Agricultural Landscape Understanding At Country-Scale

• URL: http://arxiv.org/abs/2411.05359v1
• Date: Fri, 08 Nov 2024 06:29:02 GMT
• Title: 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, Arnab Basu, Vishal Batchu, Sharath Holla, Bindiya Kurle, Olana Missura, Rahul Aggarwal, Shubhika Garg, Nishi Shah, Avneet Singh, Dinesh Tewari, Agata Dondzik, Bharat Adsul, Milind Sohoni, Asim Rama Praveen, Aaryan Dangi, Lisan Kadivar, E Abhishek, Niranjan Sudhansu, Kamlakar Hattekar, Sameer Datar, Musty Krishna Chaithanya, Anumas Ranjith Reddy, Aashish Kumar, Betala Laxmi Tirumala, Alok Talekar,
• Abstract summary: 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. We have been able to identify individual fields across 151.7M hectares, and delineating key features such as water resources and vegetation.
• Score: 7.978859577060083
• License:
• 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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