# Investigating the Segment Anything Foundation Model for Mapping Smallholder Agriculture Field Boundaries Without Training Labels

arXiv: https://arxiv.org/abs/2407.01846 · HTML: https://mlsanity.com/paper/2407.01846/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 42 references that are in the map.
- First posted on arXiv 1 Jul 2024.

## Builds on (most cited first, 6 of 6)

- [A Generalizable and Accessible Approach to Machine Learning with Global Satellite Imagery](https://mlsanity.com/paper/2010.08168/) (2020)
- [The Segment Anything Model (SAM) for Remote Sensing Applications: From Zero to One Shot](https://mlsanity.com/paper/2306.16623/) (2023)
- [Autonomous GIS: the next-generation AI-powered GIS](https://mlsanity.com/paper/2305.06453/) (2023)
- [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)
- [Core Building Blocks: Next Gen Geo Spatial GPT Application](https://mlsanity.com/paper/2310.11029/) (2023)

## Built on by (most cited first, 3 of 3)

- [No time to train! Training-Free Reference-Based Instance Segmentation](https://mlsanity.com/paper/2507.02798/) (2025)
- [Delineate Anything: Resolution-Agnostic Field Boundary Delineation on Satellite Imagery](https://mlsanity.com/paper/2504.02534/) (2025)
- [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)

## Abstract (arXiv)

Accurate mapping of agricultural field boundaries is crucial for enhancing outcomes like precision agriculture, crop monitoring, and yield estimation. However, extracting these boundaries from satellite images is challenging, especially for smallholder farms and data-scarce environments. This study explores the Segment Anything Model (SAM) to delineate agricultural field boundaries in Bihar, India, using 2-meter resolution SkySat imagery without additional training. We evaluate SAM's performance across three model checkpoints, various input sizes, multi-date satellite images, and edge-enhanced imagery. Our results show that SAM correctly identifies about 58% of field boundaries, comparable to other approaches requiring extensive training data. Using different input image sizes improves accuracy, with the most significant improvement observed when using multi-date satellite images. This work establishes proof of concept for using SAM and maximizing its potential in agricultural field boundary mapping. Our work highlights SAM's potential in delineating agriculture field boundary in training-data scarce settings to enable a wide range of agriculture related analysis.