Building Data Sets for Rainforest Deforestation Detection Through a Citizen Science Project

• Published in: IEEE geoscience and remote sensing letters Vol. 19; pp. 1 - 5
• Main Authors: Dallaqua, Fernanda Beatriz Jordan Rojas, Faria, Fabio Augusto, Fazenda, Alvaro Luiz
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2022; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Active learning (AL); Artificial satellites; Collections; Conditioned stimulus; Datasets; Deforestation; Detection; Earth; Feasibility studies; Forestry; Image classification; Image segmentation; Learning algorithms; Machine learning; machine learning (ML); Modules; Rainforests; Remote sensing; Segments; supervised learning; Task analysis; Training; Tropical climate; Tropical forests
• DOI: 10.1109/LGRS.2020.3032098
• EISSN: 1558-0571
• ISSN: 1545-598X

Originally, the ForestEyes project aims to detect deforestation in tropical forests based on citizen science (CS) and machine learning (ML) approaches, in which the volunteers analyze and label segments of remote sensing images to build new training sets for creating different classification models. In previous work, only three modules related to CS have been proposed. In this letter, two new modules are created: 1) organization and selection and 2) ML. Therefore, these modules turn the ForestEyes project a more robust system in the deforestation detection task, building high-confidence labeled collections, increasing the monitoring coverage, and decreasing volunteer dependence. Performed experiments show that volunteers create better data sets than those based on automatic PRODES-based approaches, selecting the most relevant samples and discarding noisy segments that might disrupt ML techniques. Finally, the results showed the feasibility of allying CS with ML for rainforest deforestation detection task.