A building’s window-to-wall ratio (WWR) has critical influence on heat loss, solar gain, and daylighting levels, with implications for visual and thermal comfort as well as energy performance. However, in contrast to characteristics such as floor area, existing building WWRs are rarely available. In this work we present a machine learning based approach to parse windows from drone images and estimate the WWR. Our approach is based on firstly extracting the building 3D geometry from drone images, secondly performing semantic segmentation to detect windows and finally computing the WWR. Experiments show that our approach is effective in estimating WWR from drone images.
3D Building Reconstruction is an important problem with applications in urban planning, emergency response, and disaster planning. This paper presents a new pipeline for 3D reconstruction of buildings from RGB imagery captured via a drone. We leverage the commercial software Pix4D to construct a 3D point cloud from RGB drone imagery, which is then used in conjunction with image processing and geometric methods to extract a building footprint. The footprint is then extruded vertically based on the heights of the segmented rooftops. The footprint extraction involves two main steps, line segment detection and polygonization of the lines. To detect line segments, we project the point cloud onto a regular grid, detect preliminary lines using the Hough transform, refine them via RANSAC, and convert them into line segments by checking the density of the points surrounding the line. In the polygonization step, we convert detected line segments into polygons by constructing and completing partial polygons, and then filter them by checking for support in the point cloud. The polygons are then merged based on their respective height profiles. We have tested our system on two buildings of several thousand square feet in Alameda, CA, and obtained an F1 score of 0.93 and 0.95 respectively as compared to the ground truth.
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