Urban Planning
Overview
AI for urban planning studies how data and computational models can help understand cities and assess choices about mobility, land use, housing, public space, and climate resilience. Common approaches include GIS and remote sensing, computer vision on street-view imagery, spatial machine learning, optimization, agent-based simulation, and generative models or language-model agents. These methods are used to map urban features, estimate accessibility and heat exposure, model travel and land-use patterns, and compare possible designs or interventions. A central challenge is making results reliable across different places and populations: models need local and field-based validation, calibrated uncertainty, and human review where decisions involve equity, regulations, or professional judgment.
Latest papers
48 papers4 papers in the last four weeks, down 20% on the four weeks before. 0.0% of all new papers.