Aiding Irrigation Census in Developing Countries by Detecting Minor Irrigation Structures from Satellite Imagery

Chintan Tundia, Pooja Tank, Om Damani

2020

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 performance 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.

Paper Citation

in Harvard Style

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 - Volume 1: GISTAM, ISBN 978-989-758-425-1, pages 208-215. DOI: 10.5220/0009421302080215

in Bibtex Style

@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 - Volume 1: GISTAM,},
year={2020},
pages={208-215},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0009421302080215},
isbn={978-989-758-425-1},
}

in EndNote Style

TY - CONF

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