Quantifying Damage from Extreme Events Using Satellite Imagery and Neural Networks

We develop models to quantify damage caused by extreme events such as floods, earthquakes, and wildfires using satellite imagery and advanced neural network techniques. Our innovative approach enables damage forecasting even in the absence of historical data. This capability provides stakeholders with valuable insights to proactively prepare for potential threats and mitigate risks associated with climate change.

PREDISM: Pre-Disaster Modelling with CNN ensembles for at-risk communities
V. Anand and Y. Miura. NeurIPS 2021 Tackling Climate Change with Machine Learning Workshop (2021). [DOI]

With rising natural hazards, the machine learning community is focusing more on disaster damage but less on preemptive mitigation. This study presents PREDISM, a model using ResNets and decision trees to predict building-level damage before disasters, improving resource allocation to reduce losses. PREDISM allows damage forecast in the areas without requiring post-hazard damage data.