Nighttime Combustion Detection from NASA’s Black Marble
Description
This use case focuses on monitoring nighttime combustion over land from NASA’s Black Marble product and has demonstrated the detection of fires and gas flaring mapping by jointly using both thermal and light emission properties of these event.
Detailed example
daily detections, ensemble output.
AI / analytics pattern
Classical/Predictive Machine Learning: Models trained on data to make predictions or classifications based on identified patterns or relationships.
Automation level / stage
a) Pre-deployment – The use case is in a development or acquisition status.
Expected benefit
The ML capability first demonstrates the generation of training samples using anomaly detection, tackling the challenge of training data generation in Earth Sciences. This is followed by classification of combustion and background across diverse geographic reasons and seasons and produces detections from a suite of baseline ML approaches including per-band outlier detections, fully-connected neural networks and Siamese Networks trained with triplet and contrastive loss. The detections are then jointly considered to create a high confidence ensemble detection layer.
Controls / human review
ATO: Not reported; PIA: Not published