Agricultural Landscape Understanding At Country-Scale
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Abstract (arXiv)
Comprehensive agricultural landscape understanding is critical for addressing global challenges in food security, climate change, and resource management. This requires mapping not just crop fields, but also vital features like trees and water bodies which form an intricate mosaic in complex textitsmallholder systems dominating the Global South. Previous efforts to develop such land use maps have been limited by a narrow focus on methods for field delineation only, and also do not develop robust post-processing steps essential for real-world deployment. Further, to our knowledge, no prior system for smallholder farms has been deployed and evaluated at a national scale. This work addresses these limitations by presenting the first national-scale agricultural mapping system that moves beyond simple field delineation to enable segmentation of agricultural instances like fields, trees and water bodies. Our system is refined for real-world application using novel post-processing heuristics to ensure map consistency and accuracy, and is validated through a rigorous, multi-faceted evaluation process. Fine-grained land use maps generated by our system are publicly accessible via an API at textithrefhttp://agri.withgoogle.comhttp://agri.withgoogle.com, enabling a wide range of applications from precision agriculture and policy-making to advancing global sustainability development goals.
Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 16 of its 84 references that are in the map. First posted on arXiv 8 Nov 2024.
Builds on
16 of 84 references.
References · 16
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4 more
- 2021Panoptic Segmentation Meets Remote Sensingcited by 14
- 2020Farmland Parcel Delineation Using Spatio-temporal Convolutional Networkscited by 13
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Citing papers per year: 2025: 2.
2 papers.