Open Geospatial Engine: A Cloud-based Spatiotemporal Computing Platform

We present a large-scale Flame and Smoke Detection Dataset (FASDD) covering complex and varied fire scenarios. FASDD contains fire, smoke, and confusing non-fire/non-smoke images acquired at different distances (near and far), different scenes (indoor and outdoor), different light intensities (day and night), and from various platforms (surveillance cameras, drones, and satellites). To the best of our knowledge, FASDD is currently the most versatile and comprehensive dataset for fire detection.

We developed an interpretable machine learning model to predict sub-seasonal and near-future wildfire-burned area over African and South American regions. We found strong time-lagged controls (up to 6–8 months) of local climate wetness on burned areas. A skillful use of such time-lagged controls in machine learning models results in highly accurate predictions of wildfire-burned areas; this will also help develop relevant early-warning and management systems for tropical wildfires.

We present a large-scale Flame and Smoke Detection Dataset (FASDD) covering complex and varied fire scenarios. FASDD contains fire, smoke, and confusing non-fire/non-smoke images acquired at different distances (near and far), different scenes (indoor and outdoor), different light intensities (day and night), and from various sensors (surveillance cameras, UAV, and satellites). To the best of our knowledge, it is the largest multimodal dataset for deep learning based fire detection.