<p>Volunteered geographic information (VGI), such as OpenStreetMap (OSM), provides extensive coverage; however, its quality may vary due to inconsistent contributor expertise and a lack of regulation. This study introduces an intrinsic quality assessment methodology that employs geometric properties and usage types of linear features to evaluate the reliability of spatial data. These internal characteristics have historically received insufficient attention as indicators of quality. Notably, geometric features are inherent within all spatial data and can be consistently extracted, unlike descriptive attributes, which are often incomplete or entirely absent. To validate this approach, an extrinsic assessment method utilizing a feature-matching technique was adopted to examine the correlation between intrinsic factors and positional accuracy, a standard measure of spatial quality. The feature-matching approach utilizes normalized fuzzy values derived from three geometric criteria: orientation difference, Hausdorff distance, and buffer overlap. This method identifies corresponding features between volunteered and official datasets and calculates positional accuracy. The dataset employed in this study encompasses a comprehensive history of linear volunteered features for the study area. The results compare intrinsic assessment outcomes with positional accuracy and reveal a strong inverse relationship between geometric metrics—such as length, sinuosity, number of intersections, and enclosed area—and positional accuracy. Moreover, usage type significantly affects positional accuracy, with residential features demonstrating the highest mean accuracy. A subregional analysis within Tehran confirmed that the identified relationships can persist across different urban typologies. All observed relationships are statistically significant, underscoring the effectiveness of geometric and usage-based factors in intrinsic quality assessment and their correlation with positional accuracy.</p>