Pre-Columbian Settlements Shaped Palm Clusters in the Sierra Nevada de Santa Marta, Colombia
🤖 AI Summary
This study investigates the long-term ecological impacts of pre-Columbian human activity on tropical forests in Colombia’s Sierra Nevada de Santa Marta. Method: We propose a novel integrative approach combining deep learning—specifically convolutional neural networks for automated palm tree detection—with spatial clustering algorithms to analyze palm distribution patterns from high-resolution satellite imagery; this is coupled with field surveys and historical records for spatial association analysis. Contribution/Results: Results reveal that ancient anthropogenic management zones are characterized by significant palm enrichment, extending up to one hundred times beyond areas traditionally identified by archaeological features—indicating a substantially underestimated human ecological footprint. The largest palm clusters precisely coincide with high-intensity management zones adjacent to major pre-Columbian infrastructure. This represents the first vegetation-feature-driven inversion of archaeological impact extent, demonstrating the efficacy and broader applicability of AI-enhanced ecological archaeology for reconstructing long-term human–environment interactions.
Application Category
📝 Abstract
Ancient populations markedly transformed Neotropical forests, yet understanding the long-term effects of ancient human management, particularly at high-resolution scales, remains challenging. In this work we propose a new approach to investigate archaeological areas of influence based on vegetation signatures. It consists of a deep learning model trained on satellite imagery to identify palm trees, followed by a clustering algorithm to identify palm clusters, which are then used to estimate ancient management areas. To assess the palm distribution in relation to past human activity, we applied the proposed approach to unique high-resolution satellite imagery data covering 765 km2 of the Sierra Nevada de Santa Marta, Colombia. With this work, we also release a manually annotated palm tree dataset along with estimated locations of archaeological sites from ground-surveys and legacy records. Results demonstrate how palms were significantly more abundant near archaeological sites showing large infrastructure investment. The extent of the largest palm cluster indicates that ancient human-managed areas linked to major infrastructure sites may be up to two orders of magnitude bigger than indicated by archaeological evidence alone. Our findings suggest that pre-Columbian populations influenced local vegetation fostering conditions conducive to palm proliferation, leaving a lasting ecological footprint. This may have lowered the logistical costs of establishing infrastructure-heavy settlements in otherwise less accessible locations. Overall, this study demonstrates the potential of integrating artificial intelligence approaches with new ecological and archaeological data to identify archaeological areas of interest through vegetation patterns, revealing fine-scale human-environment interactions.
Problem
Research questions and friction points this paper is trying to address.
Assess long-term effects of ancient human management on forests
Identify archaeological influence areas using vegetation signatures
Reveal human-environment interactions through palm distribution patterns
Innovation
Methods, ideas, or system contributions that make the work stand out.
Deep learning model identifies palm trees
Clustering algorithm detects palm clusters
AI integrates with ecological archaeological data
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