Deep learning for understanding satellite imagery : an experimental survey

Mohanty, Sharada Prasanna; Czakon, Jakub; Kaczmarek, Kamil A.; Pyskir, Andrzej; Tarasiewicz, Piotr; Kunwar, Saket; Rohrbach, Janick; Luo, Dave; Prasad, Manjunath; Fleer, Sascha; Göpfert, Jan Philip; Tandon, Akshat; Mollard, Guillaume; Rayaprolu, Nikhil; Salathe, Marcel; Schilling, Malte

doi:10.3389/frai.2020.534696

Please use this identifier to cite or link to this item: https://doi.org/10.21256/zhaw-25912

Publication type:	Article in scientific journal
Type of review:	Peer review (publication)
Title:	Deep learning for understanding satellite imagery : an experimental survey
Authors:	Mohanty, Sharada Prasanna Czakon, Jakub Kaczmarek, Kamil A. Pyskir, Andrzej Tarasiewicz, Piotr Kunwar, Saket Rohrbach, Janick Luo, Dave Prasad, Manjunath Fleer, Sascha Göpfert, Jan Philip Tandon, Akshat Mollard, Guillaume Rayaprolu, Nikhil Salathe, Marcel Schilling, Malte
et. al:	No
DOI:	10.3389/frai.2020.534696 10.21256/zhaw-25912
Published in:	Frontiers in Artificial Intelligence
Volume(Issue):	3
Issue:	534696
Issue Date:	2020
Publisher / Ed. Institution:	Frontiers Research Foundation
ISSN:	2624-8212
Language:	English
Subjects:	Deep learning; Machine learning; Remote sensing; Satellite imagery; Semantic segmentation
Subject (DDC):	006: Special computer methods
Abstract:	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 automated satellite image translation. To facilitate research in this direction, we introduce the Satellite Imagery Competition using a modified SpaceNet dataset. Participants had to come up with different segmentation models to detect positions of buildings on satellite images. In this work, we present five approaches based on improvements of U-Net and Mask R-Convolutional Neuronal Networks models, coupled with unique training adaptations using boosting algorithms, morphological filter, Conditional Random Fields and custom losses. The good results-as high as AP=0.937 and AR=0.959 -from these models demonstrate the feasibility of Deep Learning in automated satellite image annotation.
URI:	https://digitalcollection.zhaw.ch/handle/11475/25912
Fulltext version:	Published version
License (according to publishing contract):	CC BY 4.0: Attribution 4.0 International
Departement:	School of Engineering
Organisational Unit:	Institute of Data Analysis and Process Design (IDP)
Appears in collections:	Publikationen School of Engineering

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Mohanty, S. P., Czakon, J., Kaczmarek, K. A., Pyskir, A., Tarasiewicz, P., Kunwar, S., Rohrbach, J., Luo, D., Prasad, M., Fleer, S., Göpfert, J. P., Tandon, A., Mollard, G., Rayaprolu, N., Salathe, M., & Schilling, M. (2020). Deep learning for understanding satellite imagery : an experimental survey. Frontiers in Artificial Intelligence, 3(534696). https://doi.org/10.3389/frai.2020.534696

Mohanty, S.P. et al. (2020) ‘Deep learning for understanding satellite imagery : an experimental survey’, Frontiers in Artificial Intelligence, 3(534696). Available at: https://doi.org/10.3389/frai.2020.534696.

S. P. Mohanty et al., “Deep learning for understanding satellite imagery : an experimental survey,” Frontiers in Artificial Intelligence, vol. 3, no. 534696, 2020, doi: 10.3389/frai.2020.534696.

MOHANTY, Sharada Prasanna, 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 und Malte SCHILLING, 2020. Deep learning for understanding satellite imagery : an experimental survey. Frontiers in Artificial Intelligence. 2020. Bd. 3, Nr. 534696. DOI 10.3389/frai.2020.534696

Mohanty, Sharada Prasanna, Jakub Czakon, Kamil A. Kaczmarek, Andrzej Pyskir, Piotr Tarasiewicz, Saket Kunwar, Janick Rohrbach, et al. 2020. “Deep Learning for Understanding Satellite Imagery : An Experimental Survey.” Frontiers in Artificial Intelligence 3 (534696). https://doi.org/10.3389/frai.2020.534696.

Mohanty, Sharada Prasanna, et al. “Deep Learning for Understanding Satellite Imagery : An Experimental Survey.” Frontiers in Artificial Intelligence, vol. 3, no. 534696, 2020, https://doi.org/10.3389/frai.2020.534696.