Research on Building Change Detection Technology Based on Deep Learning
[Purposes]In response to the demand for dynamic monitoring of buildings,a deep learning based rapid change monitoring technology route for buildings is proposed by comprehensively analyzing the application cases of deep learning technology in change discovery.[Methods]Based on multi-source and multi-temporal remote sensing images and thematic data such as geographical conditions monitor-ing,the building change samples are labeled and the building change detection sample set is established.[Findings]The study area is selected,and the building change detection algorithm model is optimize and use to extract building change information with an accuracy of 79.07%and a recall rate of 74.60%.[Con-clusions]The results indicate that the change detection technology based on deep learning has a rela-tively ideal effect and can significantly improve efficiency,providing data and technical support for natu-ral resource investigation and monitoring,urban planning,and physical examinations.
万方数据
维普
