FlatCityBuf: A new cloud-optimised CityJSON format

Abstract. With the increasing availability of large-scale 3D city models, efficient data storage and transmission formats are essential. While the geospatial community has developed cloud-optimised formats for 2D datasets (binary files that can be efficiently indexed and accessed through HTTP Range requests), 3D city models with complex geometries, attributes, textures, and semantic surfaces still rely on text-based files using the CityGML standard (CityJSON and XML files). In this paper, we present FlatCityBuf, a new compact binary encoding format for 3D city models based on FlatBuffers and CityJSON. Our approach leverages the benefits of FlatBuffers, including cross-platform support, zero-copy data access, and efficient deserialisation, while adhering to the CityGML data model. The addition of spatial and attribute indices enables efficient queries to retrieve partial data. We evaluate the read performance and compression ratios of FlatCityBuf against CityJSONSeq using real-world 3D city models and demonstrate its advantages over existing formats. The results highlight FlatCityBuf’s efficient storage and transfer of 3D city model data, achieving for real-world datasets 10–30% compression compared to the already compact CityJSON format; for deserialisation it is 9–250× faster and uses 2–6× less memory. The schemas and accompanying software for conversion to/from CityJSON are publicly available at https://github.com/cityjson/flatcitybuf under a permissive license.