Journal of Multimedia Processing and Technologies Juan José Prieto-Gutiérrez 17 4 2026 https://doi.org/10.6025/jmpt/2026/17/4/227-245 https://www.dline.info/jmpt/fulltext/v17n4/jmptv17n4_3.pdf Automated construction monitoring increasingly relies on geospatial metadata and machine learning to track urban development; however, the true generalizability and reliability of metadata driven models remain underexplored. This study evaluates the predictive capabilities of geospatial metadata including polygon geometry, temporal duration, and annotation attributes using Random Forest, Extra Trees, and Logistic Regression models on a large scale GeoJSON dataset of urban construction sites. We conducted binary (site presence) and multiclass (construction type) classification tasks, revealing a stark contrast: while Random Forest achieved near perfect binary accuracy (~99.9%), multiclass construction type recognition remained highly ambiguous (macro-F1 = 0.610) due to semantic and geometric overlaps among categories such as Residential, Commercial, and Industrial. Crucially, permutation feature importance analysis exposed that the binary predictive signal was almost entirely concentrated in a single annotationderived variable (n_features), with all physical and geometric features registering zero importance. This indicates that the near perfect binary performance is an artifact of the annotation workflow and potential target leakage rather than genuine physical site detection. We conclude that metadata only models are insufficient for fine grained semantic typing. Furthermore, we issue a critical methodological warning for the GeoAI community: rigorous spatial cross validation (e.g., MGRS-tile holdouts) and annotation leakage audits are mandatory to prevent spatially and procedurally inflated performance metrics. Future frameworks must integrate multimodal satellite imagery to achieve robust, generalizable construction type recognition.