# Detecting Cadastral Boundary from Satellite Images Using U-Net model

Source: [Detecting Cadastral Boundary from Satellite Images Using U-Net model](<https://www.sota2.com/research/paper/68fa76fd1e209131cea7047d>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-26T04:07:06.968Z

Authors: Neda Rahimpour Anaraki, Maryam Tahmasbi, Saeed Reza Kheradpisheh

Published: 2025-02-16T09:04:37.000Z

## Abstract

Finding the cadastral boundaries of farmlands is a crucial concern for land administration. Therefore, using deep learning methods to expedite and simplify the extraction of cadastral boundaries from satellite and unmanned aerial vehicle (UAV) images is critical. In this paper, we employ transfer learning to train a U-Net model with a ResNet34 backbone to detect cadastral boundaries through three-class semantic segmentation: "boundary", "field", and "background". We evaluate the performance on two satellite images from farmlands in Iran using "precision", "recall", and "F-score", achieving high values of 88%, 75%, and 81%, respectively, which indicate promising results.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2502.11044>)

[PDF](<https://arxiv.org/pdf/2502.11044.pdf>)