AI System Revolutionizes Urban Green Space Mapping, Unveiling Environmental Inequalities

New AI system accurately maps urban green spaces, exposing environmental divides | Newswise

A research team led by Rumi Chunara -- an NYU associate professor with appointments in both the Tandon School of Engineering and the School of Global Public Health -- has unveiled a new artificial intelligence (AI) system that uses satellite imagery to track urban green spaces more accurately than prior methods, critical to ensuring healthy cities. To validate their approach, the researchers tested the system in Karachi, Pakistan's largest city where several team members are based. Karachi proved an ideal test case with its mix of dense urban areas and varying vegetation conditions. Accepted for publication by the ACM Journal on Computing and Sustainable Societies, the team's analysis exposed a stark environmental divide: some areas enjoy tree-lined streets while many neighborhoods have almost no vegetation at all. Cities have long struggled to track their green spaces precisely, from parks to individual street trees, with traditional satellite analysis missing up to about 37% of urban vegetation. As cities face climate change and rapid urbanization, especially in Asia and Africa, accurate measurement has become vital. Green spaces can help reduce urban temperatures, filter air pollution, and provide essential spaces for exercise and mental health. But these benefits may be unequally distributed. Low-income areas often lack vegetation, making them hotter and more polluted than tree-lined wealthy neighborhoods. The research team developed their solution by enhancing AI segmentation architectures, such as DeepLabV3+. Using high-resolution satellite imagery from Google Earth, they trained the system by augmenting their training data to include varied versions of green vegetation under different lighting and seasonal conditions -- a process they call 'green augmentation.' This technique improved vegetation detection accuracy by 13.4% compared to existing AI methods -- a significant advance in the field. When measuring how often the system correctly identifies vegetation, it achieved 89.4% accuracy with 90.6% reliability, substantially better than traditional methods which only achieve 63.3% accuracy with 64.0% reliability. "Previous methods relied on simple light wavelength measurements," said Chunara, who serves as the Director of the NYU Center for Health Data Science and is a member of NYU Tandon's Visualization Imaging and Data Analysis Center (VIDA). "Our system learns to recognize more subtle patterns that distinguish trees from grass, even in challenging urban environments. This type of data is necessary for urban planners to identify neighborhoods that lack vegetation so they can develop new green spaces that will deliver the most benefits possible. Without accurate mapping, cities cannot address disparities effectively." The Karachi analysis found the city averages just 4.17 square meters of green space per person, less than half the World Health Organization's (WHO's) recommended minimum of 9 square meters per capita. The disparity within neighborhoods is dramatic: while some outlying union councils -- Pakistan's smallest local government body, a total of 173 were included in the study -- have more than 80 square meters per person, five union councils have less than 0.1 square meters per capita. The study revealed that areas with more paved roads -- typically a marker of economic development -- tend to have more trees and grass. More significantly, in eight different union councils studied, areas with more vegetation showed markedly lower surface temperatures, demonstrating green spaces' role in cooling cities. Singapore offers a contrast, showing what's possible with deliberate planning. Despite having a similar population density to Karachi, it provides 9.9 square meters of green space per person, exceeding the WHO target. The researchers have made their methodology public, though applying it to other cities would require retraining the system on local satellite imagery. This study adds to Chunara's body of work developing computational and statistical methods, including data mining and machine learning, to understand social determinants of health and health disparities. Prior studies include using social media posts to map neighborhood-level systemic racism and homophobia and assess their mental health impact, as well as analyzing electronic health records to understand telemedicine access disparities during COVID-19. In addition to Chunara, the paper's authors are Miao Zhang, a Ph.D. candidate in NYU Tandon's Department of Computer Science and Engineering and VIDA; and Hajra Arshad, Manzar Abbas, Hamzah Jehanzeb, Izza Tahir, Javerya Hassan and Zainab Samad from The Aga Khan University's Department of Medicine in Karachi. Samad also holds an appointment in The Aga Khan University's CITRIC Health Data Science Center. Funding for the study was provided by the National Science Foundation and National Institutes of Health.

