This paper investigates how the provenance and resolution of geospatial data used to construct mesh maps affect the accuracy and robustness of mesh-based visual localisation. Mesh-based approaches offer significant advantages over traditional pipelines reliant on Structure from Motion (SfM) models, including the ability to scale to city-sized scenes—by leveraging large-scale data sources such as national mapping databases— and on-demand generation of arbitrary synthetic views. While prior work has focused on algorithmic improvements to mesh-based localisation, none has systematically analysed how different input data affect localisation outcomes. In this work, we evaluate three meshes—derived from aerial oblique imagery, combined aerial and ground mobile mapping data, and close-range ground imagery—across the egenioussBench Extended and House of Science query sets and four image matchers. We show that mesh quality is the dominant factor governing localisation performance. In the House of Science experiments, aerial meshes lack the resolution required to resolve fac¸ade detail, causing near-total localisation failure regardless of matcher. In the egenioussBench Extended experiments, augmenting an aerial mesh with ground data yields consistent but less dramatic improvements. We further introduce the Perceptual Detail Score (PDS), a viewing-condition-aware metric that proves to be a strong predictor of downstream pose accuracy across all experimental configurations.