To enhance the generation of building footprints in ControlCity, the multimodal diffusion model can be extended by integrating additional data sources such as satellite imagery, social media geotagged data, and government urban planning datasets.
Satellite Imagery: High-resolution satellite images can provide detailed visual context about urban areas, including building heights, materials, and surrounding landscapes. By incorporating this data, the model can improve the accuracy of building footprint generation, particularly in complex urban environments.
Social Media Geotagged Data: Platforms like Instagram or Twitter often contain geotagged images that can provide real-time insights into urban dynamics. Analyzing these images can help capture transient urban features, such as pop-up markets or temporary structures, which are often not represented in static datasets like OSM.
Government Urban Planning Datasets: Local government databases often contain zoning laws, land use plans, and building permits. Integrating this information can help the model understand regulatory constraints and urban development trends, leading to more realistic and contextually appropriate building footprints.
Environmental Data: Incorporating environmental datasets, such as flood zones, green spaces, and air quality indices, can help the model generate building footprints that are not only accurate but also sustainable and resilient to environmental challenges.
By leveraging these diverse data sources, ControlCity can enhance its multimodal capabilities, leading to more comprehensive and context-aware urban morphology generation.