# Abstract

Three-dimensional (3D) city models have evolved into essential tools for urban planning, simulation, and analysis. However, current formats like CityJSON and CityJSONSeq lack cloud-native optimizations for efficiently handling large-scale datasets. While cloud-optimized formats exist for 2D data, solutions for complex 3D city models remain limited. We introduce FlatCityBuf, a cloud-optimized encoding for CityJSON based on FlatBuffers, which combines binary serialization with spatial and attribute indexing. Our implementation employs a packed Hilbert R-tree for spatial queries and a bottom-up Static B+ tree for attribute indexing, enabling efficient bounding box and attribute filtering. By utilizing FlatBuffers' zero-copy deserialization and structured flattening of CityJSON's nested hierarchies, we achieve significant performance improvements. The format supports HTTP Range Requests, allowing clients to fetch only required portions of the file. Performance evaluation shows FlatCityBuf achieves up to 60× faster read times than CityJSONSeq for large datasets while reducing file sizes by 15-50%. Our web prototype demonstrates practical application by allowing interactive queries on a 4GB dataset covering a 20km×20km area without downloading the entire file. FlatCityBuf bridges the gap between CityJSONSeq and optimized binary formats, enabling scalable, real-time urban data processing while maintaining CityJSON's semantic richness.