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Advancing Semantic Segmentation for Building Detection in Very High-Resolution Data

Giessing, Lennart (2025) Advancing Semantic Segmentation for Building Detection in Very High-Resolution Data. Masterarbeit, Universität Konstanz.

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Kurzfassung

Accurate building footprints are essential for urban planning, crisis management, and social science research, yet Germany lacks a comprehensive and up to date nationwide register. Existing sources such as cadastral data, and OpenStreetMap remain incomplete or inconsistent. At the same time current deep learning models for automatic footprint extraction still suffer from systematic errors. This thesis investigates whether optimizing preprocessing, postprocessing, and training data selection can improve the CNN-based extraction model proposed by Stiller et al. [2023]. Experiments with normalization strategies, LiDAR-based height layers, tile overlap, and threshold settings show that targeted adjustments enhance performance. The final pipeline, using DSM data and refined data normalization, improved Overall Accuracy by 7.0 percentage points, IoU by 5.2 percentage points, and F1 score by 3.3 percentage points compared to the baseline. A case study on Berlin illustrates the practical value of the generated data for the social sciences by linking building geometries with demographic and building use data. The findings highlight both the technical and applied relevance of the improved workflow: advancing footprint extraction toward official usability while enabling new insights in the social sciences.

elib-URL des Eintrags:https://elib.dlr.de/216715/
Dokumentart:Hochschulschrift (Masterarbeit)
Titel:Advancing Semantic Segmentation for Building Detection in Very High-Resolution Data
Autoren:
AutorenInstitution oder E-Mail-AdresseAutoren-ORCID-iDORCID Put Code
Giessing, Lennartlennart.giessing (at) dlr.deNICHT SPEZIFIZIERTNICHT SPEZIFIZIERT
DLR-Supervisor:
BeitragsartDLR-SupervisorInstitution oder E-Mail-AdresseDLR-Supervisor-ORCID-iD
Thesis advisorStiller, DorotheeDorothee.Stiller (at) dlr.dehttps://orcid.org/0000-0002-8681-6144
Thesis advisorHertrich, Moritz Remymoritz.hertrich (at) dlr.dehttps://orcid.org/0009-0004-4468-7382
Datum:19 September 2025
Open Access:Ja
Seitenanzahl:115
Status:veröffentlicht
Stichwörter:semantic segmentation, building detection, Deep Leaning, CNN
Institution:Universität Konstanz
Abteilung:Department of Politics and Public Administration
HGF - Forschungsbereich:Luftfahrt, Raumfahrt und Verkehr
HGF - Programm:Verkehr
HGF - Programmthema:Verkehrssystem
DLR - Schwerpunkt:Verkehr
DLR - Forschungsgebiet:V VS - Verkehrssystem
DLR - Teilgebiet (Projekt, Vorhaben):V - MoDa - Models and Data for Future Mobility_Supporting Services
Standort: Oberpfaffenhofen
Institute & Einrichtungen:Deutsches Fernerkundungsdatenzentrum > Georisiken und zivile Sicherheit
Hinterlegt von: Hertrich, Moritz Remy
Hinterlegt am:23 Sep 2025 10:02
Letzte Änderung:23 Sep 2025 10:02

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