(2) The root mean square between the extracted building height and the reference building height is 5.41 m, and the mean absolute error is 3.39 m. The results are summarized as follows: (1) In terms of building footprint extraction, our method can achieve intersection-over-union indicators of 89.31% and 80.27% for the WHU Dataset and GF-7 self-annotated datasets, respectively these values are higher than the results of other models. We select Beijing as the study area to test the proposed method, and in order to verify the building extraction ability of MSAU-Net, we choose GF-7 self-annotated building dataset and a public dataset (WuHan University (WHU) Building Dataset) for model testing, while the accuracy is evaluated in detail through comparison with other models. Finally, the building height is extracted from the nDSM and combined with the results of building footprints to obtain building 3D information. Then, we generate the point cloud from the multi-view image and construct normalized digital surface model (nDSM) to represent the height of off-terrain objects. First, we propose a multi-stage attention U-Net (MSAU-Net) architecture for building footprint extraction from multi-spectral images. This article establishes an automated method for extracting building footprints and height information from GF-7 satellite imagery. The GaoFen-7 (GF-7) stereo-mapping satellite can provide multi-view and multi-spectral satellite images, which can clearly describe the fine spatial details within urban areas, while the feasibility of extracting building 3D information from GF-7 image remains understudied. The three-dimensional (3D) information of buildings can describe the horizontal and vertical development of a city.

3d satellite view