Agricultural Landscape Understanding At Country-Scale
/ Authors
Radhika Dua, Nikita Saxena, Aditi Agarwal, Alex Wilson, Gaurav Singh, Hoang Tran, Ishan Deshpande, Amandeep Kaur, Gaurav Aggarwal, Chandan Nath
and 25 more authors
Arnab Basu, Vishal Batchu, Sharath Holla, Bindiya Kurle, Olana Missura, R. Aggarwal, S. Garg, N. Shah, Avneet Singh, Dinesh Tewari, Agata Dondzik, B. Adsul, M. Sohoni, A. Praveen, Aaryan Dangi, Lisan Kadivar, E. Abhishek, Niranjan Sudhansu, Kamlakar Hattekar, Sameer G Datar, M. Chaithanya, A. Reddy, Aashish Kumar, Betala Laxmi Tirumala, A. Talekar
/ 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.
Journal: ArXiv