This study evaluates the location accuracy (LA) of the European Cooperation for Lightning Detection (EUCLID) network by analysing lightning strikes recorded near tall structures located either on flat ground or on elevated terrain, such as mountain ridges, over a 14-year period from 2012 to 2025. Structures selected for this analysis, either exceeding 150 meters in height or situated on prominent terrain like mountain tops, serve as proxy reference points for assessing the network's performance. The methodology involves calculating the ratio of lightning strike densities within 500 m of each structure to those within the surrounding ring extending from 500 m to 2 km. Structures with ratios below a defined threshold are excluded from further analysis, as the absence of elevated lightning density near the structure suggests it does not significantly attract or initiate lightning and is therefore unsuitable for our methodology. Subsequently, a density-based clustering algorithm is used to identify the most likely cluster of lightning events associated with the structure. It is then assumed that the lightning events within this cluster have struck the structure and can therefore be used in the analysis of location accuracy. Results indicate a median LA of 124 m and a 95th percentile of 258 m. These values align well with those obtained from past ground-truth campaigns using high-speed video cameras and instrumented towers. Moreover, the spatial distribution of LA derived from this methodology shows a similar pattern to that of the median value of the 50 % error ellipse semi-major axis reported by the network. In addition, a focused analysis of two well-known instrumented towers, i.e., Gaisberg and Säntis, confirms that the results in terms of LA from this methodology correspond well with findings previously reported in the literature. More generally, an overall decreasing trend in median LA over time is observed across all towers, which is consistent with expectations given the continuous evolution and improvement of the detection network. This analysis underscores the importance of consistent sensor upgrades and optimized placements to achieve high detection efficiency and location accuracy.