Machine vision models are instrumental in identifying damage caused by natural disasters

14 de October de 2021by BoldThemes
We have seen it in the press, when they report on the buildings evacuated due to the eruption of the Cumbre Vieja volcano on the island of La Palma, Spain. And also when estimating the damage caused by the natural fires in California last summer.


Computer vision (CV) and specialized artificial intelligence (AI) models help us to detect the affected elements, classify the damage, calculate the hectares of forest destroyed and the structural elements damaged, among many other things. Emergency crews, authorities, landowners and the general public benefit from the capabilities that new technology offers.

And not just after the fact anymore, solutions like DamageMap, a collaborative project between researchers at Stanford University and California Polytechnic State University, San Luis Obispo, uses aerial imagery and a deep learning algorithm to identify damage to buildings after a wildfire event in near real-time. The application could potentially guide relief personnel and crews to the areas that need it most, and keep other parties informed from a distance.

As Michelle Horton, senior developer communication manager at NVIDIA, reports, “The technology is not only accurate, it’s also fast. Using an NVIDIA GPU and the PyTorch deep learning framework accelerated by cuDNN, DamageMap processes images at a speed of approximately 60 milliseconds per image.”

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