Agricultural Landscape Understanding At Country-Scale
This work provides a foundational dataset for digitizing agriculture in the Global South, enabling targeted data-driven decision-making for stakeholders in this domain.
The authors tackled the problem of digitizing complex agricultural landscapes in India by using high-resolution imagery and a UNet-style segmentation model to produce the first national-scale multi-class panoptic segmentation output, identifying individual fields across 151.7M hectares and delineating features like water resources and vegetation.
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.