ForestEyes Project - Citizen Science and Machine Learning to detect deforested areas in tropical forests
Resumo
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.
Referências
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G. Urquhart, W. Chomentowski, D. Skole, and C. Barber, “Tropical deforestation,” 1998.
E. F. Luz et al., “The ForestWatchers: A Citizen Cyberscience Project for Deforestation Monitoring in the Tropics,” Human Computation, vol. 1, pp. 137–145, 2014.
J. R. Coura and J. Borges-Pereira, “Chagas disease: 100 years after its discovery. A systemic review,” Acta tropica, vol. 115, no. 1-2, pp. 5–13, 2010.
A. Afelt, R. Frutos, and C. Devaux, “Bats, coronaviruses, and deforestation: Toward the emergence of novel infectious diseases?” Frontiers in microbiology, vol. 9, p. 702, 2018.
M. D. Soares, R. Santos, N. Vijaykumar, and L. Dutra, “Citizen science-based labeling of imprecisely segmented images: Case study and preliminary results,” in Collaborative Systems-Simpósio Brasileiro de Sistemas Colaborativos (SBSC), 2010 Brazilian Symposium of. IEEE, 2010, pp. 87 – 94.
F. Grey, “Viewpoint: The age of citizen cyberscience,” Cern Courier, vol. 29, 2009.
J. Silvertown, “A new dawn for citizen science,” Trends in Ecology & Evolution, vol. 24, no. 9, pp. 467–471, 2009.
D. Schepaschenko, L. See, M. Lesiv, J.-F. Bastin, D. Mollicone, N.- E. Tsendbazar, L. Bastin, I. McCallum, J. C. L. Bayas, A. Baklanov et al., “Recent advances in forest observation with visual interpretation of very high-resolution imagery,” Surveys in Geophysics, vol. 40, no. 4, pp. 839–862, 2019.
S. Fritz, I. McCallum, C. Schill, C. Perger, L. See, D. Schepaschenko, M. Van der Velde, F. Kraxner, and M. Obersteiner, “Geo-Wiki: An online platform for improving global land cover,” Environmental Modelling & Software, vol. 31, pp. 110–123, 2012.
R. Petersen, L. Pintea, and L. Bourgault, “Forest Watcher Brings Data Straight to Environmental Defenders,” [link], 2017, accessed: 08-07-2020.
A. M. Smith, S. Lynn, and C. J. Lintott, “An introduction to the zooniverse,” in First AAAI conference on human computation and crowdsourcing, 2013.
A. Souza, A. M. Vieira Monteiro, C. Daleles Rennó, C. A. Almeida, D. de Morisson Valeriano, F. Morelli, L. Vinhas, L. E. P. Maurano, M. Adami, M. I. Sobral Escada, M. da Motta, and S. Amaral, “Metodologia Utilizada nos Projetos PRODES e DETER,” São José dos Campos: INPE, 2019.
F. B. Dallaqua, Álvaro L. Fazenda, and F. A. Faria, “ForestEyes Project: Conception, enhancements, and challenges,” Future Generation Computer Systems, vol. 124, pp. 422–435, 2021. [Online]. Available: https://www.sciencedirect.com/science/article/pii/S0167739X21001965
I. Jolliffe, Principal component analysis. Springer, 2011.
R. Achanta, A. Shaji, K. Smith, A. Lucchi, P. Fua, and S. Süsstrunk, “SLIC superpixels compared to state-of-the-art superpixel methods,” IEEE transactions on pattern analysis and machine intelligence, vol. 34, no. 11, pp. 2274–2282, 2012.
E. B. Alexandre, A. S. Chowdhury, A. X. Falcao, and P. A. V. Miranda, “IFT-SLIC: A general framework for superpixel generation based on simple linear iterative clustering and image foresting transform,” in 2015 28th SIBGRAPI Conference on Graphics, Patterns and Images. IEEE, 2015, pp. 337–344.
