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Authors: Chintan Tundia ; Pooja Tank and Om Damani

Affiliation: Indian Institute of Technology - Bombay, Mumbai, India

Keyword(s): Minor Irrigation Census, Object Detection, Deep Learning, Remote Sensing, Computer Vision.

Abstract: Minor irrigation structures such as well and farm ponds play very important roles in agriculture growth in developing countries. Typically, a minor irrigation census is conducted every five years to take inventory of these structures. It is essential that an up to date database of these structures be maintained for planning and policy formulation purposes. In this work, we present the design and implementation of an online system for the automatic detection of irrigation structures from satellite images. Our system is built using three popular object detection architectures - YOLO, FasterRCNN and RetinaNet. Our system takes input at multiple resolutions and fragments and reassembles the input region to perform object detection. Since currently there exists no dataset for farm pond and the only publicly available well dataset covers a small geographical region, we have prepared object detection datasets for farm ponds and wells using Google Maps satellite images. We compare the perfor mance of a number of state of the art object detection models and find that a clear trade-off exists between the detection accuracy and inference time with the RetinaNet providing a golden mean. (More)

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Paper citation in several formats:
Tundia, C. ; Tank, P. and Damani, O. (2020). Aiding Irrigation Census in Developing Countries by Detecting Minor Irrigation Structures from Satellite Imagery. In Proceedings of the 6th International Conference on Geographical Information Systems Theory, Applications and Management - GISTAM; ISBN 978-989-758-425-1; ISSN 2184-500X, SciTePress, pages 208-215. DOI: 10.5220/0009421302080215

@conference{gistam20,
author={Chintan Tundia and Pooja Tank and Om Damani},
title={Aiding Irrigation Census in Developing Countries by Detecting Minor Irrigation Structures from Satellite Imagery},
booktitle={Proceedings of the 6th International Conference on Geographical Information Systems Theory, Applications and Management - GISTAM},
year={2020},
pages={208-215},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0009421302080215},
isbn={978-989-758-425-1},
issn={2184-500X},
}

TY - CONF

JO - Proceedings of the 6th International Conference on Geographical Information Systems Theory, Applications and Management - GISTAM
TI - Aiding Irrigation Census in Developing Countries by Detecting Minor Irrigation Structures from Satellite Imagery
SN - 978-989-758-425-1
IS - 2184-500X
AU - Tundia, C.
AU - Tank, P.
AU - Damani, O.
PY - 2020
SP - 208
EP - 215
DO - 10.5220/0009421302080215
PB - SciTePress

- Science and Technology Publications, Lda.
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