A multi-scale partitioning and aggregation method for large volumes of buildings considering road networks association constraints

The automatic aggregation of buildings often requires the handling of large volumes of data, which results in high processing overhead. One efficient and feasible way of managing this issue is to partition data using a road network and perform parallel computing. Generally, road networks are used not only to divide buildings into multiple cells but also to provide strong spatial constraints for the geometric morphology and a balanced spatial distribution of building aggregation results. Existing methods have focused on the former at the expense of the latter, which easily leads to spatial conflicts between road networks, and building aggregation results in the multi-scale representation of buildings. Hence, a multi-scale partitioning and aggregation method for large volumes of buildings considering the association constraint of the road network is proposed. First, road stroke connections are established, and road levels are calculated on the basis of semantic, geometric, and topological features of road strokes. Second, large volumes of buildings are partitioned level-by-level based on multi-level roads and the convex hull of buildings. Finally, a "three morphological transformation algorithm" is proposed to aggregate buildings in each partitioning cell. OpenStreetMap data (31 x 33 km(2)) from Tokyo Bay, Japan, were used for validation. The experiment reveals that compared to the state-of-the-art method, the proposed method avoids spatial crossing conflicts and eliminates "narrow necks" in the building aggregation results. The coordination between buildings and the road network was enhanced, and the morphological similarity of the buildings before and after aggregation was improved.