Urban planning models just crossed 90% accuracy using free map data anyone can edit. A new paper builds a single deep learning pipeline that turns OpenStreetMap into a full urban intelligence system. Land use, buildings, traffic, and air quality all predicted together, not separately.  Most studies treat these as isolated problems. One model for land use. Another for traffic. Another for pollution. This one connects them. The system fuses three data layers: • OpenStreetMap for structure • Satellite imagery for surface detail • Environmental and demographic data for context  Then assigns each task a specialised model: • CNNs for land use • U-Net for building footprints • LSTMs for traffic • Hybrid models for air quality  Each model solves its own problem. The pipeline ties them into one view of the city. The results are pretty strong: • Land use classification: 91.6% accuracy • Building detection: 94.0% accuracy • Traffic prediction error: 3.6 vehicles per hour • Air quality prediction error: 2.3 µg/m³  These sit at the upper end of current GeoAI benchmarks. The improvement comes from integration. OpenStreetMap gives topology. Satellite data gives physical signals. Environmental data gives dynamics. Each fills gaps in the others. That reduces ambiguity where models usually struggle. Mixed-use zones. Dense urban cores. Noisy or incomplete maps. The system learns a more complete representation of the city. The workflow is quite simple: 1.Collect multi-source data 2.Align and standardise it 3.Train task-specific models 4.Combine outputs into urban indicators  Raw geodata in. Policy-relevant outputs out. The implications are practical. Traffic models identify congestion hotspots. Land use maps reveal missing green space. Building extraction supports infrastructure planning. Air quality forecasts guide mitigation.  All from largely open data. The constraint is compute. Training requires GPUs and careful preprocessing. Smaller cities may struggle to deploy this directly. Data quality also matters. OpenStreetMap varies by location. Even so, the direction is clear. Urban planning is shifting from static GIS layers to integrated, predictive systems. One pipeline. Multiple urban signals. Continuous updates. Cities are becoming modelled environments, not just mapped ones.Urban planning models just crossed 90% accuracy using map data that anyone can edit. A new paper builds a single deep learning pipeline that turns OpenStreetMap into a full urban intelligence system, predicting land use, buildings, traffic, and air quality together rather than treating them as separate problems.  Most existing work fragments the city into isolated tasks. One model classifies land use. Another extracts buildings. A third forecasts traffic. This paper links them into a single system. The key idea is simple. Combine three types of data that each see the city differently: • OpenStreetMap provides structure such as roads, buildings, and land use • Satellite imagery captures physical and spectral detail • Environmental and demographic data add temporal and social context  Each task is then handled by a model suited to its structure. CNNs for land use, U-Net for building footprints, LSTMs for traffic, and hybrid models for air quality. The novelty is that all outputs are combined into one coherent view of the city rather than analysed in isolation. The performance is strong across the board. Land use classification reaches 91.6% accuracy. Building detection hits 94.0%. Traffic prediction errors fall to 3.6 vehicles per hour, and air quality prediction to 2.3 µg/m³.  These results sit at the upper end of current GeoAI benchmarks, and the reason is not a single model improvement. It is the interaction between data sources. OpenStreetMap encodes topology but misses detail. Satellite imagery captures detail but struggles with semantics. Environmental data adds dynamics but lacks spatial structure. When combined, each compensates for the others, reducing ambiguity in dense or mixed-use areas where models typically fail. The workflow follows a clear pipeline. Multi-source data is collected, aligned, and standardised. Task-specific models are trained. Their outputs are then fused into multi-dimensional urban indicators that describe structure, function, mobility, and environment together.  This produces something closer to a live model of the city rather than a static map. The implications are practical. Traffic forecasts identify congestion hotspots. Land use maps reveal gaps in green space. Building extraction supports infrastructure planning. Air quality predictions guide mitigation strategies.  All of this is built largely on open data. Link to full paper: nature.com/articles/s41598-0…