Non-agriculturalization incidents are serious threats to local agricultural ecosystem and global food security. Remote sensing change detection (CD) can provide an effective approach for in-time detection and prevention of such incidents. However, existing CD methods are difficult to deal with the large intra-class differences of cropland changes in high-resolution images (HRIs). In addition, traditional CNN based models are plagued by the loss of long-range context information, and the high computational complexity brought by deep layers. Therefore, we propose a CNNtransformer network with multi-scale context aggregation (MSCANet), which combines the merits of CNN and transformer to fulfill efficient and effective cropland change detection. In the MSCANet, a CNN-based feature extractor is first utilized to capture hierarchical features, then a transformer-based multiscale context aggregator (MSCA) is designed to encode and aggregate context information. Finally, a multi-branch prediction head (MBPH) with three CNN classifiers is applied to obtain change maps, to enhance the supervision for deep layers. Besides, for the lack of change detection dataset with fine-grained cropland change of interest, we also provide a new cropland change detection dataset (CLCD), which contains 600 pairs of 512×512 bitemporal images with the spatial resolution of 0.5-2m. Comparative experiments with several CD models prove the effectiveness of the MSCANet, with the highest F1 of 64.67% on the high-resolution semantic change detection dataset (HRSCD), and of 71.29% on CLCD. Code and dataset in the paper will be available for download from the following link https://github.com/liumency/CropLand-CD.
As high temperature and heat wave have become great threats to human survival, social stability, and ecological safety, it is of great significance to master the spatial and temporal dynamic changes of temperature to prevent high temperature and heat wave risks. The meteorological station can provide accurate near ground temperature, but only within a specific space and time. In order to meet the needs of large-scale research, spatial interpolation methods were widely used to obtain spatially continuous temperature maps. However, these methods often ignore the influence of external factors on temperature, such as land cover, height, etc., and neglect to supplement temporal-wise information. To deal with these issues, a joint spatio-temporal method is proposed to obtain dense temperature mapping from multi-source remote sensing data, which combining a geographically weighted regression (GWR) model and a polynomial fitting model. Besides, a heat wave risk model is also built based on the dense temperature maps and population data, in order to evaluate the heat wave risk of different areas. Accuracy evaluations and experiments have verified the effectiveness of the proposed methods. Case study on the four cities of Zhejiang Province, China have demonstrated that areas with higher degree of urbanization are often accompanied by higher heat wave risks, such as the northern part of the study area. The study also found that the heat wave risks have presented a centralized distribution and spatial autocorrelation characteristics in the study area.
As the basic unit of farmland, parcel is crucial for remote sensing tasks, such as urban management. Previous studies of farmland parcels extraction are based on boundary detection and instance segmentation methods. However, these methods perform poorly in the parcels with complex shape and fuzzy boundary due to the insufficient feature extraction capability. Moreover, for the lack of multi-scale features extraction and fusion, they are difficult to extract different scale farmland parcels accurately. Based on these issues, we propose a Fuzzy-Boundary Enhanced Trident Network, named FBETNet, to enhance the feature of fuzzy boundary and generate multi-scale parcels. First, a semantic-guided multi-task strategy is introduced in order to enhance the feature of fuzzy boundary. Second, we design a multiscale trident module to further improve the performance of multiscale feature extraction. Finally, a adversarial data augmentation strategy is employed in the training phase to strengthen the robustness and stability of out proposed method. Experiments show that our proposed method improves significantly in both accuracy and visualization, especially for the parcels with fuzzy boundary and complex shape.
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