Optimization of extracted building footprints from UAV images

Miloš Tutnjević; Miro Govedarica; Gordana Jakovljević

doi:10.1117/12.2681315

21 September 2023 Optimization of extracted building footprints from UAV images

Miloš Tutnjević, Miro Govedarica, Gordana Jakovljević

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.

Citation Download Citation

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

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available

Members: $17.00

Non-members: $21.00 ADD TO CART

PROCEEDINGS
10 PAGES

DOWNLOAD PAPER SAVE TO MY LIBRARY

GET CITATION

RIGHTS & PERMISSIONS

Get copyright permission Get copyright permission on Copyright Marketplace

KEYWORDS

3D modeling

Unmanned aerial vehicles

Point clouds

RGB color model

Data modeling

Atomic force microscopy

Remote sensing

Show All Keywords

Keywords/Phrases

Search In:

Publication Years