2 February 2009 Fusion of LIDAR and aerial imagery for accurate building footprint extraction
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Abstract
Building footprint extraction from GIS imagery/data has been shown to be extremely useful in various urban planning and modeling applications. Unfortunately, existing methods for creating these footprints are often highly manual and rely largely on architectural blueprints or skilled modelers. Although there has been quite a lot of research in this area, most of the resultant algorithms either remain unsuccessful or still require human intervention, thus making them infeasible for practical large-scale image processing systems. In this work, we present novel LiDAR and aerial image processing and fusion algorithms to achieve fully automated and highly accurate extraction of building footprint. The proposed algorithm starts with initial building footprint extraction from LiDAR point cloud based on an iterative morphological filtering approach. This initial segmentation result, while indicating locations of buildings with a reasonable accuracy, may however produce inaccurate building footprints due to the low resolution of the LiDAR data. As a refinement process, we fuse LiDAR data and the corresponding color aerial imagery to enhance the accuracy of building footprints. This is achieved by first generating a combined gradient surface and then applying the watershed algorithm initialized by the LiDAR segmentation to find ridge lines on the surface. The proposed algorithms for automated building footprint extraction have been implemented and tested using ten overlapping LiDAR and aerial image datasets, in which more than 300 buildings of various sizes and shape exist. The experimental results confirm the efficiency and effectiveness of our fully automated building footprint extraction algorithm.
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Sakina Zabuawala, Sakina Zabuawala, Hieu Nguyen, Hieu Nguyen, Hai Wei, Hai Wei, Jacob Yadegar, Jacob Yadegar, } "Fusion of LIDAR and aerial imagery for accurate building footprint extraction", Proc. SPIE 7251, Image Processing: Machine Vision Applications II, 72510Z (2 February 2009); doi: 10.1117/12.806141; https://doi.org/10.1117/12.806141
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