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Communication Dans Un Congrès Année : 2023

Detection and semantic description of changes in Earth Observation Time Series data

Résumé

The overexploitation of natural resources and pollution are urgent concerns affecting the Earth's global system. Earth Observation (EO) data can be used to analyze the environmental impact of human activities. However, extracting meaningful insights from EO time series data requires domain expertise. In this position paper, we propose a methodology to improve the accessibility and understanding of environmental trends for a wide audience. Using Machine Learning (ML) technologies, we detect and describe in the Semantic Web (SW) changes in EO time series.
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Dates et versions

hal-04357877 , version 1 (21-12-2023)

Identifiants

  • HAL Id : hal-04357877 , version 1

Citer

Daniela F. Milon-Flores, Camille Bernard, Jérôme Gensel, Gregory Giuliani. Detection and semantic description of changes in Earth Observation Time Series data. Workshop MACLEAN 2023 on Machine Learning for Earth Observation, Sep 2023, Torino, Italy, Italy. ⟨hal-04357877⟩
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