arXiv CS
open access
Comparing a gradient boosting algorithm to the GOES FDC for wildfire detection
Wildfires pose severe risks to human life, ecosystems, and property. This study presents a machine learning approach for wildfire detection from GOES ABI imagery. A CatBoost model was trained on a large dataset with thousands of ABI images and over 300,000 matching VIIRS fire detections. An evaluati
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- Comparing a gradient boosting algorithm to the GOES FDC for wildfire detection#1157342
- The Damage Assessment of 2019 Eurobodalla Shire Wildfire to the Endangered Ecological Communities Using Change Detection and Object-Based Classification#287684
- Towards Acoustic Bioindicator Integration in AI-Based Wildfire Monitoring: A Systematic Review#1133268
- Mapping U.S. Surface Methane With Artificial Neural Networks#390091
- PyroStack: A Multi-Band Spatio-Temporal Sub-Daily Dataset for Wildfires in the United States#1131157
Record · ID 1161050
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive