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

TL;DR

This paper introduces MIS Check-Dam, a new satellite imagery dataset for object detection and segmentation of check-dams, and evaluates various deep learning methods on this dataset to advance automated irrigation infrastructure mapping.

Contribution

The paper presents a novel satellite imagery dataset for check-dam detection and assesses multiple deep learning models, providing a benchmark for future research in this domain.

Findings

Performance varies across different detection methods.

Attention-based models show promising results.

Dataset and models are publicly available.

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/.