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Object Detection and Image Segmentation with Deep Learning on Earth Observation Data: A Review-Part I: Evolution and Recent Trends

Hoeser, Thorsten and 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), pp. 1-44. Multidisciplinary Digital Publishing Institute (MDPI). DOI: 10.3390/rs12101667 ISSN 2072-4292

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Official URL: https://www.mdpi.com/2072-4292/12/10/1667

Abstract

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.

Item URL in elib:https://elib.dlr.de/135424/
Document Type:Article
Title:Object Detection and Image Segmentation with Deep Learning on Earth Observation Data: A Review-Part I: Evolution and Recent Trends
Authors:
AuthorsInstitution or Email of AuthorsAuthor's ORCID iD
Hoeser, Thorstenthorsten.hoeser (at) dlr.dehttps://orcid.org/0000-0002-7179-3664
Kuenzer, Claudiaclaudia.kuenzer (at) dlr.deUNSPECIFIED
Date:22 May 2020
Journal or Publication Title:Remote Sensing
Refereed publication:Yes
Open Access:Yes
Gold Open Access:Yes
In SCOPUS:Yes
In ISI Web of Science:Yes
Volume:12
DOI :10.3390/rs12101667
Page Range:pp. 1-44
Publisher:Multidisciplinary Digital Publishing Institute (MDPI)
ISSN:2072-4292
Status:Published
Keywords:artificial intelligence; AI; machine learning; deep learning; neural networks; convolutional neural networks; CNN; image segmentation; object detection; Earth observation
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Space
HGF - Program Themes:Earth Observation
DLR - Research area:Raumfahrt
DLR - Program:R EO - Erdbeobachtung
DLR - Research theme (Project):R - Vorhaben Geowissenschaftl. Fernerkundungs- und GIS-Verfahren
Location: Oberpfaffenhofen
Institutes and Institutions:German Remote Sensing Data Center > Land Surface Dynamics
Deposited By: Höser, Thorsten
Deposited On:29 Jun 2020 15:00
Last Modified:29 Jun 2020 15:00

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