Application of deep learning in natural wndifions of well areas
With the increasing penetration of Geographic Information Systems(GIS)across various domains,automated geoinformation processing techniques Have become paramount.This paper presents a novel automated survey method for natural conditions in well areas,integrating deep learning technologies with GIS.By leveraging advanced deep learning models such as U-Net and the Segment Anything model(SAM),we efficiently recognize and annotate critical geographical features,including buildings,water bodies,and roads,from High-resolution orthophotos.Experimental results indicate that our approach achieves an accuracy rate ranging from 95%to 100%in comparison to traditional manual annotation methods,significantly enhancing annotation efficiency and reducing time costs.Future research directions encompass enhancing the model's adaptability under diverse environmental conditions,bolstering real-time data processing capabilities,and improving user interaction experiences.This research contributes novel insights into the application of GIS automation in geospatial data analysis,holding significant implications for advancing related technologies.
Geographic Information System(GIS)Deep LearningAutomated Annotationnatural condition Survey in Well AreasU-Net
NETL
NSTL
万方数据
维普