B. Irving, “maskSLIC: regional superpixel generation with application to local pathology characterisation in medical images,” arXiv preprint arXiv:1606.09518, 2016.
J. S. Arcanjo, E. F. Luz, Á. L. Fazenda, and F. M. Ramos, “Methods for evaluating volunteers’ contributions in a deforestation detection citizen science project,” Future Generation Computer Systems, vol. 56, pp. 550– 557, 2016.
F. B. J. R. Dallaqua, “Projeto ForestEyes - Ciência Cidadã e Aprendizado de Máquina na detecção de áreas desmatadas em florestas tropicais,” Ph.D. dissertation, Universidade Federal de São Paulo. Instituto de Ciência e Tecnologia, 2020.
R. M. Haralick, K. Shanmugam et al., “Textural features for image classification,” IEEE Transactions on systems, man, and cybernetics, no. 6, pp. 610–621, 1973.
T. Ojala, M. Pietikainen, and T. Maenpaa, “Multiresolution gray-scale and rotation invariant texture classification with local binary patterns,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 24, no. 7, pp. 971–987, 2002.
M. J. Swain and D. H. Ballard, “Color indexing,” International journal of computer vision, vol. 7, no. 1, pp. 11–32, 1991.
R. O. Stehling, M. A. Nascimento, and A. X. Falcão, “A compact and efficient image retrieval approach based on border/interior pixel classification,” in Proceedings of the eleventh international conference on Information and knowledge management, 2002, pp. 102–109.
T.-C. Lu and C.-C. Chang, “Color image retrieval technique based on color features and image bitmap,” Information processing & management, vol. 43, no. 2, pp. 461–472, 2007.
D. Tuia, M. Volpi, L. Copa, M. Kanevski, and J. Munoz-Mari, “A survey of active learning algorithms for supervised remote sensing image classification,” IEEE Journal of Selected Topics in Signal Processing, vol. 5, no. 3, pp. 606–617, 2011.
F. B. J. R. Dallaqua, A. L. Fazenda, and F. A. Faria, “Active Learning Approaches for Deforested Area Classification,” in 2018 31st SIBGRAPI Conference on Graphics, Patterns and Images (SIBGRAPI). IEEE, 2018, pp. 48–55.
——, “ForestEyes Project: Can Citizen Scientists Help Rainforests?” in IEEE 15th International Conference on eScience. IEEE, 9 2019, pp. 18–27.
T. M. Kodinariya and P. R. Makwana, “Review on determining number of Cluster in K-Means Clustering,” International Journal, vol. 1, no. 6, pp. 90–95, 2013.
M. Ortega Adarme, R. Queiroz Feitosa, P. Nigri Happ, C. Aparecido De Almeida, and A. Rodrigues Gomes, “Evaluation of Deep Learning Techniques for Deforestation Detection in the Brazilian Amazon and Cerrado Biomes from Remote Sensing Imagery,” Remote Sensing, vol. 12, no. 6, 2020. [Online]. Available: https://www.mdpi.com/2072-4292/12/6/910
F. B. J. R. Dallaqua, A. L. Fazenda, and F. A. Faria, “Aprendizado Ativo com dados de Ciência Cidadã para o monitoramento de florestas tropicais,” in 1ª Escola Regional de Aprendizado de Máquina e Inteligência Artificial de São Paulo (ERAMIA-SP 2020), 2020.
——, “Building data sets for rainforest deforestation detection through a citizen science project,” IEEE Geoscience and Remote Sensing Letters, pp. 1–5, 2020.
L. Van der Maaten and G. Hinton, “Visualizing data using t-SNE.” Journal of machine learning research, vol. 9, no. 11, 2008.
F. B. J. R. Dallaqua, A. L. Fazenda, and F. A. Faria, “Projeto ForestEyes: Uma proposta para aliar ciência cidadã e aprendizado de máquina para monitoramento de desmatamento,” in Proceedings XXI Brazilian Symposium on GeoInformatics (GEOINFO), 2020.
