Abstract
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.
Highlights
The availability of spatio-temporal Earth observation data has increased dramatically over recent decades
It is characteristic that most of these studies took data from multiple locations or not-further defined places with a minor focus on a geoscientific research question or a distinct study site. They focus on the technical implementation of convolutional neural network (CNN) on Earth observation data and proof-of-concept studies, which are an essential driver of the increasing usage of CNNs in Earth observation research
We provide an extensive overview of the convolutional neural network (CNN) for image segmentation and object detection in Earth observation research by analyzing 429 publications
Summary
The availability of spatio-temporal Earth observation data has increased dramatically over recent decades This data provides the needed information to understand and monitor land-surface dynamics on a large scale, for example: urban growth and the distribution of settlements [1], vegetation cover and its temporal dynamics [2], and water availability [3]. Such studies analyze aggregated classes: buildings and impervious surfaces are summarized as built-up areas, trees and grassland become regions with different vegetation intensities, and open water and shorelines are mapped as binary water masks. In Earth observation, land surface dynamics could be better described as specific things within general classes
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
