Unsupervised Image Classification Algorithms Applied to Fire-Prone Area Detection

I. Rahimi, I. Rahimi, L. Duarte, L. Duarte, A. C. Teodoro, A. C. Teodoro

2025

Abstract

Remote sensing data has become critical in identifying fire-prone areas, providing essential insights through satellite imagery and various geospatial inputs. These data sources allow for real-time monitoring, mapping fire susceptibility, and assessing factors such as vegetation, fuel moisture, land use, and environmental conditions. Numerous supervised and unsupervised models combined with remote sensing data have shown great potential in predicting fire-prone regions, offering accurate and timely information for early warning systems and resource allocation. This study focuses on applying two unsupervised methods—PCA, and K-means—using inputs like Sentinel-2 imagery, elevation, and the Zagros Grass Index (ZGI) to identify fire-prone areas in the Kurdo-Zagrosian forests, an area increasingly vulnerable to wildfires. Among the two methods evaluated, PCA demonstrated superior performance in predicting fire-susceptible areas, accurately classifying 80% of the burned regions from 2021 to 2023 as moderate to high-risk zones.

Paper Citation

in Harvard Style

Rahimi I., Duarte L. and Teodoro A. (2025). Unsupervised Image Classification Algorithms Applied to Fire-Prone Area Detection. In Proceedings of the 11th International Conference on Geographical Information Systems Theory, Applications and Management - Volume 1: GISTAM; ISBN 978-989-758-741-2, SciTePress, pages 136-141. DOI: 10.5220/0013201800003935

in Bibtex Style

@conference{gistam25,
author={I. Rahimi and L. Duarte and A. Teodoro},
title={Unsupervised Image Classification Algorithms Applied to Fire-Prone Area Detection},
booktitle={Proceedings of the 11th International Conference on Geographical Information Systems Theory, Applications and Management - Volume 1: GISTAM},
year={2025},
pages={136-141},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013201800003935},
isbn={978-989-758-741-2},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 11th International Conference on Geographical Information Systems Theory, Applications and Management - Volume 1: GISTAM
TI - Unsupervised Image Classification Algorithms Applied to Fire-Prone Area Detection
SN - 978-989-758-741-2
AU - Rahimi I.
AU - Duarte L.
AU - Teodoro A.
PY - 2025
SP - 136
EP - 141
DO - 10.5220/0013201800003935
PB - SciTePress