Accurate interpretation of hyperspectral data depends on the availability of reference spectra: measurements of known materials compiled into spectral libraries. Such libraries support both direct classification and machine learning applications. When combined with on-site hyperspectral imaging, they have proven effective across a variety of domains including heritage conservation, homeland security, hydrology, and geology. Urban conditions, however, present unique challenges to spectral data collection. In this context, urban materials refer to the components of the built condition, including both manufactured materials (e.g., asphalt, concrete, paint) and naturally occurring materials that have been anthropogenically modified for urban use (e.g., cut stone). Although hyperspectral data have been utilized in select urban planning tasks, the broader potential of hyperspectral imaging for material identification remains underutilized. This project will develop and extend community involvement in hyperspectral remote sensing technology to analyze and study urban landscapes. This will be paired with open metadata standards, modular processing toolkits, and automated archival workflows that prioritize FAIR principles. HS-SPECTRA (Hyperspectral Standardizing and Sharing Possibilities for Urban Conditions through Toolkits, Resources, and Archiving) addresses fundamental challenges in hyperspectral library design by: 1) Developing a metadata architecture tailored to longitudinal