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Semantic labelling of building types A comparison of two approaches using Random Forest and Deep Learning

Droin, Ariane and Wurm, Michael and Sulzer, Wolfgang (2020) Semantic labelling of building types A comparison of two approaches using Random Forest and Deep Learning. In: Publikationen der Deutschen Gesellschaft für Photogrammetrie, Fernerkundung und Geoinformation e.V., pp. 527-538. Deutsche Gesellschaft für Photogrammetrie, Fernerkundung und Geoinformation e.V.. 40. Wissenschaftlich-TechnischeJahrestagung der DGPF, 04.-06. März 2020, Stuttgart, Deutschland. ISSN ISSN 0942-2870.

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Official URL: https://www.dgpf.de/src/tagung/jt2020/proceedings/proceedings/papers/76_KKNP_DGPF2020_Droin_et_al.pdf

Abstract

In the context of sustainable planning, knowledge about building type is crucial. Yet, this information is scarce and mostly inhomogeneous. In regard to the big-data era, two approaches for building type classification are presented based on different data basis. The first approach shows semantic classification of building footprints using a set of features (simple geometric, morphological and topological features) and the machine learning algorithm Random Forest. Very high accuracies for the federal states of Germany could be achieved with Kappa Coefficients between 0.87 and 0.98. The second framework presents the possibility to conduct semantic labelling of aerial images using Fully Convolutional Neural Networks. The gained accuracy in this case is a Kappa of 0.73 for the federal state of Berlin.

Item URL in elib:https://elib.dlr.de/134772/
Document Type:Conference or Workshop Item (Poster)
Title:Semantic labelling of building types A comparison of two approaches using Random Forest and Deep Learning
Authors:
AuthorsInstitution or Email of AuthorsAuthor's ORCID iD
Droin, Arianeariane.droin (at) dlr.deUNSPECIFIED
Wurm, Michaelmichael.wurm (at) dlr.dehttps://orcid.org/0000-0001-5967-1894
Sulzer, Wolfgangwolfgang.sulzer (at) uni-graz.athttps://orcid.org/0000-0001-6040-2405
Date:2020
Journal or Publication Title:Publikationen der Deutschen Gesellschaft für Photogrammetrie, Fernerkundung und Geoinformation e.V.
Refereed publication:No
Open Access:No
Gold Open Access:No
In SCOPUS:No
In ISI Web of Science:No
Page Range:pp. 527-538
Editors:
EditorsEmailEditor's ORCID iD
Kersten, Thomas P.HafenCity Universität Hamburg Thomas.Kersten@hcu-hamburg.dehttps://orcid.org/0000-0001-8910-2887
Publisher:Deutsche Gesellschaft für Photogrammetrie, Fernerkundung und Geoinformation e.V.
ISSN:ISSN 0942-2870
Status:Published
Keywords:Machine Learning, Semantic Labelling, Deep Learning, LoD1
Event Title:40. Wissenschaftlich-TechnischeJahrestagung der DGPF
Event Location:Stuttgart, Deutschland
Event Type:international Conference
Event Dates:04.-06. März 2020
Organizer:Deutsche Gesellschaft für Photogrammetrie und Fernerkundung
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 - Remote Sensing and Geo Research
Location: Oberpfaffenhofen
Institutes and Institutions:German Remote Sensing Data Center > Geo Risks and Civil Security
Deposited By: Droin, Ariane
Deposited On:22 Jun 2020 08:11
Last Modified:22 Jun 2020 08:11

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