New AI system accurately maps urban green spaces, exposing environmental divides

A research team led by Rumi Chunara -- an NYU associate professor with appointments in both the Tandon School of Engineering and the School of Global Public Health -- has unveiled a new artificial intelligence (AI) system that uses satellite imagery to track urban green spaces more accurately than prior methods, critical to ensuring healthy cities. To validate their approach, the researchers tested the system in Karachi, Pakistan's largest city where several team members are based. Karachi proved an ideal test case with its mix of dense urban areas and varying vegetation conditions. Accepted for publication by the ACM Journal on Computing and Sustainable Societies, the team's analysis exposed a stark environmental divide: some areas enjoy tree-lined streets while many neighborhoods have almost no vegetation at all. Cities have long struggled to track their green spaces precisely, from parks to individual street trees, with traditional satellite analysis missing up to about 37% of urban vegetation. As cities face climate change and rapid urbanization, especially in Asia and Africa, accurate measurement has become vital. Green spaces can help reduce urban temperatures, filter air pollution, and provide essential spaces for exercise and mental health. But these benefits may be unequally distributed. Low-income areas often lack vegetation, making them hotter and more polluted than tree-lined wealthy neighborhoods. The research team developed their solution by enhancing AI segmentation architectures, such as DeepLabV3+. Using high-resolution satellite imagery from Google Earth, they trained the system by augmenting their training data to include varied versions of green vegetation under different lighting and seasonal conditions -- a process they call 'green augmentation.' This technique improved vegetation detection accuracy by 13.4% compared to existing AI methods -- a significant advance in the field. When measuring how often the system correctly identifies vegetation, it achieved 89.4% accuracy with 90.6% reliability, substantially better than traditional methods which only achieve 63.3% accuracy with 64.0% reliability. "Previous methods relied on simple light wavelength measurements," said Chunara, who serves as the Director of the NYU Center for Health Data Science and is a member of NYU Tandon's Visualization Imaging and Data Analysis Center (VIDA). "Our system learns to recognize more subtle patterns that distinguish trees from grass, even in challenging urban environments. This type of data is necessary for urban planners to identify neighborhoods that lack vegetation so they can develop new green spaces that will deliver the most benefits possible. Without accurate mapping, cities cannot address disparities effectively." The Karachi analysis found the city averages just 4.17 square meters of green space per person, less than half the World Health Organization's (WHO's) recommended minimum of 9 square meters per capita. The disparity within neighborhoods is dramatic: while some outlying union councils -- Pakistan's smallest local government body, a total of 173 were included in the study -- have more than 80 square meters per person, five union councils have less than 0.1 square meters per capita. The study revealed that areas with more paved roads -- typically a marker of economic development -- tend to have more trees and grass. More significantly, in eight different union councils studied, areas with more vegetation showed markedly lower surface temperatures, demonstrating green spaces' role in cooling cities. Singapore offers a contrast, showing what's possible with deliberate planning. Despite having a similar population density to Karachi, it provides 9.9 square meters of green space per person, exceeding the WHO target. The researchers have made their methodology public, though applying it to other cities would require retraining the system on local satellite imagery. This study adds to Chunara's body of work developing computational and statistical methods, including data mining and machine learning, to understand social determinants of health and health disparities. Prior studies include using social media posts to map neighborhood-level systemic racism and homophobia and assess their mental health impact, as well as analyzing electronic health records to understand telemedicine access disparities during COVID-19. In addition to Chunara, the paper's authors are Miao Zhang, a PhD candidate in NYU Tandon's Department of Computer Science and Engineering and VIDA; and Hajra Arshad, Manzar Abbas, Hamzah Jehanzeb, Izza Tahir, Javerya Hassan and Zainab Samad from The Aga Khan University's Department of Medicine in Karachi. Samad also holds an appointment in The Aga Khan University's CITRIC Health Data Science Center. Funding for the study was provided by the National Science Foundation and National Institutes of Health.