Details
| Originalsprache | Englisch |
|---|---|
| Seiten (von - bis) | 10-19 |
| Seitenumfang | 10 |
| Fachzeitschrift | Geo-Spatial Information Science |
| Jahrgang | 23 |
| Ausgabenummer | 1 |
| Publikationsstatus | Veröffentlicht - 3 Feb. 2020 |
Abstract
During the last few years, artificial intelligence based on deep learning, and particularly based on convolutional neural networks, has acted as a game changer in just about all tasks related to photogrammetry and remote sensing. Results have shown partly significant improvements in many projects all across the photogrammetric processing chain from image orientation to surface reconstruction, scene classification as well as change detection, object extraction and object tracking and recognition in image sequences. This paper summarizes the foundations of deep learning for photogrammetry and remote sensing before illustrating, by way of example, different projects being carried out at the Institute of Photogrammetry and GeoInformation, Leibniz University Hannover, in this exciting and fast moving field of research and development.
ASJC Scopus Sachgebiete
- Sozialwissenschaften (insg.)
- Geografie, Planung und Entwicklung
- Erdkunde und Planetologie (insg.)
- Computer in den Geowissenschaften
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in: Geo-Spatial Information Science, Jahrgang 23, Nr. 1, 03.02.2020, S. 10-19.
Publikation: Beitrag in Fachzeitschrift › Artikel › Forschung › Peer-Review
}
TY - JOUR
T1 - Deep learning for geometric and semantic tasks in photogrammetry and remote sensing
AU - Heipke, Christian
AU - Rottensteiner, Franz
PY - 2020/2/3
Y1 - 2020/2/3
N2 - During the last few years, artificial intelligence based on deep learning, and particularly based on convolutional neural networks, has acted as a game changer in just about all tasks related to photogrammetry and remote sensing. Results have shown partly significant improvements in many projects all across the photogrammetric processing chain from image orientation to surface reconstruction, scene classification as well as change detection, object extraction and object tracking and recognition in image sequences. This paper summarizes the foundations of deep learning for photogrammetry and remote sensing before illustrating, by way of example, different projects being carried out at the Institute of Photogrammetry and GeoInformation, Leibniz University Hannover, in this exciting and fast moving field of research and development.
AB - During the last few years, artificial intelligence based on deep learning, and particularly based on convolutional neural networks, has acted as a game changer in just about all tasks related to photogrammetry and remote sensing. Results have shown partly significant improvements in many projects all across the photogrammetric processing chain from image orientation to surface reconstruction, scene classification as well as change detection, object extraction and object tracking and recognition in image sequences. This paper summarizes the foundations of deep learning for photogrammetry and remote sensing before illustrating, by way of example, different projects being carried out at the Institute of Photogrammetry and GeoInformation, Leibniz University Hannover, in this exciting and fast moving field of research and development.
KW - convolutional neural networks(CNN)
KW - Deep learning
KW - example project from IPI
KW - machine learning
UR - http://www.scopus.com/inward/record.url?scp=85078951558&partnerID=8YFLogxK
U2 - 10.1080/10095020.2020.1718003
DO - 10.1080/10095020.2020.1718003
M3 - Article
AN - SCOPUS:85078951558
VL - 23
SP - 10
EP - 19
JO - Geo-Spatial Information Science
JF - Geo-Spatial Information Science
SN - 1009-5020
IS - 1
ER -