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Review

Object Detection and Image Segmentation with Deep Learning on Earth Observation Data: A Review—Part II: Applications

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(18), 3053; https://doi.org/10.3390/rs12183053
Received: 24 August 2020 / Revised: 16 September 2020 / Accepted: 16 September 2020 / Published: 18 September 2020
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. 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.; Bachofer, F.; Kuenzer, C. Object Detection and Image Segmentation with Deep Learning on Earth Observation Data: A Review—Part II: Applications. Remote Sens. 2020, 12, 3053. https://doi.org/10.3390/rs12183053

AMA Style

Hoeser T, Bachofer F, Kuenzer C. Object Detection and Image Segmentation with Deep Learning on Earth Observation Data: A Review—Part II: Applications. Remote Sensing. 2020; 12(18):3053. https://doi.org/10.3390/rs12183053

Chicago/Turabian Style

Hoeser, Thorsten, Felix Bachofer, and Claudia Kuenzer. 2020. "Object Detection and Image Segmentation with Deep Learning on Earth Observation Data: A Review—Part II: Applications" Remote Sensing 12, no. 18: 3053. https://doi.org/10.3390/rs12183053

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