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Authors: Chintan Tundia ; Rajiv Kumar ; Om Damani and G. Sivakumar

Affiliation: Indian Institute of Technology Bombay, Mumbai, India

Keyword(s): Object Detection, Instance Segmentation, Remote Sensing, Image Transformers.

Abstract: Deep learning has led to many recent advances in object detection and instance segmentation, among other computer vision tasks. These advancements have led to wide application of deep learning based methods and related methodologies in object detection tasks for satellite imagery. In this paper, we introduce MIS Check-Dam, a new dataset of check-dams from satellite imagery for building an automated system for the detection and mapping of check-dams, focusing on the importance of irrigation structures used for agriculture. We review some of the most recent object detection and instance segmentation methods and assess their performance on our new dataset. We evaluate several single stage, two-stage and attention based methods under various network configurations and backbone architectures. The dataset and the pre-trained models are available at https://www.cse.iitb.ac.in/gramdrishti/.

CC BY-NC-ND 4.0

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Paper citation in several formats:
Tundia, C., Kumar, R., Damani, O. and Sivakumar, G. (2022). The MIS Check-Dam Dataset for Object Detection and Instance Segmentation Tasks. In Proceedings of the 17th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2022) - Volume 5: VISAPP; ISBN 978-989-758-555-5; ISSN 2184-4321, SciTePress, pages 323-330. DOI: 10.5220/0010799600003124

@conference{visapp22,
author={Chintan Tundia and Rajiv Kumar and Om Damani and G. Sivakumar},
title={The MIS Check-Dam Dataset for Object Detection and Instance Segmentation Tasks},
booktitle={Proceedings of the 17th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2022) - Volume 5: VISAPP},
year={2022},
pages={323-330},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010799600003124},
isbn={978-989-758-555-5},
issn={2184-4321},
}

TY - CONF

JO - Proceedings of the 17th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2022) - Volume 5: VISAPP
TI - The MIS Check-Dam Dataset for Object Detection and Instance Segmentation Tasks
SN - 978-989-758-555-5
IS - 2184-4321
AU - Tundia, C.
AU - Kumar, R.
AU - Damani, O.
AU - Sivakumar, G.
PY - 2022
SP - 323
EP - 330
DO - 10.5220/0010799600003124
PB - SciTePress

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