Paper: OpenFACADES: An open framework for architectural caption and attribute data enrichment via street view imagery

Building properties, such as height, usage, and material, play a crucial role in spatial data infrastructures, supporting various urban applications. Despite their importance, comprehensive building attribute data remain scarce in many urban areas. Recent advances have enabled the extraction of objective building attributes using remote sensing and street-level imagery. However, establishing a pipeline that integrates diverse open datasets, acquires holistic building imagery, and infers comprehensive building attributes at scale remains a significant challenge.

Among the first, this study bridges the gaps by introducing OpenFACADES, an open framework that leverages multimodal crowdsourced data to enrich building profiles with both objective attributes and semantic descriptors through multimodal large language models. First, we integrate street-level image metadata from Mapillary with OpenStreetMap geometries via isovist analysis, identifying images that provide suitable vantage points for observing target buildings. Second, we automate the detection of building facades in panoramic imagery and tailor a reprojection approach to convert objects into holistic perspective views that approximate real-world observation.

@article{liang2025openfacades,
title = {OpenFACADES: An open framework for architectural caption and attribute data enrichment via street view imagery},
author = {Liang, Xiucheng and Xie, Jinheng and Zhao, Tianhong and Stouffs, Rudi and Biljecki, Filip},
year = 2025,
month = dec,
journal = {ISPRS Journal of Photogrammetry and Remote Sensing},
volume = {230},
pages = {918--942},
issn = {0924-2716}
}