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

Author

Listed:
  • 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)

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.

Suggested Citation

  • Vinícius L. S. Gino & Rogério G. Negri & Felipe N. Souza & Erivaldo A. Silva & Adriano Bressane & Tatiana S. G. Mendes & Wallace Casaca, 2023. "Integrating Unsupervised Machine Intelligence and Anomaly Detection for Spatio-Temporal Dynamic Mapping Using Remote Sensing Image Series," Sustainability, MDPI, vol. 15(6), pages 1-19, March.
  • Handle: RePEc:gam:jsusta:v:15:y:2023:i:6:p:4725-:d:1089976
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    Citations

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    Cited by:

    1. Ming Chang & Shuying Meng & Zifan Zhang & Ruiguo Wang & Chao Yin & Yuxia Zhao & Yi Zhou, 2023. "Analysis of Eco-Environmental Quality and Driving Forces in Opencast Coal Mining Area Based on GWANN Model: A Case Study in Shengli Coalfield, China," Sustainability, MDPI, vol. 15(13), pages 1-20, July.
    2. Julia Rodrigues & Mauricio Araújo Dias & Rogério Negri & Sardar Muhammad Hussain & Wallace Casaca, 2024. "A Robust Dual-Mode Machine Learning Framework for Classifying Deforestation Patterns in Amazon Native Lands," Land, MDPI, vol. 13(9), pages 1-19, September.

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