Fields of The World: A Field Guide for Extracting Agricultural Field Boundaries

• URL: http://arxiv.org/abs/2602.08131v1
• Date: Sun, 08 Feb 2026 21:32:33 GMT
• Title: Fields of The World: A Field Guide for Extracting Agricultural Field Boundaries
• Abstract summary: Field boundary maps are a building block for agricultural data products and support crop monitoring, yield estimation, and disease estimation.<n>This tutorial presents the Fields of The World ecosystem: a benchmark of 1.6M field polygons across 24 countries, pre-trained segmentation models, and command-line inference tools.
• Score: 15.115722799800055
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Field boundary maps are a building block for agricultural data products and support crop monitoring, yield estimation, and disease estimation. This tutorial presents the Fields of The World (FTW) ecosystem: a benchmark of 1.6M field polygons across 24 countries, pre-trained segmentation models, and command-line inference tools. We provide two notebooks that cover (1) local-scale field boundary extraction with crop classification and forest loss attribution, and (2) country-scale inference using cloud-optimized data. We use MOSAIKS random convolutional features and FTW derived field boundaries to map crop type at the field level and report macro F1 scores of 0.65--0.75 for crop type classification with limited labels. Finally, we show how to explore pre-computed predictions over five countries (4.76M km\textsuperscript{2}), with median predicted field areas from 0.06 ha (Rwanda) to 0.28 ha (Switzerland).

