电力行业（水力发电、风力发电）、水利行业工程设计，工程勘察综合类，工程咨询，工程总承包，水文、水资源调查评价，编制开发建设项目水土保持方案，建设项目水资源论证，建筑工程设计，电力行业（变电）、市政公用行业（燃气除外）工程设计，建设项目环境影响评价，专项工程设计：废水、固废治理工程，建设工程基桩质量检测：静载检测、低应变动力检测，电力行业（送电）工程设计，水利水电建设工程蓄水安全鉴定，地质灾害治理工程勘察；地质灾害危险性评估；地质灾害治理工程设计；生产建设项目水土保持监测；工程测量；承包与其实力、规模、业绩相适应的国外工程项目，对外派遣实施上述境外工程所需的劳务人员。（依法须经批准的项目，经相关部门批准后方可开展经营活动）
Highly accurate water body extraction can be helpful for water resources monitoring and management.The current methods of water body extraction based on remote sensing images lack attention to boundary quality,resulting in inaccurate boundary delineation and low detail retention.To improve the boundary and detail accuracy of water body extraction for remote sensing images,this paper proposes a deep learning algorithm for water body extraction from high-resolution remote sensing images based on multi-scale feature fusion.The model includes a hierarchical feature extraction module and a stacked-connected decoder module that fuses multi-scale features.In the hierarchical feature extraction module,a channel attention structure is introduced for integrating shape,texture,and hue information of water bodies in high-resolution remote sensing images to better understand the shapes and boundaries of water bodies.In the stacked-connected decoder module that incorporates multi-scale features,the stacked connection of multi-level semantic information and enhanced feature extraction are performed.Meanwhile,broad background information and fine detail information are captured to achieve better water body extraction results.Experimental results on both self-annotated and publicly available datasets show that the model yields 98.37%and 91.23%accuracy,and extracts more complete edges of water bodies while retaining more details than existing semantic segmentation models.The proposed model improves the accuracy and generalization ability of water body extraction and provides references for water body extraction from high-resolution remote sensing images.
