Integrating Unsupervised Machine Intelligence and Anomaly Detection for Spatio-Temporal Dynamic Mapping Using Remote Sensing Image Series

Vinícius L. S. Gino, Rogério G. Negri (), Felipe N. Souza, Erivaldo A. Silva, Adriano Bressane, Tatiana S. G. Mendes and Wallace Casaca
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Vinícius L. S. Gino: Science and Technology Institute (ICT), São Paulo State University (UNESP), São José dos Campos 12245-000, Brazil
Rogério G. Negri: Science and Technology Institute (ICT), São Paulo State University (UNESP), São José dos Campos 12245-000, Brazil
Felipe N. Souza: Science and Technology Institute (ICT), São Paulo State University (UNESP), São José dos Campos 12245-000, Brazil
Erivaldo A. Silva: Faculty of Science and Technology (FCT), São Paulo State University (UNESP), Presidente Prudente 19060-080, Brazil
Adriano Bressane: Science and Technology Institute (ICT), São Paulo State University (UNESP), São José dos Campos 12245-000, Brazil
Tatiana S. G. Mendes: Science and Technology Institute (ICT), São Paulo State University (UNESP), São José dos Campos 12245-000, Brazil
Wallace Casaca: Institute of Biosciences, Letters and Exact Sciences (IBILCE), São Paulo State University (UNESP), São José do Rio Preto 15054-000, Brazil

Sustainability, 2023, vol. 15, issue 6, 1-19

Abstract: The synergistic use of remote sensing and unsupervised machine learning has emerged as a potential tool for addressing a variety of environmental monitoring applications, such as detecting disaster-affected areas and deforestation. This paper proposes a new machine-intelligent approach to detecting and characterizing spatio-temporal changes on the Earth’s surface by using remote sensing data and unsupervised learning. Our framework was designed to be fully automatic by integrating unsupervised anomaly detection models, remote sensing image series, and open data extracted from the Google Earth Engine platform. The methodology was evaluated by taking both simulated and real-world environmental data acquired from several imaging sensors, including Landsat-8 OLI, Sentinel-2 MSI, and Terra MODIS. The experimental results were measured with the kappa and F1-score metrics, and they indicated an assertiveness level of 0.85 for the change detection task, demonstrating the accuracy and robustness of the proposed approach when addressing distinct environmental monitoring applications, including the detection of disaster-affected areas and deforestation mapping.

Keywords: anomaly detection; time series; landscape dynamics; framework (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
References: View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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