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.
Highlights
In recent years, deep learning (DL) has received a lot of attention, in both scientific research and practical application [1,2]
Since convolutional backbones are widely used in other DL tasks, such as image segmentation and object detection, achievements in image recognition can be seen as a main driver for the field
A well-founded understanding of DL as Earth observation researcher is highly necessary when it comes to incorporating knowledge of physical and ecological relationships into DL models [7,9]—an ability that will make the difference between purely data driven Earth observation with a tendency to remain a black box and more understandable models, combining data science and geoscientific expert knowledge
Summary
Deep learning (DL) has received a lot of attention, in both scientific research and practical application [1,2]. Two main factors are responsible for this growing attention: the accessibility of data and the increase in computational processing power, especially with graphics processing units [3,4,5] Due to these developments, researchers were able to demonstrate working concepts for DL which could even outperform established approaches. High resolution optical data have already paved the way for transferring DL concepts from computer vision to Earth observation application such as detecting or segmenting vehicles, roads and buildings from overhead images. By running a thread through important DL publications, we address Earth observation researchers who want to add DL to their toolbox, or want to reflect on the evolution of DL approaches to choose a matching model design for their own research questions Providing this thorough introduction, we contribute to the open question number eight, “How to best handle high entry barriers to DL?” This foundation will further be used in Part II: Applications to discuss applications of CNNs in Earth observation research by reviewing leading Earth observation journals
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