address these limitations. Our network effectively combines global information interaction from the Swin Transformer network with deep local information integration from Convolutional Neural Networks (CNNs). Moreover, we introduce a deformable convolution-based attention mechanism module (D-CBAM) that adaptively adjusts receptive field size and shape, highlights important channels in feature maps automatically, and enhances the expressive ability of our network. Additionally, we incorporate a Feature Pyramid Attention (FPA) module during the advanced coding stage for multi-scale feature learning to improve segmentation accuracy for small water bodies. To verify the effectiveness of our method, we chose the Yellow River Basin in China as the research area and used Sentinel-2 and Sentinel-1 satellite images as well as manually labelling samples to construct a dataset. On this dataset, our method achieves a 93.7% F1 score, which is a significant improvement compared with other methods. Finally, we use the proposed method to map the seasonal and permanent water bodies in the Yellow River Basin in 2021 and compare it with existing water bodies. The results show that our method has certain advantages in mapping large-scale water bodies, which not only ensures the overall integrity but also retains local details.

The identification of colored steel buildings in images is crucial for managing the construction sector, environmental protection, and sustainable urban development. Current deep learning methods for optical remote sensing images often encounter challenges such as confusion between the roof color or shape of regular buildings and colored steel structures. Additionally, common semantic segmentation networks exhibit poor generalization and inadequate boundary regularization when extracting colored steel buildings. To overcome these limitations, we utilized the metal detection and differentiation capabilities inherent in synthetic aperture radar (SAR) data to develop a network that integrates optical and SAR data. This network, employing a triple-input structure, effectively captures the unique features of colored steel buildings. We designed a multimodal hybrid attention module in the network that discerns the varying importance of each data source depending on the context. Additionally, a boundary refinement (BR) module was introduced to extract the boundaries of the colored steel buildings in a more regular manner, and a deep supervision strategy was implemented to improve the performance of the network in the colored steel building extraction task. A BR module and deep supervision strategy were also implemented to sharpen the extraction of building boundaries, thereby enhancing the network’s accuracy and adaptability. The results indicate that, compared to mainstream semantic segmentation, this method effectively enhances the precision of colored steel building detection, achieving an accuracy rate of 83.19%. This improvement marks a significant advancement in monitoring illegal constructions and supporting the sustainable development of the Beijing–Tianjin–Hebei metropolitan region.

Wastewater treatment plants (WWTPs) play a vital role in controlling wastewater discharge and promoting recycling. Accurate WWTP identification and spatial analysis are crucial for environmental protection, urban planning, and sustainable development. However, the diverse shapes and scales of WWTPs and their key facilities pose challenges for traditional detection methods. This study employs a Multi-Attention Network (MANet) for WWTP extraction, integrating channel and spatial feature attention. Additionally, a Global-Local Feature Modeling Network (GLFMN) is introduced to segment key facilities, specifically sedimentation and secondary sedimentation tanks. The approach is applied to Beijing, utilizing geographic data such as WWTP locations, treatment capacities, and surrounding residential and water distributions. Results indicate that MANet achieves 80.1% accuracy with a 90.4% recall rate, while GLFMN significantly improves the extraction of key facilities compared to traditional methods. The spatial analysis reveals WWTP distribution characteristics, offering insights into treatment capacity and geographic influences. These findings contribute to emission regulation, water quality supervision, and enterprise management of WWTPs in Beijing. This research provides a valuable reference for optimizing wastewater treatment infrastructure and supports decision-making in environmental governance and sustainable urban development.

Landcover change can reflect changes in the natural environment and the impact of human activities. Remotely sensed big data with large-scale and multi-temporal key characteristics provide the data support for landcover change information extraction. The development of deep learning provides technical method support for information extraction from remotely sensed big data. However, the current mainstream deep learning change detection methods only establish the changing relationship between two phases of images. They cannot directly extract the ground object categories before and after the change. It is easily affected by pseudo-changes caused by the color difference of multi-temporal images, resulting in many false detections. In this paper, we propose a prior semantic network and a difference enhancement block module to establish prior guidance and constraints on changing features to solve the pseudo-change problem. We propose a semantic-change integrated single-task network, which can simultaneously extract multi-temporal landcover classification and landcover change. On the self-made, large-scale multi-temporal Landsat dataset, we have performed multi-temporal landcover change information extraction, reaching an overall accuracy of 83.1% and achieving state-of-the-art performance. Finally, we thoroughly analyzed the landcover change results in the study area from 2005 to 2020.