@article{4838,
  author = {Maleerat Sodanil},
  title = {Machine-Learning Classification of Construction-Site Annotations from Geospatial Metadata: Near-Perfect Binary Discrimination and the Limits of Construction-Type Recognition},
  journal = {Journal of Intelligent Computing},
  year = {2026},
  volume = {17},
  number = {4},
  doi = {https://doi.org/10.6025/jic/2026/17/4/223-245},
  url = {https://www.dline.info/jic/fulltext/v17n4/jicv17n4_3.pdf},
  abstract = {Automated construction monitoring increasingly relies on geospatial metadata and machine learning to
track urban development, yet 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 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). Crucially, permutation feature importance analysis exposed that the binary
predictive signal was almost entirely concentrated in a single annotation derived variable (n_features),
with all physical and geometric features registering zero importance. This indicates that the near perfect
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 and issue a critical methodological warning for the GeoAI community: rigorous spatial cross validation
and annotation leakage audits are mandatory to prevent spatially and procedurally inflated performance
metrics.},
}