Enhancing Forest Fire Detection and Monitoring Through Satellite Image Recognition: A Comparative Analysis of Classification Algorithms Using Sentinel-2 Data

• Tipo: Artigo em Livro de Atas de Conferência Internacional
• Data: 2024

Autores

Brito, T (Autor) Outra; Pereira, AI (Autor) Outra; Paulo Gomes da Costa (Autor) FEUP; Lima, J (Autor) Outra

Ata de Conferência Internacional

Título: OPTIMIZATION, LEARNING ALGORITHMS AND APPLICATIONS, PT II, OL2A 2023

Páginas: 78-92

3rd International Conference on Optimization, Learning Algorithms and Applications (OL2A)

Ponta Delgada, PORTUGAL, SEP 27-29, 2023

Indexação

ISI Web of Knowledge - 0 Citações

Scopus - 0 Citações

Outras Informações

ID Authenticus: P-00Z-WZA

DOI: 10.1007/978-3-031-53036-4_6

Abstract (EN): Worldwide, forests have been harassed by fire in recent years. Either by human intervention or other reasons, the history of the burned area is increasing considerably, harming fauna and flora. It is essential to detect an early ignition for fire-fighting authorities can act quickly, decreasing the impact of forest damage impacts. The proposed system aims to improve nature monitoring and improve the existing surveillance systems through satellite image recognition. The soil recognition via satellite images can determine the sensor modules' best position and provide crucial input information for artificial intelligence-based systems. For this, satellite images from the Sentinel-2 program are used to generate forest density maps as updated as possible. Four classification algorithms make the Tree Cover Density (TCD) map, consisting of the Gaussian Mixture Model (GMM), Random Forest (RF), Support Vector Machine (SVM), and K-Nearest Neighbors (K-NN), which identify zones by training known regions. The results demonstrate a comparison between the algorithms through their performance in recognizing the forest, grass, pavement, and water areas by Sentinel-2 images.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 15