基于深度学习的多源空间数据融合技术在实景三维建模中的应用研究

Research on the Application of Multi-source Spatial Data Fusion Technology Based on Deep Learning in Realistic 3D Modeling

随着城市化进程的加快,实景三维建模在城市管理、环境监测和基础设施管理等领域的应用日益广泛。深度学习技术支撑下,多源空间数据的融合,为实景三维建模带来了创新的解决方案。文中首先对多源空间数据的特性进行了详细分析,并探讨了其在三维建模领域中的应用需求。随后,重点讨论了深度学习技术在数据处理、特征抽取以及模型改进等方面的显著优势。探讨了多种深度学习模型在处理多源数据融合任务中的具体应用,并通过具体案例分析,展示了这些模型在实景三维建模领域的有效性和明显优势。最后,针对目前技术应用所面临的难题,文章提出了具体的优化措施及未来的研究路径。

With the acceleration of urbanization,the application of realistic 3D modeling in urban management,environmental monitoring,and infrastructure management is becoming increasingly widespread.Supported by deep learning technology,the fusion of multi-source spatial data has brought innovative solutions for realistic 3D modeling.The article first provides a detailed analysis of the characteristics of multi-source spatial data and explores its application requirements in the field of 3D modeling.Subsequently,the significant advantages of deep learning technology in data processing,feature extraction,and model improvement were discussed in detail.Explored the specific applications of various deep learning models in processing multi-source data fusion tasks,and demonstrated the effectiveness and obvious advantages of these models in the field of realistic 3D modeling through specific case analysis.Finally,in response to the challenges currently faced by technological applications,the article proposes specific optimization measures and future research paths.