Building Data Sets for Rainforest Deforestation Detection Through a Citizen Science Project - Dallaqua, Fernanda Jordan Rojas; Faria, Fabio Augusto; Fazenda, Álvaro Luis (Anais da Conference on Graphics, Patterns and Images (SIBGRAPI); 2020: Anais da XXXIII Conference on Graphics, Patterns and Images, 2020)
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.