Autonomous urban topology generation for urban flow modelling

Abstract

• New framework is developed that integrates the autonomous urban topology generation for urban flow modeling. • A new deep learning model is developed for building footprints extraction from satellite imagery • Deep learning model was meshed with Light Detection and Ranging (LiDAR) data by meshing to generate 3D building models • An illustration pedestrian level wind simulation was carried out for city of London On. One of the challenges in realistic numerical urban micro-climate modelling for wind, heat transfer, and building energy simulation applications is the complexity of urban topology and complex building geometries. This paper presents a deep learning modelling for building footprint polygon extraction from satellite imagery that is integrated with Light Detection and Ranging (LiDAR) data to generate 3D building models. The deep learning model registered a mean squared error of 0.02. The trained deep learning model can then be applied to a new set of input image data to extract building footprint polygons for autonomous application, and it can also be incrementally retrained with good quality data when it becomes available. A framework is developed that integrates the autonomous urban topology generator with urban flow modelling. The modelling steps are explained through an application example of urban flow modelling encountered during a pedestrian level wind assessment for the city of London, Ontario.