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Titel:

Deep Learning for Semantic 3D City Model Extension: Modeling Roof Superstructures using Aerial Images for Solar Potential Analysis

Dokumenttyp:
Konferenzbeitrag
Art des Konferenzbeitrags:
Textbeitrag / Aufsatz
Autor(en):
Krapf, Sebastian; Willenborg, Bruno; Knoll, Kevin; Bruhse, Matthias; Kolbe, Thomas H.
Abstract:
On a global scale, semantic 3D city models with Level of Detail 2 become more and more available. Automated generation of higher Level of Detail models is an active field of research, but low coverage of dense LiDAR or photogrammetric point clouds is a barrier. Therefore, this paper presents a novel approach for enriching semantic 3D city models with roof superstructures extracted from aerial images using deep learning. The method maps and classifies superstructures in 2D and subsequently tra...     »
Stichworte:
GISTop_Energy; GISPro_3DCityDB; GISTop_Semantic_modeling_and_transformation; GISTop_SpatialModelingAndAlgorithms; LOCenter; LOCTop_Urban_Information_Modeling_Virtual_3D_City_Model
Kongress- / Buchtitel:
ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Band / Teilband / Volume:
X-4/W2-2022
Ausrichter der Konferenz:
UNSW Sydney
Datum der Konferenz:
18.-21.10.2022
Jahr:
2022
Quartal:
3. Quartal
Jahr / Monat:
2022-10
Monat:
Oct
Seiten:
161--168
Nachgewiesen in:
Scopus; Web of Science
Serientitel:
ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Reviewed:
ja
Sprache:
en
Volltext / DOI:
doi:10.5194/isprs-annals-X-4-W2-2022-161-2022
WWW:
https://www.isprs-ann-photogramm-remote-sens-spatial-inf-sci.net/X-4-W2-2022/161/2022/
Semester:
WS 22-23
TUM Einrichtung:
Lehrstuhl für Fahrzeugtechnik; Lehrstuhl für Geoinformatik
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