G. Urquhart, W. Chomentowski, D. Skole, and C. Barber, “Tropical deforestation,” 1998.
E. F. Luz et al., “The ForestWatchers: A Citizen Cyberscience Project for Deforestation Monitoring in the Tropics,” Human Computation, vol. 1, pp. 137–145, 2014.
J. R. Coura and J. Borges-Pereira, “Chagas disease: 100 years after its discovery. A systemic review,” Acta tropica, vol. 115, no. 1-2, pp. 5–13, 2010.
A. Afelt, R. Frutos, and C. Devaux, “Bats, coronaviruses, and deforestation: Toward the emergence of novel infectious diseases?” Frontiers in microbiology, vol. 9, p. 702, 2018.
M. D. Soares, R. Santos, N. Vijaykumar, and L. Dutra, “Citizen science-based labeling of imprecisely segmented images: Case study and preliminary results,” in Collaborative Systems-Simpósio Brasileiro de Sistemas Colaborativos (SBSC), 2010 Brazilian Symposium of. IEEE, 2010, pp. 87 – 94.
F. Grey, “Viewpoint: The age of citizen cyberscience,” Cern Courier, vol. 29, 2009.
J. Silvertown, “A new dawn for citizen science,” Trends in Ecology & Evolution, vol. 24, no. 9, pp. 467–471, 2009.
D. Schepaschenko, L. See, M. Lesiv, J.-F. Bastin, D. Mollicone, N.- E. Tsendbazar, L. Bastin, I. McCallum, J. C. L. Bayas, A. Baklanov et al., “Recent advances in forest observation with visual interpretation of very high-resolution imagery,” Surveys in Geophysics, vol. 40, no. 4, pp. 839–862, 2019.
S. Fritz, I. McCallum, C. Schill, C. Perger, L. See, D. Schepaschenko, M. Van der Velde, F. Kraxner, and M. Obersteiner, “Geo-Wiki: An online platform for improving global land cover,” Environmental Modelling & Software, vol. 31, pp. 110–123, 2012.
R. Petersen, L. Pintea, and L. Bourgault, “Forest Watcher Brings Data Straight to Environmental Defenders,” [link], 2017, accessed: 08-07-2020.
A. M. Smith, S. Lynn, and C. J. Lintott, “An introduction to the zooniverse,” in First AAAI conference on human computation and crowdsourcing, 2013.
A. Souza, A. M. Vieira Monteiro, C. Daleles Rennó, C. A. Almeida, D. de Morisson Valeriano, F. Morelli, L. Vinhas, L. E. P. Maurano, M. Adami, M. I. Sobral Escada, M. da Motta, and S. Amaral, “Metodologia Utilizada nos Projetos PRODES e DETER,” São José dos Campos: INPE, 2019.
F. B. Dallaqua, Álvaro L. Fazenda, and F. A. Faria, “ForestEyes Project: Conception, enhancements, and challenges,” Future Generation Computer Systems, vol. 124, pp. 422–435, 2021. [Online]. Available: https://www.sciencedirect.com/science/article/pii/S0167739X21001965
I. Jolliffe, Principal component analysis. Springer, 2011.
R. Achanta, A. Shaji, K. Smith, A. Lucchi, P. Fua, and S. Süsstrunk, “SLIC superpixels compared to state-of-the-art superpixel methods,” IEEE transactions on pattern analysis and machine intelligence, vol. 34, no. 11, pp. 2274–2282, 2012.
E. B. Alexandre, A. S. Chowdhury, A. X. Falcao, and P. A. V. Miranda, “IFT-SLIC: A general framework for superpixel generation based on simple linear iterative clustering and image foresting transform,” in 2015 28th SIBGRAPI Conference on Graphics, Patterns and Images. IEEE, 2015, pp. 337–344.
B. Irving, “maskSLIC: regional superpixel generation with application to local pathology characterisation in medical images,” arXiv preprint arXiv:1606.09518, 2016.
