TY - GEN
T1 - M-BurnScar
T2 - 22nd International Joint Conference on Computer Science and Software Engineering
AU - Vorapatratorn, Surapol
AU - Thongsibsong, Nontawat
AU - Deeudomchan, Kampanat
AU - Boongoen, Tossapon
AU - Iam-On, Natthakan
AU - Yodcum, Jittisak
N1 - Publisher Copyright:
© 2025 IEEE.
PY - 2025/11/2
Y1 - 2025/11/2
N2 - The M-BurnScar platform is an AI-based system designed for the real-time detection of burnt scars and forest fire risk assessment using Sentinel-2 satellite imagery. This platform integrates advanced machine learning models, including Random Forest, LightGBM, K-Nearest Neighbors (KNN), Gaussian Naive Bayes, Support Vector Machines (SVM), Multi-Layer Perceptron (MLP), and Tabular Neural Networks (TabNN), to accurately detect burnt areas and predict fire risks across multiple regions in Southeast Asia. The Random Forest model achieved the highest performance with an accuracy of 97.24%, making it the most effective for burnt scar detection. The system processes large-scale satellite imagery to generate interactive maps that highlight burn severity levels, hotspot locations, and air quality (PM2.5) data. These features enable government agencies, environmental organizations, and local communities to make data-driven decisions regarding fire prevention and management. Additionally, the platform provides APIs for real-time data integration, allowing external systems to efficiently access and analyze the data. Training workshops were conducted with stakeholders to ensure effective usage, and feedback indicated strong support for the platform's simplicity, real-time capabilities, and potential to enhance fire risk management strategies.
AB - The M-BurnScar platform is an AI-based system designed for the real-time detection of burnt scars and forest fire risk assessment using Sentinel-2 satellite imagery. This platform integrates advanced machine learning models, including Random Forest, LightGBM, K-Nearest Neighbors (KNN), Gaussian Naive Bayes, Support Vector Machines (SVM), Multi-Layer Perceptron (MLP), and Tabular Neural Networks (TabNN), to accurately detect burnt areas and predict fire risks across multiple regions in Southeast Asia. The Random Forest model achieved the highest performance with an accuracy of 97.24%, making it the most effective for burnt scar detection. The system processes large-scale satellite imagery to generate interactive maps that highlight burn severity levels, hotspot locations, and air quality (PM2.5) data. These features enable government agencies, environmental organizations, and local communities to make data-driven decisions regarding fire prevention and management. Additionally, the platform provides APIs for real-time data integration, allowing external systems to efficiently access and analyze the data. Training workshops were conducted with stakeholders to ensure effective usage, and feedback indicated strong support for the platform's simplicity, real-time capabilities, and potential to enhance fire risk management strategies.
KW - burnt scar profiling
KW - fire risk prediction
KW - forest fire detection
KW - machine learning
KW - sentinel-2 satellite
UR - https://www.scopus.com/pages/publications/105032496597
U2 - 10.1109/jcsse67377.2025.11297912
DO - 10.1109/jcsse67377.2025.11297912
M3 - Conference Proceeding (ISBN)
T3 - JCSSE 2025 - 22nd International Joint Conference on Computer Science and Software Engineering
SP - 14
EP - 21
BT - JCSSE 2025 - 22nd International Joint Conference on Computer Science and Software Engineering
PB - Institute of Electrical and Electronics Engineers
Y2 - 2 November 2025 through 5 November 2025
ER -