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tk-yasuno / wildfire-vlm-modis

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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

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 / north-america-fire-analysis-v1-4-3

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Large-scale fire detection analysis using NASA FIRMS data feat: Add dynamic region support for North America case study in v1.4.3 - Enabled flexible geospatial parameterization for wildfire analysis - Updated preprocessing pipeline to support North America-specific satellite data formats.

data-engineering remote-sensing satellite-data environmental-monitoring earth-observation geospatial-analysis north-america wildfire-detection preprocessing-pipeline fire-monitoring nasa-firms environmental-ai large-scale-processing fire-analysis dynamic-region-support geospatial-parameterization regional-adaptation
  • Updated Sep 15, 2025
  • Python

tk-yasuno / africa-fire-analysis-v1-4-4

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Large-scale fire detection analysis using NASA FIRMS data feat: Add dynamic region support for Africa case study in v1.4.4 - Enabled flexible geospatial parameterization for wildfire analysis - Updated preprocessing pipeline to support Africa-specific satellite data formats.

data-engineering remote-sensing africa satellite-data environmental-monitoring earth-observation geospatial-analysis wildfire-detection preprocessing-pipeline fire-monitoring nasa-firms environmental-ai large-scale-processing fire-analysis dynamic-region-support geospatial-parameterization regional-adaptation
  • Updated Sep 15, 2025
  • Python

tk-yasuno / area-fire-analysis-v1-4

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Large-scale fire detection analysis using NASA FIRMS data. feat: Add dynamic region support for South America case study in v1-4_area - Enabled flexible geospatial parameterization for wildfire analysis - Updated preprocessing pipeline to accommodate South American satellite data formats.

data-engineering remote-sensing satellite-data environmental-monitoring earth-observation south-america geospatial-analysis wildfire-detection preprocessing-pipeline fire-monitoring nasa-firms environmental-ai large-scale-processing fire-analysis dynamic-region-support geospatial-parameterization regional-adaptation
  • Updated Sep 15, 2025
  • Python

tk-yasuno / eu-fire-analysis-v1-4-2

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Large-scale fire detection analysis using NASA FIRMS data feat: Add dynamic region support for EU case study in v1.4.2 - Enabled flexible geospatial parameterization for wildfire analysis - Updated preprocessing pipeline to support EU-specific satellite data formats.

eu data-engineering remote-sensing satellite-data environmental-monitoring earth-observation geospatial-analysis wildfire-detection preprocessing-pipeline fire-monitoring nasa-firms environmental-ai large-scale-processing fire-analysis dynamic-region-support geospatial-parameterization regional-adaptation
  • Updated Sep 15, 2025
  • Python

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