arXiv Open Access 2025

Detecting Cadastral Boundary from Satellite Images Using U-Net model

Neda Rahimpour Anaraki Maryam Tahmasbi Saeed Reza Kheradpisheh

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Abstrak

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.

Topik & Kata Kunci

cs.CV cs.LG

Penulis (3)

Neda Rahimpour Anaraki

Maryam Tahmasbi

Saeed Reza Kheradpisheh

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APA MLA BibTeX

Anaraki, N.R., Tahmasbi, M., Kheradpisheh, S.R. (2025). Detecting Cadastral Boundary from Satellite Images Using U-Net model. https://arxiv.org/abs/2502.11044

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Informasi Jurnal

Tahun Terbit: 2025
Bahasa: en
Sumber Database: arXiv
Akses: Open Access ✓