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Authors: Pratik Wagh ; Debanjan Das and Om Damani

Affiliation: Department of Computer Science & Engineering, IIT Bombay, Mumbai and India

ISBN: 978-989-758-371-1

Keyword(s): Remote Sensing, Computer Vision, Object Detection, Convolutional Neural Networks, Machine Learning.

Abstract: The Government of India conducts a well census every five years. It is time-consuming, costly, and usually incomplete. By using transfer learning-based object detection algorithms, we have built a system for the automatic detection of wells in satellite images. We analyze the performance of three object detection algorithms - Convolutional Neural Network, HaarCascade, and Histogram of Oriented Gradients on the task of well detection and find that the Convolutional Neural Network based YOLOv2 performs best and forms the core of our system. Our current system has a precision value of 0.95 and a recall value of 0.91 on our dataset. The main contribution of our work is to create a novel open-source system for well detection in satellite images and create an associated dataset which will be put in the public domain. A related contribution is the development of a general purpose satellite image annotation system to annotate and validate objects in satellite images. While our focus is on wel l detection, the system is general purpose and can be used for detection of other objects as well. (More)

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Paper citation in several formats:
Wagh, P.; Das, D. and Damani, O. (2019). Well Detection in Satellite Images using Convolutional Neural Networks.In Proceedings of the 5th International Conference on Geographical Information Systems Theory, Applications and Management - Volume 1: GISTAM, ISBN 978-989-758-371-1, pages 117-125. DOI: 10.5220/0007734901170125

@conference{gistam19,
author={Pratik Sanjay Wagh. and Debanjan Das. and Om P. Damani.},
title={Well Detection in Satellite Images using Convolutional Neural Networks},
booktitle={Proceedings of the 5th International Conference on Geographical Information Systems Theory, Applications and Management - Volume 1: GISTAM,},
year={2019},
pages={117-125},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0007734901170125},
isbn={978-989-758-371-1},
}

TY - CONF

JO - Proceedings of the 5th International Conference on Geographical Information Systems Theory, Applications and Management - Volume 1: GISTAM,
TI - Well Detection in Satellite Images using Convolutional Neural Networks
SN - 978-989-758-371-1
AU - Wagh, P.
AU - Das, D.
AU - Damani, O.
PY - 2019
SP - 117
EP - 125
DO - 10.5220/0007734901170125

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