ForestEyes Project: Conception, enhancements, and challenges

Autor(es)

Dallaqua, Fernanda B. J. R. ^[1] ; Fazenda, alvaro L. ^[1] ; Faria, Fabio A. ^[1]

Afiliação do(s) autor(es)

^[1] Univ Fed Sao Paulo, Inst Ciencia & Tecnol, Sao Jose Dos Campos - Brazil

• Tipo de documento: Artigo Científico
• Fonte: FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE; v. 124, p. 422-435, NOV 2021.
• Citações Web of Science: 0

Resumo

Rainforests play an important role in the global ecosystem. However, significant regions of them are facing deforestation and degradation due to several reasons. Diverse government and private initiatives were created to monitor and alert for deforestation increases from remote sensing images, using different ways to deal with the notable amount of generated data. Citizen Science projects can also be used to reach the same goal. Citizen Science consists of scientific research involving nonprofessional volunteers for analyzing, collecting data, and using their computational resources to outcome advancements in science and to increase the public's understanding of problems in specific knowledge areas such as astronomy, chemistry, mathematics, and physics. In this sense, this work presents a Citizen Science project called ForestEyes, which uses volunteer's answers through the analysis and classification of remote sensing images to monitor deforestation regions in rainforests. To evaluate the quality of those answers, different campaigns/workflows were launched using remote sensing images from Brazilian Legal Amazon and their results were compared to an official groundtruth from the Amazon Deforestation Monitoring Project PRODES. In this work, the first two workflows that enclose the State of Rondonia in the years 2013 and 2016 received more than 35,000 answers from 383 volunteers in the 2,050 created tasks in only two and a half weeks after their launch. For the other four workflows, even enclosing the same area (Rondonia) and different setups (e.g., image segmentation method, image spatial resolution, and detection target), they received 51,035 volunteers' answers gathered from 281 volunteers in 3,358 tasks. In the performed experiments, it was possible to observe that the volunteers achieved satisfactory overall accuracy, higher than 75%, in the classification of forestation and non-forestation areas using the ForestEyes project. Furthermore, considering an efficient segmentation and a better image spatial resolution, they achieved almost 66% accuracy in the classification of recent deforestation, which is a great challenge to overcome. Therefore, these results show that Citizen Science might be a powerful tool in monitoring deforestation regions in rainforests as well as in obtaining high-quality labeled data. (C) 2021 Elsevier B.V. All rights reserved. (AU)

• Processo FAPESP: 15/24485-9 - Internet do futuro aplicada a cidades inteligentes
• Beneficiário: Fabio Kon
• Modalidade de apoio: Auxílio à Pesquisa - Temático
• Processo FAPESP: 14/50937-1 - INCT 2014: da Internet do Futuro
• Beneficiário: Fabio Kon
• Modalidade de apoio: Auxílio à Pesquisa - Temático