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.

翻译：农业景观极为复杂，在全球南方地区尤其如此，那里的田块更小，农业实践更为多样。本文报告了我们在研究区域印度对农业景观（自然与人工）进行数字化的工作进展。我们利用高分辨率影像和UNet风格的分割模型，生成了首个国家尺度的多类别全景分割成果。通过这项工作，我们成功识别了跨越1.517亿公顷的独立田块，并划定了水资源和植被等关键特征。我们分享了该成果如何通过我们团队以及下游用户的外部验证，并列举了一些可促成针对性数据驱动决策的示例用例。我们相信，通过生成这一基础底图层，该数据集将为农业数字化进程作出贡献。