ForestEyes Project - Citizen Science and Machine Learning to detect deforested areas in tropical forests

  • Fernanda B. J. R. Dallaqua UNIFESP
  • Fabio A. Faria UNIFESP
  • Álvaro L. Fazenda UNIFESP


The conservation of tropical forests is urgent and necessary due to the important role they play in the global ecosystem. Several governmental and private initiatives were created to detect deforestation in tropical forests through analyses of remote sensing images, which demands skilled labor and different ways to deal with a great amount of data. Citizen Science could be used to mitigate these challenges, as it consists of nonspecialized volunteers collecting, analyzing, and classifying data to solve technical and scientific problems. In this sense, this work proposes the ForestEyes Project 1, which aims to combine citizen science and machine learning for deforestation detection. The volunteers classify remote sensing images, and these data are used as the training set for classification algorithms. The volunteers classified more than 5, 000 tasks from remote sensing images of the Brazilian Legal Amazon, and the results were compared to a groundtruth from the Amazon Deforestation Monitoring Project PRODES. The volunteers achieved good labeling of the remote sensing data, even for recent deforestation tasks, building high-confidence labeled collections as they selected the most relevant samples and discarded noisy segments that might disrupt machine learning techniques. Finally, the proposed methodology is promising, and with improvements, it could be able to generate complementary information to official monitoring programs or even generate information for areas not yet monitored.


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DALLAQUA, Fernanda B. J. R.; FARIA, Fabio A.; FAZENDA, Álvaro L.. ForestEyes Project - Citizen Science and Machine Learning to detect deforested areas in tropical forests. In: WORKSHOP DE TESES E DISSERTAÇÕES - CONFERENCE ON GRAPHICS, PATTERNS AND IMAGES (SIBGRAPI), 34. , 2021, Online. Anais [...]. Porto Alegre: Sociedade Brasileira de Computação, 2021 . p. 14-20. DOI: