Farmland Extent and Visible Boundary Mapping from 1 m NAIP Imagery Using Residual U-Net and Text-Prompted SAM 3 Refinement
- URL: http://arxiv.org/abs/2607.21881v1
- Date: Fri, 24 Jul 2026 00:44:09 GMT
- Title: Farmland Extent and Visible Boundary Mapping from 1 m NAIP Imagery Using Residual U-Net and Text-Prompted SAM 3 Refinement
- Abstract summary: Agricultural field maps are often proprietary, incomplete, or outdated.<n>This study presents a reproducible workflow for mapping farmland extent and visible boundaries from 1 m NAIP RGB imagery.<n>The product is a semantic farmland-extent layer, not a cadastral parcel map, and supports agricultural monitoring where current field layers are unavailable.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Agricultural field maps are often proprietary, incomplete, or outdated, yet they provide the spatial framework for crop monitoring, production accounting, and land-conversion analysis. This study presents a reproducible workflow for mapping farmland extent and visible boundaries from 1 m NAIP RGB imagery. Thirty-seven scenes spanning open cropland, peri-urban interfaces, semi-arid irrigation geometries, and fragmented mosaics were annotated in CVAT and converted to binary masks. Non-overlapping 256 x 256 patches yielded 5,698 samples, split by source scene into 3,850 training, 770 validation, and 1,078 test patches. A residual U-Net (ResUNet) trained with a Dice-dominant loss, L = 2.5(1 - Dice) + BCE, achieved test accuracy 0.8808, IoU 0.8605, Dice 0.9234, precision 0.8766, and recall 0.9794. A frozen SAM 3 branch prompted with "agricultural farmland field" was fused with ResUNet by logical OR. On selected difficult patches, Dice improved from 0.858 to 0.955 (orchard rows) and from 0.804 to 0.903 (fragmented parcels). Sliding-window stitching produced coherent regional masks (example tile Dice 0.898 and 0.919). The product is a semantic farmland-extent layer, not a cadastral parcel map, and supports agricultural monitoring where current field layers are unavailable.
Related papers
- From Foundation Embeddings to Cropland Maps: Label Efficiency, Temporal Transferability and Independent Human Validation [0.45998296599540095]
Geospatial foundation models provide reusable representations of satellite imagery that support downstream mapping.<n>We evaluate whether annual AlphaEarth embeddings support binary cultivated-versus-non-cultivated mapping in Maine, USA.
arXiv Detail & Related papers (2026-09-15T13:04:35Z) - RoRA: Role-Oriented Regional Allocation for Visual Token Pruning in MLLMs [50.93650157115467]
We present RoRA, a training-free framework that casts visual token pruning as role-oriented regional evidence allocation.<n>Under matched budgets, RoRA consistently outperforms strong training-free baselines across LLaVA and Qwen-VL families.<n>At a 66.7% pruning ratio, RoRA requires only 0.7 ms for token selection and reduces end-to-end inference time by 24.6%.
arXiv Detail & Related papers (2026-08-07T10:39:47Z) - Leak-Free Cross-Validated Stacking with Per-Architecture Calibration for Sand-Boil Segmentation in Earthen Levees [0.0]
Sand boils, points where water seeping beneath an earthen levee re-emerges at the surface, are early warnings of internal erosion.<n>Deep segmentation networks are increasingly used to find them in inspection photographs.<n>Two common ways of working around that scarcity quietly inflate reported accuracy.<n>We present a sand-boil segmentation framework that closes both loopholes.
arXiv Detail & Related papers (2026-07-28T07:21:02Z) - 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) - Amplifying, Not Learning: Fine-Tuned AI Text Detectors Amplify a Pretrained Direction [51.56484100374058]
Text detectors amplify a pretrained typicality axis; they do not construct an AI-vs-human boundary.<n>On raw encoders before any task supervision, projecting onto centroid(AI)-centroid(HC3) achieves NYT-vs-HC3 AUROC 0.806/0.944/0.834 across three architectures.<n>On RoBERTa-base, raw projection exceeds fine-tuning; on RoBERTa-base, full fine-tuning reduces discrimination below raw on both fluent-formal populations tested.
arXiv Detail & Related papers (2026-05-20T19:08:38Z) - Remote SAMsing: From Segment Anything to Segment Everything [0.1749935196721634]
SAM2 produces high-quality zero-shot segmentation on natural images.<n>Applying it to large remote sensing scenes exposes two problems.<n>We propose Remote SAMsing, an open-source pipeline that solves both problems without modifying SAM2 or requiring training data.
arXiv Detail & Related papers (2026-04-30T21:44:03Z) - OPTED: Open Preprocessed Trachoma Eye Dataset Using Zero-Shot SAM 3 Segmentation [0.0]
Trachoma remains the leading infectious cause of blindness worldwide, with Sub-Saharan Africa bearing over 85% of the global burden and Ethiopia alone accounting for more than half of all cases.<n>Yet publicly available preprocessed datasets for automated trachoma classification are scarce, and none originate from the most affected region.<n>We present OPTED, an open-source preprocessed trachoma eye dataset constructed using the Segment Anything Model 3 (SAM 3) for automated region-of-interest extraction.
arXiv Detail & Related papers (2026-03-06T21:07:08Z) - Fields of The World: A Field Guide for Extracting Agricultural Field Boundaries [15.115722799800055]
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.
arXiv Detail & Related papers (2026-02-08T21:32:33Z) - Pix2Geomodel: A Next-Generation Reservoir Geomodeling with Property-to-Property Translation [2.004012818482403]
This study introduces Pix2Geomodel, a novel conditional generative adversarial network (cGAN) framework based on Pix2Pix.<n>It is designed to predict reservoir properties (facies, porosity, permeability, and water saturation) from the Rotliegend reservoir of the Groningen gas field.<n>Results demonstrated high accuracy for facies (PA 0.88, FWIoU 0.85) and water saturation (PA 0.96, FWIoU 0.95), with moderate success for porosity (PA 0.70, FWIoU 0.55) and permeability (PA 0.74, FWIoU 0.60), and robust translation performance
arXiv Detail & Related papers (2025-06-21T15:58:27Z) - 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) - 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) - Rethinking Localization Map: Towards Accurate Object Perception with
Self-Enhancement Maps [78.2581910688094]
This work introduces a novel self-enhancement method to harvest accurate object localization maps and object boundaries with only category labels as supervision.
In particular, the proposed Self-Enhancement Maps achieve the state-of-the-art localization accuracy of 54.88% on ILSVRC.
arXiv Detail & Related papers (2020-06-09T12:35:55Z)
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.