Fogo no Mato: a Real-Time Decision Support Service for Combating Forest Fires
Abstract
Fogo no Mato is a socio-environmental and educational initiative focused on the monitoring, prevention, and understanding of fire outbreaks in rural areas and native vegetation. By integrating remote sensing data from satellites and other sensors, the project aims to combine citizen science and geotechnologies to support wildfire response efforts and guide public policy. The initiative proposes the integration of multiple data sources through techniques in data science, artificial intelligence, and geoprocessing. The project is carried out in partnership with the Military Fire Department of Paraíba, which actively participates in testing and evaluating the tool, contributing suggestions for its continuous improvement.
Keywords:
Forest Fire, Mobile Application, Business Intelligence, Decision-Support Systems, Real-Time Monitoring
References
Arab, S. T., Islam, M. M., Shamsuzzoha, M., Alam, K. F., Muhsin, N., Noguchi, R., and Ahamed, T. (2022). A review of remote sensing applications in agriculture and forestry to establish big data analytics. Remote Sensing Application: Regional Perspectives in Agriculture and Forestry, pages 1–24.
Ghouri, A. M. and Mani, V. (2019). Role of real-time information-sharing through saas: An industry 4.0 perspective. International Journal of Information Management, 49:301–315.
Ibrahim, A. M. A., Abdullah, N. S., and Bahari, M. (2023). Software as a service challenges: A systematic literature review. In Proceedings of the Future Technologies Conference, pages 257–272. Springer.
Jain, P., Coogan, S. C., Subramanian, S. G., Crowley, M., Taylor, S., and Flannigan, M. D. (2020). A review of machine learning applications in wildfire science and management. Environmental Reviews, 28(4):478–505.
Milanés-Batista, C., Tamayo-Yero, H., De Oliveira, D., and Nuñez-Alvarez, J. (2020). Application of business intelligence in studies management of hazard, vulnerability and risk in cuba. In IOP Conference Series: Materials Science and Engineering, volume 844, page 012033. IOP Publishing.
Shah, S. A., Seker, D. Z., Hameed, S., and Draheim, D. (2019). The rising role of big data analytics and iot in disaster management: recent advances, taxonomy and prospects. IEEE Access, 7:54595–54614.
Silva Junior, J. A. d. and Pacheco, A. d. P. (2021). Avaliação de incêndio em ambiente de caatinga a partir de imagens landsat-8, índice de vegetação realçado e análise por componentes principais. Ciência Florestal, 31:417–439.
Subburaj, J., Murugan, K., Keerthana, P., and Aalam, S. S. (2024). Catastropheguard: A guard against natural catastrophes through advances in ai and deep learning technologies. In Internet of Things and AI for Natural Disaster Management and Prediction, pages 28–55. IGI Global.
Ghouri, A. M. and Mani, V. (2019). Role of real-time information-sharing through saas: An industry 4.0 perspective. International Journal of Information Management, 49:301–315.
Ibrahim, A. M. A., Abdullah, N. S., and Bahari, M. (2023). Software as a service challenges: A systematic literature review. In Proceedings of the Future Technologies Conference, pages 257–272. Springer.
Jain, P., Coogan, S. C., Subramanian, S. G., Crowley, M., Taylor, S., and Flannigan, M. D. (2020). A review of machine learning applications in wildfire science and management. Environmental Reviews, 28(4):478–505.
Milanés-Batista, C., Tamayo-Yero, H., De Oliveira, D., and Nuñez-Alvarez, J. (2020). Application of business intelligence in studies management of hazard, vulnerability and risk in cuba. In IOP Conference Series: Materials Science and Engineering, volume 844, page 012033. IOP Publishing.
Shah, S. A., Seker, D. Z., Hameed, S., and Draheim, D. (2019). The rising role of big data analytics and iot in disaster management: recent advances, taxonomy and prospects. IEEE Access, 7:54595–54614.
Silva Junior, J. A. d. and Pacheco, A. d. P. (2021). Avaliação de incêndio em ambiente de caatinga a partir de imagens landsat-8, índice de vegetação realçado e análise por componentes principais. Ciência Florestal, 31:417–439.
Subburaj, J., Murugan, K., Keerthana, P., and Aalam, S. S. (2024). Catastropheguard: A guard against natural catastrophes through advances in ai and deep learning technologies. In Internet of Things and AI for Natural Disaster Management and Prediction, pages 28–55. IGI Global.
Published
2025-09-29
How to Cite
BRASILEIRO ARAÚJO, Tiago; RIBEIRO DE ALMEIDA, Damião; GOMES LOPES FILHO, José; FERREIRA BRASIL, Hicaro; SILVA SOBRAL, Igor; FERREIRA SOARES, Ana Lícia; GOMES SALES, Anna Beatriz.
Fogo no Mato: a Real-Time Decision Support Service for Combating Forest Fires. In: DEMOS AND APPLICATIONS - BRAZILIAN SYMPOSIUM ON DATABASES (SBBD), 40. , 2025, Fortaleza/CE.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2025
.
p. 88-93.
DOI: https://doi.org/10.5753/sbbd_estendido.2025.247660.
