Deep Learning for Understanding Satellite Imagery: An Experimental Survey

Deep Learning for Understanding Satellite Imagery: An Experimental Survey

Translating satellite imagery into maps requires intensive effort and time, especially leading to inaccurate maps of the affected regions during disaster and conflict. The combination of availability of recent datasets and advances in computer vision made through deep learning paved the way toward a...

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Bibliographic Details
Main Authors: Sharada Prasanna Mohanty, Jakub Czakon, Kamil A. Kaczmarek, Andrzej Pyskir, Piotr Tarasiewicz, Saket Kunwar, Janick Rohrbach, Dave Luo, Manjunath Prasad, Sascha Fleer, Jan Philip Göpfert, Akshat Tandon, Guillaume Mollard, Nikhil Rayaprolu, Marcel Salathe, Malte Schilling
Format: Article
Language:English
Published: Frontiers Media S.A. 2020-11-01
Series:Frontiers in Artificial Intelligence
Subjects:
deep learning
machine learning
remote sensing
satellite imagery
semantic segmentation
Online Access:https://www.frontiersin.org/articles/10.3389/frai.2020.534696/full

Internet

https://www.frontiersin.org/articles/10.3389/frai.2020.534696/full

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