Hoeser, Thorsten und Bachofer, Felix und Kuenzer, Claudia (2020) Object Detection and Image Segmentation with Deep Learning on Earth Observation Data: A Review-Part II: Applications. Remote Sensing, 12 (18), Seite 3053. Multidisciplinary Digital Publishing Institute (MDPI). doi: 10.3390/rs12183053. ISSN 2072-4292.
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Offizielle URL: https://www.mdpi.com/2072-4292/12/18/3053
Kurzfassung
In Earth observation (EO), large-scale land-surface dynamics are traditionally analyzed by investigating aggregated classes. The increase in data with a very high spatial resolution enables investigations on a fine-grained feature level which can help us to better understand the dynamics of land surfaces by taking object dynamics into account. To extract fine-grained features and objects, the most popular deep-learning model for image analysis is commonly used: the convolutional neural network (CNN). In this review, we provide a comprehensive overview of the impact of deep learning on EO applications by reviewing 429 studies on image segmentation and object detection with CNNs. We extensively examine the spatial distribution of study sites, employed sensors, used datasets and CNN architectures, and give a thorough overview of applications in EO which used CNNs. Our main finding is that CNNs are in an advanced transition phase from computer vision to EO. Upon this, we argue that in the near future, investigations which analyze object dynamics with CNNs will have a significant impact on EO research. With a focus on EO applications in this Part II, we complete the methodological review provided in Part I.
elib-URL des Eintrags: | https://elib.dlr.de/136194/ | ||||||||||||||||
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Dokumentart: | Zeitschriftenbeitrag | ||||||||||||||||
Titel: | Object Detection and Image Segmentation with Deep Learning on Earth Observation Data: A Review-Part II: Applications | ||||||||||||||||
Autoren: |
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Datum: | 18 September 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/rs12183053 | ||||||||||||||||
Seitenbereich: | Seite 3053 | ||||||||||||||||
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: | 07 Okt 2020 10:29 | ||||||||||||||||
Letzte Änderung: | 25 Okt 2023 08:43 |
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