# The MIS Check-Dam Dataset for Object Detection and Instance Segmentation Tasks

Source: [The MIS Check-Dam Dataset for Object Detection and Instance Segmentation Tasks](<https://www.sota2.com/research/paper/68fa6eb81e209131cea41c4a>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-26T01:04:50.062Z

Authors: Chintan Tundia, Rajiv Kumar, Om Damani, G. Sivakumar

Published: 2021-11-30T18:04:02.000Z

## 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/.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2111.15613>)

[PDF](<https://arxiv.org/pdf/2111.15613.pdf>)