User: Guest  Login
Title:

Improving Semantic Segmentation of Roof Segments Using Large-Scale Datasets Derived from 3D City Models and High-Resolution Aerial Imagery

Document type:
Zeitschriftenaufsatz
Author(s):
Faltermeier, Florian L.; Krapf, Sebastian; Willenborg, Bruno; Kolbe, Thomas H.
Abstract:
Advances in deep learning techniques for remote sensing as well as the increased availability of high-resolution data enable the extraction of more detailed information from aerial images. One promising task is the semantic segmentation of roof segments and their orientation. However, the lack of annotated data is a major barrier for deploying respective models on a large scale. Previous research demonstrated the viability of the deep learning approach for the task, but currently, published data...     »
Keywords:
GISPro_CityGML; GISTop_CityModeling; GISTop_SpatialModelingAndAlgorithms; GISTop_Energy; RTGIS
Journal title:
Remote Sensing
Year:
2023
Journal volume:
15
Year / month:
2023-04
Quarter:
1. Quartal
Month:
Apr
Journal issue:
17
Reviewed:
ja
Language:
en
Fulltext / DOI:
doi:10.3390/rs15071931
WWW:
https://www.mdpi.com/2072-4292/15/7/1931
Print-ISSN:
2072-4292
E-ISSN:
2072-4292
Submitted:
26.02.2023
Accepted:
30.03.2023
Date of publication:
04.04.2023
TUM Institution:
Lehrstuhl für Fahrzeugtechnik; Lehrstuhl für Geoinformatik
Format:
Text
 BibTeX