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Looking beyond smart cities
Two-dimensional radiographs, while commonly used for evaluating sub-surface hard structures of teeth, have low sensitivity for early cari...
City Science researchers are developing a slew of tangible and digital platforms dedicated to solving spatial design and urban planning c...
Creating technology for social change
Making the invisible visible–inside our bodies, around us, and beyond–for health, work, and connection
MIT researchers have developed a system that can produce images of objects shrouded by fog so thick that human vision can’t penetrate it.
Tangible interactive matrix for real-time computation and 3D projection mappingThe Tactile Matrix is a system for an illuminated, machine...
The Gender Shades project pilots an intersectional approach to inclusive product testing for AI.Algorithmic Bias PersistsGender Shades is...
All people are created equal, but in the eyes of the algorithm, not all faces are just yet.A new study from MIT and Microsoft r...
A new review of face recognition software found that, when identifying gender, the software is most accurate for men with light skin...
Examination of facial-analysis software shows error rate of 0.8 percent for light-skinned men, 34.7 percent for dark-skinned women.
New research out of MIT’s Media Lab is underscoring what other experts have reported or at least suspected before: facial recognition tec...
Keith Angelino, David A. Edlund, Pratik Shah. Near-Infrared Imaging for Detecting Caries and Structural Deformities in Teeth. Journal of Translational Engineering in Health and Medicine. 10.1109/JTEHM.2017.2695194
This project depicts the design, deployment and operation of a Tangible Regulation Platform, a physical-technological apparatus made for ...
Ira Winder and the Tactile Matrix won the award for best demonstration at the IEEE Future Technologies Conference.
We recently led a workshop in Saudi Arabia, with staff from the Riyadh Development Authority, to test a new version of our CityScope plat...
Transforming data into knowledge
Read more about this project hereMIT City Science is working with Hafencity University to develop CityScope for the neighborhood of Rothe...
View the main City Science Andorra project profile.Research in dynamic tools, mix users (citizens, workers) amenities, services, and land...
The Mobility Futures Collaborative in the MIT Department of Urban Studies and Planning (DUSP) and the Changing Places grou...
Developed by Ira Winder with the MIT Centre for Transportation and Logistics, the model seeks to use real population data and create a si...
This project focused on pedestrian accessibility in collaboration with Singapore Centre for Liveable Cities. Researchers and planners cam...
This project is the first of two projects in collaboration with GSK. We are developing a computational simulation that allows a human use...
This is an open source geo spatial exploration tool. Using various public APIs including Open Street Map and the United States Census, we...
This is the second project from the GSK collaboration. This project considers how space and collaboration are intertwined. We are develop...
Facebook volunteers and work-at-home moms might be making city planning decisions, thanks to AI research conducted by MIT scientists. Res...
Using computer vision to examine Google Street View, the researchers analyzed how streets and blocks have changed in five American cities.
Tested with five American cities, Streetchange quantifies the physical improvement or deterioration of neighborhoods.
A recently published paper in the Proceedings of the National Academy of Sciences (PNAS) looks at factors that predict neighborhood change.
Researchers have used machine learning to quantify the physical improvement or deterioration of neighborhoods in five American cities.
Computer vision uncovers predictors of physical urban change
Paiva, Prada, W., (Eds.)., 4738, datePaiva, Prada, W., (Eds.)., 4738, datePaiva, Prada, W., (Eds.)., 4738, date