From Stars to LETTERS: A Multi-Dimensional, FAIR-Aligned Framework for Geospatial Metadata Quality Evaluation

Star-based schemes, such as the 5-star Linked Open Data model and its geospatial extensions, are widely used to characterize openness and interoperability. However, in practice, higher star ratings are often assigned on the basis of technical properties such as RDF exposure or schema publication without requiring the satisfaction of foundational metadata quality conditions. This weakens the monotonic interpretation of star levels and can produce ambiguous signals for data users. To address this issue, we propose the LETTER framework, a multi-dimensional evaluation model in which seven independent binary dimensions describe metadata readiness for reuse: Provenance (P), Access (A), Structure (S), Connections (C), License (L), Identifiers (I), and Quality (Q). The framework is aligned with FAIR principles and mapped to ISO 19115 and ISO 19157 concepts. We evaluate it through an exploratory comparative case study of four purposively selected datasets from the German Spatial Data Infrastructure (GDI-DE): Municipal Points of Interest (Trier), Thuringia Digital Elevation Model (DEM), Administrative Units (VG250), and INSPIRE Digital Land Model (DLM). The results show that datasets receiving comparatively high star ratings may still lack machine-actionable provenance, quality evidence, stable identifiers, or robust access conditions. In particular, the analysis highlights a recurring ‘PDF Trap’, where relevant trust information exists only in narrative documentation and therefore remains inaccessible to automated reuse workflows. We conclude that LETTER provides clearer diagnostic power than scalar star ratings by exposing which metadata functions are actually satisfied and which remain missing.