Smart city and Urban Planning
Aleben (Germany) –
Smart Grid Lighting Optimization
•Task: Reduce energy costs by 40% while enhancing urban safety with AI-powered Smart Grid optimization.
•The city is addressing high energy costs and inconsistent lighting coverage. Using iPhone-based data and advanced neural networks, we are creating detailed 3D models of lighting fixtures, integrating them with OpenStreetMap, and optimizing Smart Grid efficiency.
•Smart Grids, urban lighting optimization, energy-efficient planning, and public safety enhancements
Data Collection:
•IPhone-generated dense point cloud, mapped trajectory on Google Maps.
•Detail and accuracy 1 cm (Number font thickness 1-1.5 cm)
Data Processing Pipeline:
•Pixel-level object segmentationation
•Shooting trajectory on Google maps
•Merge dense point-clouds with OSM (OpenStreetMap (OSM) add contextual information and detailed map of the world. For the particular location OSM provides very schematic building shapes and locations so our cloud-point could also be used to improve the public domain data)
Data Processing Pipeline:
•Recognition of all types of urban lighting fixtures
•Detected and recognized objects in 3D
We also use data visualization techniques, such as heat maps and graphs, to display statistical information about objects
Results:
•By analyzing data on energy consumption, environmental conditions, Smart Grid systems can make data-driven decisions to adjust lighting levels and reduce energy usage during peak hours.
