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Deforestation via Unsupervised Computing Learning: Modeling and Applications in Preservation Parks of the Amazon Biome

Grant number: 22/13665-0
Support Opportunities:Scholarships in Brazil - Scientific Initiation
Effective date (Start): January 01, 2023
Effective date (End): December 31, 2023
Field of knowledge:Physical Sciences and Mathematics - Computer Science - Computing Methodologies and Techniques
Principal Investigator:Wallace Correa de Oliveira Casaca
Grantee:Júlia Rodrigues Marques do Nascimento
Host Institution: Instituto de Biociências, Letras e Ciências Exatas (IBILCE). Universidade Estadual Paulista (UNESP). Campus de São José do Rio Preto. São José do Rio Preto , SP, Brazil

Abstract

Computational Learning-inspired techniques has contributed effectively and transdiciplinarily in Computational Vision and Pattern Recognition applications, such as deforstation mapping in the Amazon biome. In this context, data-driven algorithms have promoted the development of innovative and accurate tools for the detection of deforested areas, by analyzing time series of remotely sensed images while gaining knowledge about the current state of the target region and its historical vegetation cover data. Considering the above-discussed issue, this research focuses on the study and implementation of a Computational Learning-based methodology to classify deforested areas in the Brazilian Amazon. Our goal is to study, assess and investigate time-series of deforestation data concerning environmental preservation units in São Félix do Xingu, Brazil's sixth largest municipality in Brazil, located in the state of Pará. The proposed data-driven solution combine the use of fully unsupervised Machine Learning techniques for dealing with image time-series, i.e., the One-Class Support Vector Machine and Isolation Forest methods, as well as Anomaly Detection models and Spectral Indices so as to properly identify and classify deforested areas. Besides the robustness and accuracy provided when applying Machine Learning, the proposed methodology has been designed to work as a user-independent tool in aspects such as the non-mandatory use of reference data and the capability to access integrated, up-to-date catalogs from the Google Earth Engine open access platform.

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