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Open AccessEditor’s ChoiceReview

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

1
German Remote Sensing Data Center (DFD), German Aerospace Center (DLR), Münchner Straße 20, D-82234 Wessling, Germany
2
Department of Remote Sensing, Institute of Geography and Geology, University Würzburg, Am Huband, D-97074 Wuerzburg, Germany
*
Author to whom correspondence should be addressed.
Remote Sens. 2020, 12(10), 1667; https://doi.org/10.3390/rs12101667
Received: 27 April 2020 / Revised: 19 May 2020 / Accepted: 19 May 2020 / Published: 22 May 2020
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. View Full-Text
Keywords: artificial intelligence; AI; machine learning; deep learning; neural networks; convolutional neural networks; CNN; image segmentation; object detection; Earth observation artificial intelligence; AI; machine learning; deep learning; neural networks; convolutional neural networks; CNN; image segmentation; object detection; Earth observation
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MDPI and ACS Style

Hoeser, T.; Kuenzer, C. Object Detection and Image Segmentation with Deep Learning on Earth Observation Data: A Review-Part I: Evolution and Recent Trends. Remote Sens. 2020, 12, 1667. https://doi.org/10.3390/rs12101667

AMA Style

Hoeser T, Kuenzer C. Object Detection and Image Segmentation with Deep Learning on Earth Observation Data: A Review-Part I: Evolution and Recent Trends. Remote Sensing. 2020; 12(10):1667. https://doi.org/10.3390/rs12101667

Chicago/Turabian Style

Hoeser, Thorsten; Kuenzer, Claudia. 2020. "Object Detection and Image Segmentation with Deep Learning on Earth Observation Data: A Review-Part I: Evolution and Recent Trends" Remote Sens. 12, no. 10: 1667. https://doi.org/10.3390/rs12101667

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