Projeto ForestEyes – Ciência Cidadã e Aprendizado de Máquina na Detecção de Áreas Desmatadas em Florestas Tropicais

• Autor(a) principal: Jordan Rojas Dallaqua, Fernanda Beatriz [UNIFESP]
• Data de Publicação: 2020
• Tipo de documento: Tese
• Idioma: por
• Título da fonte: Repositório Institucional da UNIFESP
• dARK ID: ark:/48912/001300002hjxw
• Texto Completo: https://sucupira.capes.gov.br/sucupira/public/consultas/coleta/trabalhoConclusao/viewTrabalhoConclusao.jsf?popup=true&id_trabalho=10889464; https://hdl.handle.net/11600/64931
• topic: ForestEyes; Ciência Cidadã; Aprendizado De Máquina; Aprendizado Ativo; Detecção De Desmatamento

Tropical forests’ conservation is a current issue of social and ecological relevance due to their important role in the global ecosystem. Tropical forests have a great diversity of fauna and flora, act in the regulation of climate and rainfall, absorb large amounts of carbon dioxide, and serve as a home for countless indigenous peoples. Unfortunately, millions of hectares are deforested and degraded every year, requiring government or private initiative programs to monitor tropical forests. Most of these programs involve the inspection of remote sensing images by specialists, generally counting on the support of computational resources for automatic detection of patterns. This thesis proposes a novel methodology that aims to detect deforestation in tropical forests based on Citizen Science and Machine Learning. With the created methodology, it was possible to develop the prototype of a system called ForestEyes. It uses non-specialized volunteers to inspect images for the target task, interacting with them through an appropriate graphical interface, allocated as a project on the well-known Citizen Science platform Zooniverse. In the performed experiments, six official campaigns have been carried out, receiving more than 81, 000 contributions from 644 distinct volunteers. The results were compared with the official monitoring program for the Brazilian Legal Amazon (PRODES). The volunteers, within the concept of the wisdom of crowds, achieved excellent data labeling when considered an efficient segmentation even for early deforestation detection, which is considered a challenge for any similar system. These labeled data were used as a training set for different Machine Learning techniques, the results of which are comparable and many times even better than the achieved by using the official monitoring program as input data. Active Learning, with a balanced initial training set, obtained results comparable to the classic supervised learning but using smaller amounts of samples. New Active Learning approaches based on the entropy of the classification have been proposed, which have proved to be suitable for some conditions. In this way, the developed methodology shows promise, and with its improvement, it can complement official monitoring systems or be applied to regions where there is a shortage of specialists or of official monitoring programs.