Rotulagem Massiva de Imagens por Ciência Cidadã para Detecção de Desmatamento
Resumo
A ciência cidadã é uma ferramenta essencial para democratizar o conhecimento, pois por meio dela, qualquer pessoa pode auxiliar na análise de dados em larga escala. No contexto ambiental, essa abordagem é essencial para monitorar ameaças como o desmatamento de florestas tropicais. Nesse sentido, este trabalho demonstra como a participação massiva de voluntários pode gerar rótulos confiáveis pela sabedoria das multidões. No projeto ForestEyes, foram classificadas e rolutadas, colaborativamente, 1800 imagens de sensoriamento remoto (com uma média de 875 classificações diárias), para as quais obteve-se 88,78% de acurácia em relação aos dados de referência, evidenciando o potencial da ciência cidadã como apoio na detecção de desmatamento.
Referências
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Brown, K. and Pearce, D. W. (2023). The causes of tropical deforestation: the economic and statistical analysis of factors giving rise to the loss of the tropical forests. Taylor & Francis.
Dallaqua, F. B., Fazenda, Á. L., and Faria, F. A. (2021). Foresteyes project: Conception, enhancements, and challenges. Elsevier FGCS, 124:422–435.
Diniz, Cesar G. et al. (2015). Deter-b: The new amazon near real-time deforestation detection system. IEEE J-STARS, 8(7):3619–3628.
Drusch, M. et al. (2012). Sentinel-2: Esa’s optical high-resolution mission for gmes operational services. Remote Sensing of Environment, 120:25–36.
Fazenda, A. L. and Faria, F. A. (2024). Foresteyes: Citizen scientists and machine learning-assisting rainforest conservation. Communications of the ACM, 67(8):95–96.
INPE (2024). PRODES - Project for Monitoring Deforestation in the Legal Amazon by Satellite. [link]. Acessado em setembro/2024.
Lapola, David et al. (2023). The drivers and impacts of amazon forest degradation. Science, 379(6630):eabp8622.
Mach, E. (1865). Über die Wirkung der räumlichen Vertheilung des Lichtreizes auf die Netzhaut. K. k. Hof-und Staatsdruckerei.
Main-Knorn, M. et al. (2017). Sen2Cor for Sentinel-2. In Image and Signal Processing for Remote Sensing, volume 10427, page 1042704.
Pro, G. F. W., Watcher, F., and Atlases, F. (2023). Global forest watch. Update.
Reis, E. and Guzman, R. (2023). An econometric model of amazon deforestation. In The causes of tropical deforestation, pages 172–191. Routledge.
Resende, Hugo et al. (2024). Sampling strategies based on wisdom of crowds for amazon deforestation detection. In SIBGRAPI, pages 1–6. IEEE.
Roy, David P. et al. (2014). Landsat-8: Science and product vision for terrestrial global change research. Remote sensing of Environment, 145:154–172.
Simpson, Robert et al. (2014). Zooniverse: observing the world’s largest citizen science platform. In ACM WWW, pages 1049–1054.
Artaxo, Paulo wt al. (2022). Tropical and boreal forest atmosphere interactions: A review. Tellus. Series B: Chemical and Physical Meteorology, 74:24–163.
Brown, K. and Pearce, D. W. (2023). The causes of tropical deforestation: the economic and statistical analysis of factors giving rise to the loss of the tropical forests. Taylor & Francis.
Dallaqua, F. B., Fazenda, Á. L., and Faria, F. A. (2021). Foresteyes project: Conception, enhancements, and challenges. Elsevier FGCS, 124:422–435.
Diniz, Cesar G. et al. (2015). Deter-b: The new amazon near real-time deforestation detection system. IEEE J-STARS, 8(7):3619–3628.
Drusch, M. et al. (2012). Sentinel-2: Esa’s optical high-resolution mission for gmes operational services. Remote Sensing of Environment, 120:25–36.
Fazenda, A. L. and Faria, F. A. (2024). Foresteyes: Citizen scientists and machine learning-assisting rainforest conservation. Communications of the ACM, 67(8):95–96.
INPE (2024). PRODES - Project for Monitoring Deforestation in the Legal Amazon by Satellite. [link]. Acessado em setembro/2024.
Lapola, David et al. (2023). The drivers and impacts of amazon forest degradation. Science, 379(6630):eabp8622.
Mach, E. (1865). Über die Wirkung der räumlichen Vertheilung des Lichtreizes auf die Netzhaut. K. k. Hof-und Staatsdruckerei.
Main-Knorn, M. et al. (2017). Sen2Cor for Sentinel-2. In Image and Signal Processing for Remote Sensing, volume 10427, page 1042704.
Pro, G. F. W., Watcher, F., and Atlases, F. (2023). Global forest watch. Update.
Reis, E. and Guzman, R. (2023). An econometric model of amazon deforestation. In The causes of tropical deforestation, pages 172–191. Routledge.
Resende, Hugo et al. (2024). Sampling strategies based on wisdom of crowds for amazon deforestation detection. In SIBGRAPI, pages 1–6. IEEE.
Roy, David P. et al. (2014). Landsat-8: Science and product vision for terrestrial global change research. Remote sensing of Environment, 145:154–172.
Simpson, Robert et al. (2014). Zooniverse: observing the world’s largest citizen science platform. In ACM WWW, pages 1049–1054.
Publicado
20/07/2025
Como Citar
RESENDE, Hugo; FAZENDA, Álvaro L.; FARIA, Fabio A..
Rotulagem Massiva de Imagens por Ciência Cidadã para Detecção de Desmatamento. In: WORKSHOP DE COMPUTAÇÃO APLICADA À GESTÃO DO MEIO AMBIENTE E RECURSOS NATURAIS (WCAMA), 16. , 2025, Maceió/AL.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2025
.
p. 49-58.
ISSN 2595-6124.
DOI: https://doi.org/10.5753/wcama.2025.7514.
