DLR-Logo -> http://www.dlr.de
DLR Portal Home | Imprint | Privacy Policy | Contact | Deutsch
Fontsize: [-] Text [+]

Building footprint extraction from Digital Surface Models using Neural Networks

Davydova, Ksenia and Cui, Shiyong and Reinartz, Peter (2016) Building footprint extraction from Digital Surface Models using Neural Networks. In: Proceedings of SPIE, 10004, pp. 1-10. SPIE Remote Sensing 2016, 26.-29. Sep 2016, Edinburgh. doi: 10.1117/12.2240727.

[img] PDF

Official URL: http://proceedings.spiedigitallibrary.org/proceeding.aspx?articleid=2571485


Two-dimensional building footprints are a basis for many applications: from cartography to three-dimensional building models generation. Although, many methodologies have been proposed for building footprint extraction, this topic remains an open research area. Neural networks are able to model the complex relationships between the multivariate input vector and the target vector. Based on these abilities we propose a methodology using neural networks and Markov Random Fields (MRF) for automatic building footprint extraction from normalized Digital Surface Model (nDSM) and satellite images within urban areas. The proposed approach has mainly two steps. In the first step, the unary terms are learned for the MRF energy function by a four-layer neural network. The neural network is learned on a large set of patches consisting of both nDSM and Normalized Difference Vegetation Index (NDVI). Then prediction is performed to calculate the unary terms that are used in the MRF. In the second step, the energy function is minimized using a max ow algorithm, which leads to a binary building mask. The building extraction results are compared with available ground truth. The comparison illustrates the efficiency of the proposed algorithm which can extract approximately 80% of buildings from nDSM with high accuracy.

Item URL in elib:https://elib.dlr.de/108368/
Document Type:Conference or Workshop Item (Speech)
Title:Building footprint extraction from Digital Surface Models using Neural Networks
AuthorsInstitution or Email of AuthorsAuthor's ORCID iD
Davydova, Kseniaksenia.davydova (at) dlr.deUNSPECIFIED
Cui, Shiyongshiyong.cui (at) dlr.deUNSPECIFIED
Reinartz, Peterpeter.reinartz (at) dlr.deUNSPECIFIED
Journal or Publication Title:Proceedings of SPIE
Refereed publication:Yes
Open Access:Yes
Gold Open Access:No
In ISI Web of Science:Yes
DOI :10.1117/12.2240727
Page Range:pp. 1-10
EditorsEmailEditor's ORCID iD
Keywords:Building footprint extraction, binary mask, Digital Surface Model, neural networks, Markov Random Fields, Normalized Difference Vegetation Index
Event Title:SPIE Remote Sensing 2016
Event Location:Edinburgh
Event Type:international Conference
Event Dates:26.-29. Sep 2016
Organizer:SPIE Remote Sensing
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Space
HGF - Program Themes:Earth Observation
DLR - Research area:Raumfahrt
DLR - Program:R EO - Earth Observation
DLR - Research theme (Project):R - Vorhaben hochauflösende Fernerkundungsverfahren (old)
Location: Oberpfaffenhofen
Institutes and Institutions:Remote Sensing Technology Institute > Photogrammetry and Image Analysis
Deposited By: Bittner, Ksenia
Deposited On:25 Nov 2016 13:21
Last Modified:31 Jul 2019 20:05

Repository Staff Only: item control page

Help & Contact
electronic library is running on EPrints 3.3.12
Copyright © 2008-2017 German Aerospace Center (DLR). All rights reserved.