深度学习技术在井区自然状况调查中的应用
Application of deep learning in natural wndifions of well areas
黄冬 1黎翔 1杨棚程 1任晟宏 1龙曦1
作者信息
- 1. 四川科宏石油天然气工程有限公司 四川 成都 610000
- 折叠
摘要
随着地理信息系统(GIS)在各领域应用的深入,自动化的地理信息处理技术变得尤为重要.提出了一种结合深度学习技术与GIS的新型自动化井区自然状况调查方法.利用U形网络模型(U-Net)和分割任意物体模型(SAM)等先进的深度学习模型,实现了高分辨率正射影像中房屋、水系和道路等关键地理要素的高效识别和标注.实验结果显示,与传统手工标注方法相比,在识别准确率上达到95%～100%,大幅提升了标注效率并减少了时间成本.未来的研究方向包括提高模型在不同环境条件下的适应性,增强实时数据处理能力,以及改进用户交互体验.研究成果为GIS自动化处理技术在地理空间数据分析领域的应用提供了新思路,对推动相关技术的发展具有重要意义.
Abstract
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.
关键词
地理信息系统/深度学习/自动化标注/井区自然状况调查/U形网络模型Key words
Geographic Information System(GIS)/Deep Learning/Automated Annotation/natural condition Survey in Well Areas/U-Net引用本文复制引用
出版年
2024
NETL
NSTL
维普
万方数据