Related papers

• Delineate Anything v2: A Global Foundation Model for Field Delineation [2.9595476100051896]
  We introduce Delineate Anything v2, a globally scalable foundation model for wide-area field boundary.<n>In this work, we demonstrate the ability to build a framework that surpasses the state-of-the-art benchmark for zero-shot capabilities.
  arXiv  Detail & Related papers  (2026-07-21T12:59:45Z)
• A Modelling and Evaluation Framework for EuroCrops-Driven Sentinel-2 Crop Segmentation [78.66324246922831]
  This work presents a pipeline for generating semantic-segmentation-ready agricultural datasets from Sentinel-2 imagery and EuroCrops parcel-level annotations.<n>The dataset contains 67,337 patches from five European countries and uses a reduced taxonomy of ten crop classes plus background.<n>A four-level U-Net with Group Normalization was trained using 10 Sentinel-2 spectral bands and a composite loss combining class-weighted cross-entropy and Dice loss.
  arXiv  Detail & Related papers  (2026-05-30T11:20:29Z)
• The first global agricultural field boundary map at 10m resolution [23.150413419951388]
  Most global remote-sensing products for agriculture are only available at the pixel level.<n>No openly available, globally consistent map of agricultural field boundaries exists to date.<n>We present the first global field boundary dataset at 10,m resolution for the years 2024 and 2025.
  arXiv  Detail & Related papers  (2026-05-11T15:54:42Z)
• PRUE: A Practical Recipe for Field Boundary Segmentation at Scale [50.194423500109025]
  We conduct the first systematic evaluation of segmentation and geospatial foundation models (GFMs) for global field boundary delineation.<n>A U-Net semantic segmentation model outperforms instance-based and GFM alternatives on a suite of performance and deployment metrics.<n>Our approach provides a practical framework for reliable, scalable, and reproducible field boundary delineation across model design, training, and inference.
  arXiv  Detail & Related papers  (2026-03-28T02:47:46Z)
• National level satellite-based crop field inventories in smallholder landscapes [0.746823468023145]
  We provide the first national-level dataset of 21 million individual fields for Mozambique.<n>Our maps separate active cropland from non-agricultural land use with an overall accuracy of 93%.<n>Field size in Mozambique is very low overall, with half of the fields being smaller than 0.16 ha, and 83% smaller than 0.5 ha.
  arXiv  Detail & Related papers  (2025-07-14T17:23:43Z)
• Multispectral Remote Sensing for Weed Detection in West Australian Agricultural Lands [3.6284577335311563]
  The Kondinin region in Western Australia faces significant agricultural challenges due to pervasive weed infestations, causing economic losses and ecological impacts.<n>This study constructs a tailored multispectral remote sensing framework for weed detection to advance precision agriculture practices.<n>Unmanned aerial vehicles were used to collect raw multispectral data from two experimental areas over four years, covering 0.6046 km2 and ground truth annotations were created with GPS-enabled vehicles to manually label weeds and crops.
  arXiv  Detail & Related papers  (2025-02-12T07:01:42Z)
• Fields of The World: A Machine Learning Benchmark Dataset For Global Agricultural Field Boundary Segmentation [12.039406240082515]
  Fields of The World (FTW) is a novel benchmark dataset for agricultural field instance segmentation.<n>FTW is an order of magnitude larger than previous datasets with 70,462 samples.<n>We show that models trained on FTW have better zero-shot and fine-tuning performance in held-out countries.
  arXiv  Detail & Related papers  (2024-09-24T17:20:58Z)
• Evaluating the Role of Training Data Origin for Country-Scale Cropland Mapping in Data-Scarce Regions: A Case Study of Nigeria [0.6249768559720122]
  A key challenge is understanding how the quantity, quality, and proximity of the training data to the target region influences model performance.<n>We evaluate this in Nigeria, using 1,827 manually labelled samples covering the whole country, and subsets of the Geowiki dataset.<n>Results show local data significantly boosts performance, with accuracy gains up to 0.246 (RF) and 0.178 (LSTM)
  arXiv  Detail & Related papers  (2023-12-18T01:23:22Z)
• Small Area Estimation with Random Forests and the LASSO [39.58317527488534]
  This work is motivated by Ghanaian data available from the sixth Living Standard Survey (GLSS) and the 2010 Population and Housing Census. We compare areal-level random forests and LASSO approaches to a frequentist forward variable selection approach and a Bayesian shrinkage method. We find substantial between-area variation, the log consumption areal point estimates showing a 1.3-fold variation across the GAMA region.
  arXiv  Detail & Related papers  (2023-08-29T10:02:10Z)
• HarvestNet: A Dataset for Detecting Smallholder Farming Activity Using Harvest Piles and Remote Sensing [50.4506590177605]
  HarvestNet is a dataset for mapping the presence of farms in the Ethiopian regions of Tigray and Amhara during 2020-2023. We introduce a new approach based on the detection of harvest piles characteristic of many smallholder systems. We conclude that remote sensing of harvest piles can contribute to more timely and accurate cropland assessments in food insecure regions.
  arXiv  Detail & Related papers  (2023-08-23T11:03:28Z)
• Enabling Country-Scale Land Cover Mapping with Meter-Resolution Satellite Imagery [42.70832378336697]
  High-resolution satellite images can provide abundant, detailed spatial information for land cover classification. Few studies have applied high-resolution images to land cover mapping in detailed categories at large scale. We present a large-scale land cover dataset, Five-Billion-Pixels. It contains more than 5 billion labeled pixels of 150 high-resolution Gaofen-2 (4 m) satellite images.
  arXiv  Detail & Related papers  (2022-09-01T21:00:23Z)
• Jalisco's multiclass land cover analysis and classification using a novel lightweight convnet with real-world multispectral and relief data [51.715517570634994]
  We present our novel lightweight (only 89k parameters) Convolution Neural Network (ConvNet) to make LC classification and analysis. In this work, we combine three real-world open data sources to obtain 13 channels. Our embedded analysis anticipates the limited performance in some classes and gives us the opportunity to group the most similar.
  arXiv  Detail & Related papers  (2022-01-26T14:58:51Z)
• Unlocking large-scale crop field delineation in smallholder farming systems with transfer learning and weak supervision [14.98900824231117]
  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. We demonstrate the methods' success in India where we efficiently generated 10,000 new field labels.
  arXiv  Detail & Related papers  (2022-01-13T03:01:06Z)
• A CNN Approach to Simultaneously Count Plants and Detect Plantation-Rows from UAV Imagery [56.10033255997329]
  We propose a novel deep learning method based on a Convolutional Neural Network (CNN) It simultaneously detects and geolocates plantation-rows while counting its plants considering highly-dense plantation configurations. The proposed method achieved state-of-the-art performance for counting and geolocating plants and plant-rows in UAV images from different types of crops.
  arXiv  Detail & Related papers  (2020-12-31T18:51:17Z)
• Farmland Parcel Delineation Using Spatio-temporal Convolutional Networks [77.63950365605845]
  Farm parcel delineation provides cadastral data that is important in developing and managing climate change policies. This data can also be useful for the agricultural insurance sector for assessing compensations following damages associated with extreme weather events. Using satellite imaging can be a scalable and cost effective manner to perform the task of farm parcel delineation.
  arXiv  Detail & Related papers  (2020-04-11T19:49:09Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.