Mapping the Intelligence Gap: GPU-Accelerated Machine Learning and Hyperparameter Optimization for the Temporal Analysis of Dutch Colonial Cartography of the Isthmus of Panama, 1609 to 1714

ABSTRACT This article proposes and partially demonstrates a computational methodology for the historical analysis of Dutch colonial cartography of the Isthmus of Panama across the period 1609 to 1714. Drawing on GPU-accelerated convolutional neural network (CNN) architectures informed by contemporary deep learning practice, combined with systematic hyperparameter optimization, we argue that a temporal corpus of early modern Dutch maps can be analyzed to detect and quantify emergent changes in cartographic content, changes that reflect and record transformations in the social, environmental, legal, and geopolitical landscape of the Tierra Firme colonial theater. The article advances three connected arguments. First, that Dutch cartography of Panama during this period constitutes a structured data corpus amenable to machine learning analysis in ways that traditional cartographic history has not exploited. Second, that hyperparameter-optimized CNN models trained on cartographic feature categories, fortification markers, indigenous settlement representations, coastal hydrographic data, and legal-territorial inscriptions, can detect temporal patterns of informational emergence, suppression, and revision that correspond to datable historical events. Third, that the resulting “intelligence gap” between Dutch cartographic knowledge production and that of competing European powers is not merely a humanistic interpretation but a measurable, reproducible historical phenomenon. The methodology is positioned as foundational work for a larger dissertation project examining how knowledge asymmetry shaped the outcomes of imperial competition in the early modern Atlantic world.