An Abu Dhabi-based startup is using artificial intelligence (AI) and data analytics to provide detailed temperature dynamics for urban planners, businesses, and residents. FortyGuard, founded by entrepreneur Jay Sadiq, is addressing the critical lack of granular temperature data in cities. The company’s technology processes billions of data points daily, creating models of urban heat patterns to help stakeholders make informed decisions to combat extreme heat.
The urban heat island effect, where cities experience temperatures several degrees higher than surrounding rural areas, results from heat-absorbing materials like concrete and asphalt, vehicle emissions, and reduced wind flow caused by tall buildings. US cities such as Chicago and Los Angeles have taken steps, including adding green rooftops and painting roads with solar-reflective materials, to address the concern. However, Mr Sadiq identified a significant data gap, pinpointing the hottest areas in cities to deploy heat-reducing asphalt.
“When I started the company, I realised the level of information needed didn’t exist,” Mr Sadiq told CNN. This challenge inspired him to develop a solution that combines elevation, vegetation, water bodies, and atmospheric conditions to deliver a comprehensive view of urban temperature dynamics.
FortyGuard’s innovative approach delivers models with 89 per cent accuracy for some US cities, mapping urban heat at a granular 10-square-metre resolution. The company has collaborated with clients such as Masdar City, a sustainable urban project in the UAE, to identify heat hotspots and recommend solutions like planting trees and adding water features.
The startup envisions broader applications for its technology. Sadiq aims to integrate urban heat intelligence into platforms like real estate or map services, enabling homebuyers to identify cooler neighbourhoods or joggers to plan optimal routes. “Our approach goes beyond measuring air temperature at a specific point in time,” he says.
Experts like James Voogt, a professor of urban climatology at the University of Western Ontario, recognise the demand for precise urban heat data. “The key element is how such AI is being trained and on what,” he said.
Chao Ren, an applied climatology specialist at the University of Hong Kong, stressed the significance of how this data is utilised. “The question is really, who will be the end user of your data, and who will put such urban heat information into their practices?” she said.
