Hoeser, Thorsten und Kuenzer, Claudia (2020) Object Detection and Image Segmentation with Deep Learning on Earth Observation Data: A Review-Part I: Evolution and Recent Trends. Remote Sensing, 12 (10), Seiten 1-44. Multidisciplinary Digital Publishing Institute (MDPI). doi: 10.3390/rs12101667. ISSN 2072-4292.
PDF
- Verlagsversion (veröffentlichte Fassung)
11MB |
Offizielle URL: https://www.mdpi.com/2072-4292/12/10/1667
Kurzfassung
Deep learning (DL) has great influence on large parts of science and increasingly established itself as an adaptive method for new challenges in the field of Earth observation (EO). Nevertheless, the entry barriers for EO researchers are high due to the dense and rapidly developing field mainly driven by advances in computer vision (CV). To lower the barriers for researchers in EO, this review gives an overview of the evolution of DL with a focus on image segmentation and object detection in convolutional neural networks (CNN). The survey starts in 2012, when a CNN set new standards in image recognition, and lasts until late 2019. Thereby, we highlight the connections between the most important CNN architectures and cornerstones coming from CV in order to alleviate the evaluation of modern DL models. Furthermore, we briefly outline the evolution of the most popular DL frameworks and provide a summary of datasets in EO. By discussing well performing DL architectures on these datasets as well as reflecting on advances made in CV and their impact on future research in EO, we narrow the gap between the reviewed, theoretical concepts from CV and practical application in EO.
elib-URL des Eintrags: | https://elib.dlr.de/135424/ | ||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Dokumentart: | Zeitschriftenbeitrag | ||||||||||||
Titel: | Object Detection and Image Segmentation with Deep Learning on Earth Observation Data: A Review-Part I: Evolution and Recent Trends | ||||||||||||
Autoren: |
| ||||||||||||
Datum: | 22 Mai 2020 | ||||||||||||
Erschienen in: | Remote Sensing | ||||||||||||
Referierte Publikation: | Ja | ||||||||||||
Open Access: | Ja | ||||||||||||
Gold Open Access: | Ja | ||||||||||||
In SCOPUS: | Ja | ||||||||||||
In ISI Web of Science: | Ja | ||||||||||||
Band: | 12 | ||||||||||||
DOI: | 10.3390/rs12101667 | ||||||||||||
Seitenbereich: | Seiten 1-44 | ||||||||||||
Verlag: | Multidisciplinary Digital Publishing Institute (MDPI) | ||||||||||||
ISSN: | 2072-4292 | ||||||||||||
Status: | veröffentlicht | ||||||||||||
Stichwörter: | artificial intelligence; AI; machine learning; deep learning; neural networks; convolutional neural networks; CNN; image segmentation; object detection; Earth observation | ||||||||||||
HGF - Forschungsbereich: | Luftfahrt, Raumfahrt und Verkehr | ||||||||||||
HGF - Programm: | Raumfahrt | ||||||||||||
HGF - Programmthema: | Erdbeobachtung | ||||||||||||
DLR - Schwerpunkt: | Raumfahrt | ||||||||||||
DLR - Forschungsgebiet: | R EO - Erdbeobachtung | ||||||||||||
DLR - Teilgebiet (Projekt, Vorhaben): | R - Geowissenschaftl. Fernerkundungs- und GIS-Verfahren | ||||||||||||
Standort: | Oberpfaffenhofen | ||||||||||||
Institute & Einrichtungen: | Deutsches Fernerkundungsdatenzentrum > Dynamik der Landoberfläche | ||||||||||||
Hinterlegt von: | Höser, Thorsten | ||||||||||||
Hinterlegt am: | 29 Jun 2020 15:00 | ||||||||||||
Letzte Änderung: | 25 Okt 2023 08:32 |
Nur für Mitarbeiter des Archivs: Kontrollseite des Eintrags