【摘要】 针对基于卫星影像的三维建模在建筑产业、城市规划、文化遗产保护、工业制造等领域中的应用问题，提出了一种基于深度学习的解决方案，着重探讨如何通过高效、迅速的方式生成单张卫星影像中的建筑模型。运用深度卷积网络模型提取建筑足迹，同时，引入了改进的道格拉斯-普克算法对建筑足迹进行规则化，并通过推算建筑高程和屋顶样式识别填充，对建筑模型进行完善。最终，通过综合上述参数输出，成功实现了在单张卫星影像中对建筑进行高效批量重建。更多还原

【Abstract】 This paper aims to address the application challenges of three-dimensional modeling based on satellite imagery in industries such as construction, urban planning, cultural heritage preservation, and industrial manufacturing. It focuses on exploring efficient and rapid methods for generating architectural models from single satellite images. The paper proposes a solution based on deep learning, utilizing a deep convolutional neural network model to extract building footprints. Simultaneously, it introduces an improved Douglas-Peucker algorithm for regularizing building footprints and enhances the architectural model by estimating building heights and identifying roof styles. Ultimately, by synthesizing the aforementioned parameters, the paper successfully achieves efficient and batch reconstruction of buildings from individual satellite images.更多还原