Green Urban Planning Using Computer Vision and Machine Learning

Urban areas worldwide face the dual challenge of managing rising temperatures due to the urban heat island (UHI) effect and creating sustainable urban spaces. This paper introduces an integrated tool that combines UHI simulation with plant species optimization for green roofs and walls. The tool employs machine learning (ML), computer vision (CV), and geographic information systems (GIS) to aid architects and urban planners in designing climate-resilient cities. By leveraging local climate data, vegetation indices, and building characteristics, the tool predicts the impact of increased vegetation on UHI mitigation and provides optimal plant recommendations. The comprehensive workflow includes data collection, predictive modelling, and user-friendly visualization, enabling informed decision-making for sustainable urban planning.