Landslide susceptibility mapping (LSM) is significant for disaster prevention and mitigation, land use management, and as a reference for decision-making. Convolutional neural networks (CNNs) in deep learning have been successfully applied to LSM studies and have been shown to improve the accuracy of LSM. Although optimizing the quality of negative samples at the input step of a deep learning model can improve the accuracy of the model, the risk of model overfitting may increase. In this study, an LSM method based on the Gaussian heatmap sampling technique and a CNN was developed from the perspective of LSM dataset sampling. A Gaussian heatmap sampling technique was used to enrich the variety of landslide inventory at the input step of the deep learning model to improve the accuracy of the LSM results. This sampling technique involved the construction of a landslide susceptibility Gaussian heatmap neural network model, LSGH-Net, by combining a CNN. A series of optimization strategies such as attention mechanism, dropout, etc., were applied to improve the model structure and training process. The results demonstrated that the proposed approach outperformed the benchmark CNN-based algorithm in terms of metrics (Accuracy = 95.30%, F1 score = 95.13%, and Sensitivity = 91.79%). The Gaussian heatmap sampling technique effectively improved the accuracy of frequency histograms of the landslide susceptibility index, which provided finer-grained mapping details and more reasonable landslide density. By analyzing Gaussian heatmap at different scales, the approach proposed in this paper is an important reference for different regions and other disaster susceptibility studies as well.

Building extraction based on remote sensing images has been widely used in many industries. However, state-of-the-art methods produce an incomplete segmentation of buildings owing to unstable multi-scale context aggregation and a lack of consideration of semantic boundaries, ultimately resulting in large uncertainties in predictions at building boundaries. In this study, efficient fine building extraction methods were explored, which demonstrated that the rational use of edge features can significantly improve building recognition performance. Herein, a fine building extraction network based on a multi-scale edge constraint (MEC-Net) was proposed, which integrates the multi-scale feature fusion advantages of UNet++ and fuses edge features with other learnable multi-scale features to achieve the effect of prior constraints. Attention was paid to the alleviation of noise interference in the edge features. At the data level, according to the improvement of copy-paste according to the characteristics of remote sensing imaging, a data augmentation method for buildings (build-building) was proposed, which increased the number and diversity of positive samples by simulating the construction of buildings to increase the generalization of MEC-Net. MEC-Net achieved 91.13%, 81.05% and 74.13% IoU on the WHU, Massachusetts and Inria datasets, and it has a good inference efficiency. The experimental results show that MEC-Net outperforms the state-of-the-art methods, demonstrating its superiority. MEC-Net improves the accuracy of building boundaries by rationally using previous edge features.

Extracting buildings from remote sensing imagery can serve many industries. Single-category building extraction, however, has been unable to meet the actual needs of sectors such as disaster prevention and mitigation. In this study, we create a large-scale high-quality multispectral dataset (BFE-Set) for fine-grained extraction of buildings. BFE-Set uses the GaoFen-2 multispectral satellite as the data source and divides buildings into three fine-grained categories according to their structure types: steel and reinforced concrete structure, masonry structure, and block stone structure. It includes 20 counties where disasters often occur in China, and contains nearly 260,000 building instances. We propose the BFE-Net, a fully convolutional neural network that performs better on the BFE-Set for fine-grained extraction. The styles of buildings in different regions are quite different. Through the combined use of batch normalization and instance normalization, BFE-Net enhances the model’s style invariance. BFE-Net extracts feature from visible light and near-infrared data through a parallel feature extraction sub-network and use an attention mechanism to fuse the two modal features. This takes full advantage of the characteristic complementary effects of visible light and near-infrared. In addition, knowledge distillation is used to fully mine the inter-class relationships of fine-grained buildings, making the model more generalized in fine-grained classification. The experimental results show that BFE-Net is superior to other building extraction methods, and the fine-grained extraction effect is encouraging, which can provide a valuable reference. BFE-Set and BFE-Net will play a greater role in industries such as disaster response and smart cities.

