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multi-indicator-analysis

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tk-yasuno / wildfire-alphaearth-sentinel

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This MVP demonstrates a multi-indicator, high-reliability wildfire detection framework that surpasses conventional approaches. By combining Earth observation with intelligent vector analytics, it opens pathways to operational-scale environmental monitoring.

machine-learning remote-sensing feature-engineering satellite-data environmental-monitoring earth-observation geospatial-analysis early-warning-systems wildfire-detection vector-analytics fire-monitoring environmental-ai spatial-data-processing fire-analysis multi-indicator-analysis reliability-ai operational-scale-ai
  • Updated Aug 1, 2025
  • Jupyter Notebook

tk-yasuno / wildfire-vlm-modis

Star 0

The MVP provides automated fire risk assessment by extracting wildfire indicators—such as smoke, flame patterns, and thermal anomalies—from imagery, and presenting them in structured natural language analysis.

machine-learning remote-sensing satellite-imagery environmental-monitoring earth-observation natural-language-analysis smoke-detection wildfire-detection image-feature-extraction environmental-ai structured-report-generation fire-analysis multi-indicator-analysis wildfire-risk-assessment thermal-anomalies flame-pattern-analysis
  • Updated Jul 28, 2025
  • Jupyter Notebook

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