# Unlocking large-scale crop field delineation in smallholder farming systems with transfer learning and weak supervision arXiv: https://arxiv.org/abs/2201.04771 · HTML: https://mlsanity.com/paper/2201.04771/ ## Key facts - Cited by 21 papers in the map (data through 14 Oct 2026). - Builds on 6 of its 43 references that are in the map. - First posted on arXiv 13 Jan 2022. ## Builds on (most cited first, 6 of 6) - [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015) - [Generalized Intersection over Union: A Metric and A Loss for Bounding Box Regression](https://mlsanity.com/paper/1902.09630/) (2019) - [ResUNet-a: a deep learning framework for semantic segmentation of remotely sensed data](https://mlsanity.com/paper/1904.00592/) (2019) - [Deep learning on edge: extracting field boundaries from satellite images with a convolutional neural network](https://mlsanity.com/paper/1910.12023/) (2019) - [Looking for change? Roll the Dice and demand Attention](https://mlsanity.com/paper/2009.02062/) (2020) - [Farmland Parcel Delineation Using Spatio-temporal Convolutional Networks](https://mlsanity.com/paper/2004.05471/) (2020) ## Built on by (most cited first, 21 of 21) - [Fewshot learning on global multimodal embeddings for earth observation tasks](https://mlsanity.com/paper/2310.00119/) (2023) - [Multi-Region Transfer Learning for Segmentation of Crop Field Boundaries in Satellite Images with Limited Labels](https://mlsanity.com/paper/2404.00179/) (2024) - [Delineate Anything: Resolution-Agnostic Field Boundary Delineation on Satellite Imagery](https://mlsanity.com/paper/2504.02534/) (2025) - [PRUE: A Practical Recipe for Field Boundary Segmentation at Scale](https://mlsanity.com/paper/2603.27101/) (2026) - [Responsible AI for Earth Observation](https://mlsanity.com/paper/2405.20868/) (2024) - [Farmland Extent and Visible Boundary Mapping from 1 m NAIP Imagery Using Residual U-Net and Text-Prompted SAM 3 Refinement](https://mlsanity.com/paper/2607.21881/) (2026) - [Investigating the Segment Anything Foundation Model for Mapping Smallholder Agriculture Field Boundaries Without Training Labels](https://mlsanity.com/paper/2407.01846/) (2024) - [GTPBD: A Fine-Grained Global Terraced Parcel and Boundary Dataset](https://mlsanity.com/paper/2507.14697/) (2025) - [A Comprehensive Review of Agricultural Parcel and Boundary Delineation from Remote Sensing Images: Recent Progress and Future Perspectives](https://mlsanity.com/paper/2508.14558/) (2025) - [Taking it further: leveraging pseudo labels for field delineation across label-scarce smallholder regions](https://mlsanity.com/paper/2312.08384/) (2023) - [Agricultural Landscape Understanding At Country-Scale](https://mlsanity.com/paper/2411.05359/) (2024) - [The first global agricultural field boundary map at 10m resolution](https://mlsanity.com/paper/2605.11055/) (2026) - [Invariant Features for Global Crop Type Classification](https://mlsanity.com/paper/2509.03497/) (2025) - [AI for Agriculture: the Comparison of Semantic Segmentation Methods for Crop Mapping with Sentinel-2 Imagery](https://mlsanity.com/paper/2311.12993/) (2023) - [Satellite Sunroof: High-res Digital Surface Models and Roof Segmentation for Global Solar Mapping](https://mlsanity.com/paper/2408.14400/) (2024) - [Tackling fluffy clouds: robust agricultural field boundary delineation from Sentinel-1 and Sentinel-2 satellite image time series](https://mlsanity.com/paper/2409.13568/) (2024) - [A Decade of Wheat Mapping for Lebanon](https://mlsanity.com/paper/2504.11366/) (2025) - [Segmentation-Aware Latent Diffusion for Satellite Image Super-Resolution: Enabling Smallholder Farm Boundary Delineation](https://mlsanity.com/paper/2511.14481/) (2025) - [MAgSeg: Segmentation of Agricultural Landscapes in High-Resolution Satellite Imagery using Multimodal Large Language Models](https://mlsanity.com/paper/2605.16179/) (2026) - [Fields of the Planet: Field Boundary Mapping Beyond 10m](https://mlsanity.com/paper/2607.04449/) (2026) - [Hob-VL: A Benchmark for Visually Grounded Boolean Reasoning](https://mlsanity.com/paper/2610.01605/) (2026) ## Abstract (arXiv) Crop field boundaries aid in mapping crop types, predicting yields, and delivering field-scale analytics to farmers. Recent years have seen the successful application of deep learning to delineating field boundaries in industrial agricultural systems, but field boundary datasets remain missing in smallholder systems due to (1) small fields that require high resolution satellite imagery to delineate and (2) a lack of ground labels for model training and validation. In this work, we combine transfer learning and weak supervision to overcome these challenges, and we demonstrate the methods' success in India where we efficiently generated 10,000 new field labels. Our best model uses 1.5m resolution Airbus SPOT imagery as input, pre-trains a state-of-the-art neural network on France field boundaries, and fine-tunes on India labels to achieve a median Intersection over Union (IoU) of 0.86 in India. If using 4.8m resolution PlanetScope imagery instead, the best model achieves a median IoU of 0.72. Experiments also show that pre-training in France reduces the number of India field labels needed to achieve a given performance level by as much as 20× when datasets are small. These findings suggest our method is a scalable approach for delineating crop fields in regions of the world that currently lack field boundary datasets. We publicly release the 10,000 labels and delineation model to facilitate the creation of field boundary maps and new methods by the community.