Site selection from space for urban design: aligning EO surface thermal patterns with urban form and functions
This paper presents a novel methodology that integrates Earth observation-derived surface thermal patterns with urban form and function through multicriteria ranking and clustering to enable effective, design-oriented site selection for urban planning, as demonstrated in case studies of Genoa and Turin.
Original paper licensed under CC BY 4.0 (https://creativecommons.org/licenses/by/4.0/). This is an AI-generated explanation of the paper below. It is not written or endorsed by the authors. For technical accuracy, refer to the original paper. Read full disclaimer
Cities are getting hotter. This is not just a feeling on a summer day; it is a measurable shift in the physical world where concrete, asphalt, and buildings trap heat, creating pockets of warmth that can be significantly hotter than the surrounding countryside. Scientists call this the surface urban heat island effect. It is a complex problem because the heat does not distribute evenly. It depends on the shape of the city—how tall the buildings are, how narrow the streets are, and how much green space exists—as well as on the people living there, such as the elderly or those who cannot easily access cooling resources. For decades, researchers have used satellites to map these temperature patterns from space, providing a broad view of where the heat concentrates. However, a gap has remained between these satellite images and the actual work of city planners and architects. Planners do not design cities using the large, irregular grid lines of government census data or the broad zones used in climate models. They design using blocks, streets, and specific plots of land. When the data used to identify danger zones does not match the way designers see and shape the city, the information becomes difficult to use for real-world solutions.
A team of researchers from the University of Genoa and the CIMA Research Foundation set out to bridge this gap. They developed a new method to translate satellite temperature data into a language that urban designers can actually use. Instead of relying on standard administrative boundaries, which often cut through coherent neighborhoods and mix different types of buildings, the team used a technique called enclosed tessellation. This approach breaks the city down into closed, contiguous shapes that naturally follow the physical barriers of the built environment, such as rows of buildings and street networks. By using these shapes, the researchers could align the thermal data from space directly with the physical form of the city. They then combined this with a vast array of other information, including population density, the age of residents, the distance to hospitals and parks, and the amount of tree cover. Using a structured decision-making process, they weighed all these factors together to rank every single plot of land in two Italian cities, Genoa and Turin, based on how much risk they faced from extreme heat.
The study focused on two very different cities to test if their method worked across different landscapes. Genoa is a coastal city squeezed between steep hills, characterized by narrow streets and high humidity. Turin, by contrast, sits on a flat plain along a river with a more compact and uniform layout. The researchers analyzed data from the summer of 2022, a period of intense heat, using satellite images to measure the temperature of the ground surface. They did not just look at the heat; they looked at the hazard, the exposure, and the vulnerability of the people living there. They tested their method against traditional census blocks to see if their new approach offered a clearer picture. The results showed that their method was more stable and consistent. While the traditional census blocks produced rankings that shifted significantly depending on how the data was weighted, the new shape-based approach provided a clearer, more reliable map of where the most critical interventions were needed. In both cities, the analysis confirmed that areas with dense construction, little vegetation, and high numbers of vulnerable residents were the most at risk.
Beyond simply identifying the hottest spots, the researchers also grouped these high-risk areas into distinct types. They found that the most dangerous places in Genoa fell into six different categories, reflecting the city's complex and varied terrain, while Turin's high-risk areas fit into just three simpler categories. This distinction is vital because it tells planners that a one-size-fits-all solution will not work. A dense, narrow street in a hilly neighborhood requires a different cooling strategy than a flat, open district. The study suggests that by understanding these specific local profiles, cities can better plan for nature-based solutions, such as planting trees or creating shaded areas, in the places where they will be most effective. The researchers emphasize that this is a tool for prioritization, not a final design plan. It highlights where to look and what kind of conditions exist, allowing designers to then apply their expertise to create specific solutions.
The work confirms that the way we measure and map heat matters deeply for how we fix it. If the data does not match the physical reality of the city, the solutions may miss the mark. By aligning satellite observations with the actual forms and functions of urban spaces, this research offers a more precise way to protect communities from rising temperatures. It suggests that the path to cooler cities lies not just in better technology, but in better alignment between the data we collect and the spaces we inhabit. The study does not claim to have solved the problem of urban heat, but it provides a clearer, more usable map for the journey ahead, showing exactly where the most urgent work needs to begin.
Drowning in papers in your field?
Get daily digests of the most novel papers matching your research keywords — with technical summaries, in your language.