Agricultural Landscape Understanding At Country-Scale
Radhika Dua, Nikita Saxena, Aditi Agarwal, Alex Wilson, Gaurav Singh,, Hoang Tran, Ishan Deshpande, Amandeep Kaur, Gaurav Aggarwal, Chandan Nath,, Arnab Basu, Vishal Batchu, Sharath Holla, Bindiya Kurle, Olana Missura, Rahul, Aggarwal, Shubhika Garg, Nishi Shah, Avneet Singh

TL;DR
This paper presents a novel approach to digitize and analyze agricultural landscapes at a country scale in India using high-resolution imagery and advanced segmentation models, enabling detailed mapping of fields and features.
Contribution
It introduces the first national-scale multi-class panoptic segmentation dataset for Indian agricultural landscapes, advancing automated landscape understanding.
Findings
Identified individual fields across 151.7 million hectares.
Generated detailed maps of water resources and vegetation.
Validated segmentation outputs with external users and case studies.
Abstract
Agricultural landscapes are quite complex, especially in the Global South where fields are smaller, and agricultural practices are more varied. In this paper we report on our progress in digitizing the agricultural landscape (natural and man-made) in our study region of India. We use high resolution imagery and a UNet style segmentation model to generate the first of its kind national-scale multi-class panoptic segmentation output. Through this work we have been able to identify individual fields across 151.7M hectares, and delineating key features such as water resources and vegetation. We share how this output was validated by our team and externally by downstream users, including some sample use cases that can lead to targeted data driven decision making. We believe this dataset will contribute towards digitizing agriculture by generating the foundational baselayer.
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Taxonomy
TopicsAgricultural Systems and Practices · Remote Sensing and Land Use
