# Agricultural Landscape Understanding At Country-Scale

arXiv: https://arxiv.org/abs/2411.05359 · HTML: https://mlsanity.com/paper/2411.05359/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 84 references that are in the map.
- First posted on arXiv 8 Nov 2024.

## Builds on (most cited first, 16 of 16)

- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Mask R-CNN](https://mlsanity.com/paper/1703.06870/) (2017)
- [Masked-attention Mask Transformer for Universal Image Segmentation](https://mlsanity.com/paper/2112.01527/) (2021)
- [Panoptic Feature Pyramid Networks](https://mlsanity.com/paper/1901.02446/) (2019)
- [SatMAE: Pre-training Transformers for Temporal and Multi-Spectral Satellite Imagery](https://mlsanity.com/paper/2207.08051/) (2022)
- [Panoptic-DeepLab](https://mlsanity.com/paper/1910.04751/) (2019)
- [Scale-MAE: A Scale-Aware Masked Autoencoder for Multiscale Geospatial Representation Learning](https://mlsanity.com/paper/2212.14532/) (2022)
- [K-Net: Towards Unified Image Segmentation](https://mlsanity.com/paper/2106.14855/) (2021)
- [SSAP: Single-Shot Instance Segmentation With Affinity Pyramid](https://mlsanity.com/paper/1909.01616/) (2019)
- [Deep learning on edge: extracting field boundaries from satellite images with a convolutional neural network](https://mlsanity.com/paper/1910.12023/) (2019)
- [Unlocking large-scale crop field delineation in smallholder farming systems with transfer learning and weak supervision](https://mlsanity.com/paper/2201.04771/) (2022)
- [Panoptic Segmentation: A Review](https://mlsanity.com/paper/2111.10250/) (2021)
- [Panoptic Segmentation Meets Remote Sensing](https://mlsanity.com/paper/2111.12126/) (2021)
- [Farmland Parcel Delineation Using Spatio-temporal Convolutional Networks](https://mlsanity.com/paper/2004.05471/) (2020)
- [Multi-Region Transfer Learning for Segmentation of Crop Field Boundaries in Satellite Images with Limited Labels](https://mlsanity.com/paper/2404.00179/) (2024)
- [Small, Sparse, but Substantial: Techniques for Segmenting Small Agricultural Fields Using Sparse Ground Data](https://mlsanity.com/paper/2005.01947/) (2020)

## Built on by (most cited first, 2 of 2)

- [Farm-Level, In-Season Crop Identification for India](https://mlsanity.com/paper/2507.02972/) (2025)
- [Segmentation-Aware Latent Diffusion for Satellite Image Super-Resolution: Enabling Smallholder Farm Boundary Delineation](https://mlsanity.com/paper/2511.14481/) (2025)

## 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.