Scholarlink

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.

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Detection and Semantic Description of Changes in Earth Observation Time Series Data

  • 会议论文

  • 发表日期：

    2025年

查看全文
  • Daniela F. Milon-Flores,
  • Camille Bernard,
  • Jérôme Gensel,
  • Gregory Giuliani

摘要

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.