@article{4844,
  author = {Juan José Prieto-Gutiérrez},
  title = {Independence of Optical Parameters and Wavelength Effects in Synthetic Tissue Imaging Data},
  journal = {Journal of Multimedia Processing and Technologies},
  year = {2026},
  volume = {17},
  number = {4},
  doi = {https://doi.org/10.6025/jmpt/2026/17/4/227-245},
  url = {https://www.dline.info/jmpt/fulltext/v17n4/jmptv17n4_3.pdf},
  abstract = {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.},
}