# Farmland Parcel Delineation Using Spatio-temporal Convolutional Networks

arXiv: https://arxiv.org/abs/2004.05471 · HTML: https://mlsanity.com/paper/2004.05471/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Apr 2020.

## Built on by (most cited first, 13 of 13)

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- [SustainBench: Benchmarks for Monitoring the Sustainable Development Goals with Machine Learning](https://mlsanity.com/paper/2111.04724/) (2021)
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- [Unlocking large-scale crop field delineation in smallholder farming systems with transfer learning and weak supervision](https://mlsanity.com/paper/2201.04771/) (2022)
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- [Using Multiple Input Modalities Can Improve Data-Efficiency and O.O.D. Generalization for ML with Satellite Imagery](https://mlsanity.com/paper/2507.13385/) (2025)
- [Agricultural Landscape Understanding At Country-Scale](https://mlsanity.com/paper/2411.05359/) (2024)
- [Revolutionizing Global Food Security: Empowering Resilience through Integrated AI Foundation Models and Data-Driven Solutions](https://mlsanity.com/paper/2310.20301/) (2023)
- [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)
- [Segment Anything for Satellite Imagery: A Strong Baseline and a Regional Dataset for Automatic Field Delineation](https://mlsanity.com/paper/2506.16318/) (2025)
- [Segmentation-Aware Latent Diffusion for Satellite Image Super-Resolution: Enabling Smallholder Farm Boundary Delineation](https://mlsanity.com/paper/2511.14481/) (2025)
- [GeoSAM-Lite: A Lightweight Foundation Model for Onboard Remote Sensing Segmentation](https://mlsanity.com/paper/2607.03760/) (2026)

## Abstract (arXiv)

Farm parcel delineation provides cadastral data that is important in developing and managing climate change policies. Specifically, farm parcel delineation informs applications in downstream governmental policies of land allocation, irrigation, fertilization, green-house gases (GHG's), etc. This data can also be useful for the agricultural insurance sector for assessing compensations following damages associated with extreme weather events - a growing trend related to climate change. Using satellite imaging can be a scalable and cost effective manner to perform the task of farm parcel delineation to collect this valuable data. In this paper, we break down this task using satellite imaging into two approaches: 1) Segmentation of parcel boundaries, and 2) Segmentation of parcel areas. We implemented variations of UNets, one of which takes into account temporal information, which achieved the best results on our dataset on farmland parcels in France in 2017.