Paper
21 September 2023 Optimization of extracted building footprints from UAV images
Author Affiliations +
Proceedings Volume 12786, Ninth International Conference on Remote Sensing and Geoinformation of the Environment (RSCy2023); 127860T (2023) https://doi.org/10.1117/12.2681315
Event: Ninth International Conference on Remote Sensing and Geoinformation of the Environment (RSCy2023), 2023, Ayia Napa, Cyprus
Abstract
Building footprints is important information in many types of applications, including optimization of rescuer response in case of catastrophic events, urban planning, urban dynamic monitoring, 3D building modeling etc. Traditionally, in remote sensing, building footprints are detected from very high-resolution images or point clouds. Convolution Neural Network (CNN) based semantic image segmentation model has become a common way to extract buildings footprints from remote sensing data with high accuracy regardless of differences in landscapes, shapes, texture, and used materials. However, the results of extraction usually represent rooftop outlines with overhangs rather than true building footprints. This paper presents the methodology for the optimization of building footprints by using contour information, which is derived from the UAV point cloud. First, the CNN model was used to extract rooftops from high-resolution UAV-based orthophoto. After that, the cross-section of the mesh model was performed in order to detect the outline of the building. The optimum height of the mesh cross section was derived based on an analysis of the Digital Elevation Model and Digital Surface Model. The generated results were compared with Open Street Map (OSM) and reference cadastral datasets. Quantitative and qualitative evaluations show that the proposed methodology can significantly improve the accuracy of CNN-extracted building footprints (and OSM data) compared to cadastral data. In addition, the high of buildings is simultaneously derived. Therefore, our approach opens up the possibility to use the full potential of UAV products for generating accurate building footprints and 3D building models of LoD1 with compatible accuracy as cadastral.
© (2023) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Miloš Tutnjević, Miro Govedarica, and Gordana Jakovljević "Optimization of extracted building footprints from UAV images", Proc. SPIE 12786, Ninth International Conference on Remote Sensing and Geoinformation of the Environment (RSCy2023), 127860T (21 September 2023); https://doi.org/10.1117/12.2681315
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KEYWORDS
3D modeling

Unmanned aerial vehicles

Point clouds

RGB color model

Data modeling

Atomic force microscopy

Remote sensing

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