The undulating terrain in mountainous and hilly regions results in a greater variety and complexity of shadows. Efficient methods for shadow detection and reconstruction in high-resolution remote sensing images are particularly important in such hilly areas. The accurate detection of shadow masks is a prerequisite for shadow reconstruction. By utilizing the features of high hue and low intensity in shadow areas, an initial spectral ratio is constructed based on the CIELCh color space model. Simple linear iterative clustering is employed to perform superpixel segmentation on the image, and the segmented results are spatially constrained to reconstruct the initial spectral ratio. Afterwards, an automatic multi-level global thresholding approach is applied to obtain the shadow mask and eliminate the influence of interfering objects. For shadow reconstruction, the segmented superpixels are treated as the smallest processing units. Similar neighboring objects have similar ambient light intensities. Based on this, we propose a shadow reconstruction method, which compensates shadow superpixels using adjacent non-shadow superpixels and determines compensation weights based on their similarity. Furthermore, the shadow boundaries are dilated to obtain penumbra, and mean filtering is performed to compensate for the illumination in the penumbra. Finally, the proposed method is qualitatively and quantitatively compared with existing shadow detection and reconstruction methods. Experimental results demonstrate that this method can accurately detect shadows in high-resolution remote sensing images in mountainous and hilly environments, and effectively reconstruct the spectral information of shadow areas. This has significant implications for subsequent feature extraction and further analysis in mountainous and hilly regions.

Earthquake disasters are marked by their unpredictability and potential for extreme destructiveness. Accurate information on building damage, captured in post-earthquake remote sensing images, is critical for an effective post-disaster emergency response. The foundational features within these images are essential for the accurate extraction of building damage data following seismic events. Presently, the availability of publicly accessible datasets tailored specifically to earthquake-damaged buildings is limited, and existing collections of post-earthquake building damage characteristics are insufficient. To address this gap and foster research advancement in this domain, this paper introduces a new, large-scale, publicly available dataset named the Major Earthquake Damage Building Feature Set (MEDBFS). This dataset comprises image data sourced from five significant global earthquakes and captured by various optical remote sensing satellites, featuring diverse scale characteristics and multiple spatial resolutions. It includes over 7000 images of buildings pre- and post-disaster, each subjected to stringent quality control and expert validation. The images are categorized into three primary groups: intact/slightly damaged, severely damaged, and completely collapsed. This paper develops a comprehensive feature set encompassing five dimensions: spectral, texture, edge detection, building index, and temporal sequencing, resulting in 16 distinct classes of feature images. This dataset is poised to significantly enhance the capabilities for data-driven identification and analysis of earthquake-induced building damage, thereby supporting the advancement of scientific and technological efforts for emergency earthquake response.

Offshore aquaculture promotes the development of aquaculture industry and brings huge economic benefits to fishermen, while seriously affecting the near-coast environment. Accurate access to the range of offshore farms at home and abroad is of great significance for marine disaster warning and coastal management. Remote sensing is a very effective means of observing offshore farms. Offshore farms segmentation technology is more mature in high-resolution optical images. SAR images have the advantage of being available all day and all night. Using SAR images to extract offshore farms has become a recent research hotspot. Gaofen-3 and HISEA-1 satellites have high-resolution marine observation capabilities. Combined with their super observation capabilities, this article proposes a technical solution to extract offshore farms using Gaofen-3 and HISEA-1 images. First, the combine-crop component module is proposed to create a new SAR images dataset based the original dataset. Next, common data augmentation methods, such as flip, rotate, and copy–paste, are used, and the online multiscale resolution image expansion is proposed to enrich the dataset and enlarge the data volume. Finally, the U_EFF_NET model is proposed. The model uses encoder–decoder structure and the light-weight feature extraction network EfficientNet-b0 as backbone. The decoder embeds the attention mechanism. The optimization strategy uses joint loss function to achieve multitask learning, and the OHEM strategy is added to the joint loss function. The method proposed in this article has high accuracy and high practicality as the frequency-weight intersection ratio of the extracted results on the test set is 98.12%. And the inference time is at the forefront.