Capitalizing the Predictive Potential of Machine Learning to Detect Various Fire Types Using NASA’s MODIS Satellite Data for the Mediterranean Basin - read online conference paper summary

Aug 5, 2026

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This study investigates machine learning for classifying different fire types using NASA’s FIRMS MODIS satellite data for the Mediterranean basin. Training uses data from 2019–2021, then validated on 2022 data using XGBoost and Random Forest. Results show XGBoost delivers higher predictive accuracy than Random Forest, reaching an overall F1 score above 95% and macro F1 scores of 84% across fire types. The work supports near real-time wildfire monitoring and response through more accurate fire type forecasting.

LanguageEnglish

CategoryResearch & Report

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Pages5

Reading Time13 min

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Abstract

Introduction

Remote Sensing and Fire Monitoring

NASA EOS and MODIS overview

NASA FIRMS and MODIS data attributes

Capitalizing the Predictive Potential of Machine Learning to Detect Various Fire Types Using NASA’s MODIS Satellite Data for the Mediterranean Basin Nima Kamali Lassem Department of Information Systems and Technologies, Bilkent University, Türkiye Obai Mohamed Hisham Abdelmohsen Gaafar Department of Computer Science, University of Milan, Italy Seyid Amjad Ali Department of Information Systems and Technologies, Bilkent University, Türkiye ABSTRACT This study investigates the realm of machine learning for the classification of different fire types using NASA’s FIRMS MODIS satellite data for the Mediterranean basin. Concentrating on the Mediterranean basin and utilizing data spanning from 2019 to 2021 for model training, XGBoost and Random Forest models were subsequently validated for the 2022 data. The findings distinctly illustrate XGBoost’s superior predictive precision as compared to Random Forest by showcasing an impressive overall F1 score surpassing 95% and 84% macro F1 score across various fire types. This study emphasizes the prospect of machine learning to improve worldwide wildfire monitoring and response by providing exact, real-time fire type forecasts. CCS CONCEPTS • Computing methodologies; • Machine learning; • Machine learning approaches; • Classification and regression trees; KEYWORDS Wildfire prediction, MODIS, Mediterranean basin, XGBoost, Random Forest ACM Reference Format: Nima Kamali Lassem, Obai Mohamed Hisham Abdelmohsen Gaafar, and Seyid Amjad Ali. 2023. Capitalizing the Predictive Potential of Machine Learning to Detect Various Fire Types Using NASA’s MODIS Satellite Data for the Mediterranean Basin. In 2023 The 7th International Conference on Advances in Artificial Intelligence (ICAAI) (ICAAI 2023), October 13–15, 2023, Istanbul, Turkiye. ACM, New York, NY, USA, 5 pages. [https://doi.org/10.1145/3633598.3633603](https://doi.org/10.1145/3633598.3633603) 1 INTRODUCTION In the face of intensifying forest fire occurrences, the accurate monitoring of these events has become critical. While forest fires are integral to our ecosystems, their growing harmfulness poses serious threats to infrastructure, human settlements, and biodiversity. Beyond immediate devastation, wildfires can disrupt water bodies and have far-reaching consequences on the environment, as noted in ’Wildfire’s Impact on Our Environment’ [1] . Furthermore, the This work is licensed under a Creative Commons Attribution International 4.0 License. ICAAI 2023, October 13–15, 2023, Istanbul, Turkiye © 2023 Copyright held by the owner/author(s) . ACM ISBN 979-8-4007-0898-5/23/10 . [https://doi.org/10.1145/3633598.3633603](https://doi.org/10.1145/3633598.3633603) hazardous pollutants in wildfire smoke, as underlined by WHO, have a direct influence on public health [2] . NASA’s Doug Morton anticipates a rise in forest fires across the US by 2050 [3], emphasizing the need for effective fire monitoring. Recently, due to the aforementioned reasons, there has been a major upsurge in the number of studies that are being conducted by the research community to highlight this serious issue. Authors in [4, 5] have provided a detailed review regarding forest fires and some machine learning based algorithms that can be used for their detection. Similarly, Gözde et al., [6] used a dataset1 that consists of fires in a national park in northern Portugal between January 2000 and December 2003 to compare the performance of various machine learning algorithms. Amid severe challenges posed by forest fires, systems like NASA’s Fire Information for Resource Management System (FIRMS) have revolutionized disaster management. Yet, accurately predicting fire types remains crucial for assessing threats and aiding rescue services. This paper highlights the importance of predicting fire types observed by FIRMS MODIS satellites by using novel machine-learning methods. By addressing gaps in fire type prediction, we seek to enhance g

FAQs

Which machine learning models are evaluated for fire type classification?

The study evaluates XGBoost and Random Forest. Both are trained on 2019–2021 data and validated on 2022 data.

What satellite dataset and region are used in this research?

It uses NASA’s FIRMS MODIS satellite data, focused on the Mediterranean basin.

How does XGBoost performance compare with Random Forest?

XGBoost outperforms Random Forest, achieving an overall F1 score above 95% and macro F1 around 84% across various fire types.

Aug 5, 2026

Upload by Levi