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In recent years, high resolution (HR) remote sensing images have brought significant changes to land cover monitoring. Monitoring water resources, a vital and scarce commodity for human survival, is a crucial aspect of land cover monitoring and forms the foundation for water resource allocation in regions facing shortages. Hence, a universal method is necessary to address this problem and handle large-scale water resources monitoring. We explore the network structure via improving the interpretability to reduce the network layers and propose a lightweight pixel-wise semantic segmentation model, which achieves waterbody extraction from GF-1 remote sensing images with high accuracy and high speed. The proposed model structure addresses both semantic segmentation for large and small waterbodies, extracting fine-grained edge and contour information from high-resolution feature maps. Additionally, it efficiently extracts multi-scale high-level semantic information using residual convolutional blocks and dilated convolutional blocks. The multi-scale feature maps are fused, and binary classification is predicted through Support Vector Machines (SVM). Furthermore, the paper introduces a model training method with an adaptive learning rate, reducing the overall training time. To validate the model's performance, remote sensing images from GF-1 are utilized to construct a dataset. Experimental results, compared with five models, demonstrate that the proposed method achieves the highest accuracy and least training time. [ABSTRACT FROM AUTHOR]

To better understand how contour-levee irrigation practice impacts water resources for formulating effective water management policies, it is important to obtain its application on large-scale data sets, e.g. state-wide. Automatic classification of contour levee croplands from high-resolution aerial images is of great potential given the success of deep neural networks and the availability of high-resolution remote sensing imagery. This paper proposes a gradient CNN model to classify fields with contour levees from remote sensing images. Our model produces high-quality segmentation masks that are refined with superpixel-based segmentation post-processing. Our method is evaluated using images by the National Agriculture Imagery Program (NAIP) for the counties in Arkansas. A comparison with the state-of-the-art methods demonstrates the improved performance of our proposed method. Our method demonstrates superior performance in the classification of challenging cases and achieves an overall 3.08% of accuracy improvement and 28.57% BER error reduction, compared to the second-best method. The p-value with respect to the second-best method is 0.005, which indicates great statistical significance. In addition, the results for data of different counties demonstrate the exceptional generalization ability of our method. [ABSTRACT FROM AUTHOR]