TY  - JOUR
A1  - Hoeser, Thorsten
A1  - Kuenzer, Claudia
T1  - Object detection and image segmentation with deep learning on Earth observation data: a review-part I: evolution and recent trends
T2  - Remote Sensing
N2  - 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.
KW  - artificial intelligence
KW  - AI
KW  - machine learning
KW  - deep learning
KW  - neural networks
KW  - convolutional neural networks
KW  - CNN
KW  - image segmentation
KW  - object detection
KW  - Earth observation
Y1  - 2020
UR  - https://opus.bibliothek.uni-wuerzburg.de/frontdoor/index/index/docId/20591
UR  - https://nbn-resolving.org/urn:nbn:de:bvb:20-opus-205918
SN  - 2072-4292
VL  - 12
IS  - 10
ER  -