J. S. Arcanjo, E. F. Luz, Á. L. Fazenda, and F. M. Ramos, “Methods for evaluating volunteers’ contributions in a deforestation detection citizen science project,” Future Generation Computer Systems, vol. 56, pp. 550– 557, 2016.
F. B. J. R. Dallaqua, “Projeto ForestEyes - Ciência Cidadã e Aprendizado de Máquina na detecção de áreas desmatadas em florestas tropicais,” Ph.D. dissertation, Universidade Federal de São Paulo. Instituto de Ciência e Tecnologia, 2020.
R. M. Haralick, K. Shanmugam et al., “Textural features for image classification,” IEEE Transactions on systems, man, and cybernetics, no. 6, pp. 610–621, 1973.
T. Ojala, M. Pietikainen, and T. Maenpaa, “Multiresolution gray-scale and rotation invariant texture classification with local binary patterns,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 24, no. 7, pp. 971–987, 2002.
M. J. Swain and D. H. Ballard, “Color indexing,” International journal of computer vision, vol. 7, no. 1, pp. 11–32, 1991.
R. O. Stehling, M. A. Nascimento, and A. X. Falcão, “A compact and efficient image retrieval approach based on border/interior pixel classification,” in Proceedings of the eleventh international conference on Information and knowledge management, 2002, pp. 102–109.
T.-C. Lu and C.-C. Chang, “Color image retrieval technique based on color features and image bitmap,” Information processing & management, vol. 43, no. 2, pp. 461–472, 2007.
D. Tuia, M. Volpi, L. Copa, M. Kanevski, and J. Munoz-Mari, “A survey of active learning algorithms for supervised remote sensing image classification,” IEEE Journal of Selected Topics in Signal Processing, vol. 5, no. 3, pp. 606–617, 2011.
F. B. J. R. Dallaqua, A. L. Fazenda, and F. A. Faria, “Active Learning Approaches for Deforested Area Classification,” in 2018 31st SIBGRAPI Conference on Graphics, Patterns and Images (SIBGRAPI). IEEE, 2018, pp. 48–55.
——, “ForestEyes Project: Can Citizen Scientists Help Rainforests?” in IEEE 15th International Conference on eScience. IEEE, 9 2019, pp. 18–27.
T. M. Kodinariya and P. R. Makwana, “Review on determining number of Cluster in K-Means Clustering,” International Journal, vol. 1, no. 6, pp. 90–95, 2013.
M. Ortega Adarme, R. Queiroz Feitosa, P. Nigri Happ, C. Aparecido De Almeida, and A. Rodrigues Gomes, “Evaluation of Deep Learning Techniques for Deforestation Detection in the Brazilian Amazon and Cerrado Biomes from Remote Sensing Imagery,” Remote Sensing, vol. 12, no. 6, 2020. [Online]. Available: https://www.mdpi.com/2072-4292/12/6/910
F. B. J. R. Dallaqua, A. L. Fazenda, and F. A. Faria, “Aprendizado Ativo com dados de Ciência Cidadã para o monitoramento de florestas tropicais,” in 1ª Escola Regional de Aprendizado de Máquina e Inteligência Artificial de São Paulo (ERAMIA-SP 2020), 2020.
——, “Building data sets for rainforest deforestation detection through a citizen science project,” IEEE Geoscience and Remote Sensing Letters, pp. 1–5, 2020.
L. Van der Maaten and G. Hinton, “Visualizing data using t-SNE.” Journal of machine learning research, vol. 9, no. 11, 2008.
F. B. J. R. Dallaqua, A. L. Fazenda, and F. A. Faria, “Projeto ForestEyes: Uma proposta para aliar ciência cidadã e aprendizado de máquina para monitoramento de desmatamento,” in Proceedings XXI Brazilian Symposium on GeoInformatics (GEOINFO), 2020.
Publicado
18/10/2021
Como Citar
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
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p. 14-20.
DOI: https://doi.org/10.5753/sibgrapi.est.2021.20008.
