City-scale building energy simulation provides a significant reference for planning and urban management. However, large-scale building energy simulation is often unfeasible due to the huge amount of computational resources required and the lack of high-precision building models. For such reasons, this study developed a tiled multi-city urban objects dataset and a distributed data ontology. Such a data metric not only transforms the conventional whole-city simulation model into patch-based distributed simulations but also incorporates interactive relationships among objects in cities. The dataset stores urban objects (8,196,003 buildings; 238,736 vegetations; 2,381,6698 streets; 430,364 UrbanTiles; 430,464 UrbanPatches) from thirty major cities in the United States. It also aggregated morphological features for each UrbanTile. To validate the performance of the developed dataset, a sample test was conducted in one city subset (Portland). The results conclude that the linear increase of time usage of modeling and simulation with the increase of building numbers. With the tiled data structure, the proposed dataset is also efficient for the building microclimate estimation.