Large-scale LOD1 building extraction from a textured 3D mesh of a scene

Liuyun Duan; Michael Swaine; Sebastien Tripodi; Mohamed A. Cherif; Arno Gobbin; Nicolas Girard; Yuliya Tarabalka; Lionel Laurore

doi:10.1117/12.2679998

19 October 2023 Large-scale LOD1 building extraction from a textured 3D mesh of a scene

Liuyun Duan, Michael Swaine, Sebastien Tripodi, Mohamed A. Cherif, Arno Gobbin, Nicolas Girard, Yuliya Tarabalka, Lionel Laurore

Author Affiliations +

Proceedings Volume 12733, Image and Signal Processing for Remote Sensing XXIX; 127330E (2023) https://doi.org/10.1117/12.2679998
Event: SPIE Remote Sensing, 2023, Amsterdam, Netherlands

Abstract

We propose a novel deep learning approach which performs building semantic segmentation of large-scale textured 3D meshes, followed by a polygonal extraction of footprints and heights. Extracting accurate individual building structures poses a challenge due to the complexity and the variety of architecture and urban designs, where a single overhead image is not enough. Integrating elevation data from a 3D mesh allows to better distinguish individual buildings in three-dimensional space. Another advantage is to avoid occlusion issues in the case of oblique imagery, where tall buildings mask smaller buildings behind them in the case of non-nadir images (especially problematic in urban areas). The proposed method transforms the input data from a 3D textured mesh to a true orthorectified RGB image by rendering both the color information and the depth information from a virtual camera looking straight down. Depth information is then converted to a normalized DSM (nDSM) by subtracting the Copernicus GDEM v3 30-meter Digital Elevation Model (DEM). Viewing the 3D textured mesh as a four-band raster image (RGB + nDSM) allows us to use a very efficient fully convolutional neural network based on the U-net architecture for processing large-scale areas. The proposed method was evaluated on three urban areas in Brazil, America, and France. It allows a fourfold improvement in productivity for cartography of buildings in complex urban areas.

Conference Presentation

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

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Liuyun Duan, Michael Swaine, Sebastien Tripodi, Mohamed A. Cherif, Arno Gobbin, Nicolas Girard, Yuliya Tarabalka, and Lionel Laurore "Large-scale LOD1 building extraction from a textured 3D mesh of a scene", Proc. SPIE 12733, Image and Signal Processing for Remote Sensing XXIX, 127330E (19 October 2023); https://doi.org/10.1117/12.2679998

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