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Turning pictures of buildings into better disaster preparedness

2021-05-27  |   Editor : houxue2018  
Category : News

Abstract

Researchers from the National Science Foundation’s SimCenter, an engineering community focused on modeling the impact of natural hazards, built the Building Recognition using AI at Large-Scale suite of tools. BRAILS creates enhanced building databases for cities by running artificial intelligence-powered simulations on high-performance computers at the Texas Advanced Computing Center (TACC) at the University of Texas at Austin.

Content

Scientists are using data from Google Maps and satellite images to power artificial intelligence applications that can automatically identify characteristics of city’s buildings that would be vulnerable in an earthquake, hurricane or tsunami.

Researchers from the National Science Foundation’s SimCenter, an engineering community focused on modeling the impact of natural hazards, built the Building Recognition using AI at Large-Scale suite of tools. BRAILS creates enhanced building databases for cities by running artificial intelligence-powered simulations on high-performance computers at the Texas Advanced Computing Center (TACC) at the University of Texas at Austin.

"We want to simulate the impact of hazards on all of the buildings in a region, but we don't have a description of the building attributes," said Charles Wang, a postdoctoral researcher at the University of California, Berkeley, and the lead developer of BRAILS told TACC News. "Using AI, we are able to get the needed information. We can train neural network models to infer building information from images and other sources of data."

The basic BRAILS framework uses computer vision to automatically pull building details – architectural features such as roofs, windows, and chimneys -- from satellite and ground-level images found in Google Maps and merges these with other datasets, such as Microsoft Footprint Data and OpenStreetMap. BRAILS users can also enhance the data with tax records, city surveys and other information to get more accurate assessments.

A crowdsourcing effort has contributed some of the data labeling. Volunteer with the SimCenter’s Building Detective for Disaster Preparedness project identified specific architectural features of structures, such as garages, roofs and adjacent buildings, that are then used to train additional machine learning modules. The citizen-science project was launched on March. Within a few weeks, a thousand volunteers had annotated 20,000 images, Wang said.

The researchers ran a series of testbeds to determine the accuracy of the AI-based models. Each generated an inventory of a city’s structures and simulated the impact of a hazard based on historical or plausible events. The team has created testbeds for earthquakes in San Francisco, and for hurricanes in Lake Charles, La., the Texas coast and Atlantic City, N.J.

To train the BRAILS modules and run the simulations, the researchers relied on TACC’s supercomputers -- notably Frontera, the fastest academic supercomputer in the world, and Maverick 2, a GPU-based system designed for deep learning.

"The hazard event simulations -- applying wind fields or ground shaking to thousands or millions of buildings to assess the impact of a hurricane or earthquake -- requires a lot of computing resources and time," Wang said. "For one city-wide simulation, depending on the size, it typically takes hours to run on TACC."

When asked about the performance of BRAILS, Wang touted the accuracy. "For some models, like occupancy, we are seeing the accuracy is close to 100%,” he said. “For other modules, like roof type, we're seeing 90% accuracy."

The researhers outlined the framework in the February 2021 issue of Automation in Construction and presented a testbed for Hurricane Laura the 2020 hurricane that made landfall in Louisiana at the 2021 Workshop on SHared Operational REsearch Logistics In the Nearshore Environment.

"Our objectives are two-fold," Wang said. "First, to mitigate the damage in the future by doing simulations and providing results to decision- and policy-makers. And second, to use this data to quickly simulate a real scenario – instantly following a new event, before the reconnaissance team is deployed. We hope near-real-time simulation results can help guide emergency response with greater accuracy."

Sources: GCN

https://gcn.com/articles/2021/05/19/brails-city-hazard-assessment.aspx

Provided by the IKCEST Disaster Risk Reduction Knowledge Service System

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