Pre-Columbian Settlements Shaped Palm Clusters in the Sierra Nevada de Santa Marta, Colombia
- URL: http://arxiv.org/abs/2507.06949v1
- Date: Wed, 09 Jul 2025 15:31:44 GMT
- Title: Pre-Columbian Settlements Shaped Palm Clusters in the Sierra Nevada de Santa Marta, Colombia
- Authors: Sebastian Fajardo, Sina Mohammadi, Jonas Gregorio de Souza, César Ardila, Alan Tapscott Baltar, Shaddai Heidgen, Maria Isabel Mayorga Hernández, Sylvia Mota de Oliveira, Fernando Montejo, Marco Moderato, Vinicius Peripato, Katy Puche, Carlos Reina, Juan Carlos Vargas, Frank W. Takes, Marco Madella,
- Abstract summary: We propose a new approach to investigate archaeological areas of influence based on vegetation signatures.<n>It consists of a deep learning model trained on satellite imagery to identify palm trees, followed by a clustering algorithm to identify palm clusters.<n>Results demonstrate how palms were significantly more abundant near archaeological sites showing large infrastructure investment.
- Score: 29.174699285095375
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- 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.
Related papers
- AI-ming backwards: Vanishing archaeological landscapes in Mesopotamia and automatic detection of sites on CORONA imagery [41.94295877935867]
We upgrade an existing deep learning model with the knowledge provided by one of the oldest sets of grayscale satellite imagery, known as CORONA.<n>The initial Bing based convolutional network model was retrained using CORONA satellite imagery for the district of Abu Ghraib, west of Baghdad.
arXiv Detail & Related papers (2025-07-17T14:21:50Z) - Detection and Geographic Localization of Natural Objects in the Wild: A Case Study on Palms [13.350975037304194]
We develop PRISM (Processing, Inference, and Mapping), a flexible pipeline for detecting and localizing palms in dense tropical forests using large orthomosaic images.<n>Our contributions are threefold. First, we construct a large UAV-derived orthomosaic dataset collected across 21 ecologically diverse sites in western Ecuador, annotated with 8,830 bounding boxes and 5,026 palm center points.<n>Second, we evaluate multiple state-of-the-art object detectors based on efficiency and performance, integrating zero-shot SAM 2 as the segmentation backbone. Third, we apply calibration methods to align confidence scores with IoU and explore s
arXiv Detail & Related papers (2025-02-18T16:43:11Z) - Real-Time Localization and Bimodal Point Pattern Analysis of Palms Using UAV Imagery [13.085752393960886]
We introduce PalmDSNet, a deep learning framework for real-time detection, segmentation, and counting of canopy palms.
We use UAV-captured imagery to create orthomosaics from 21 sites across western Ecuadorian tropical forests.
Expert annotations were used to create a comprehensive dataset, including 7,356 bounding boxes on image patches and 7,603 palm centers across five orthomosaics.
arXiv Detail & Related papers (2024-10-14T22:23:10Z) - Deep Learning tools to support deforestation monitoring in the Ivory Coast using SAR and Optical satellite imagery [0.0]
Satellites can be employed to recognize the disappearance of the forest to prevent it from expand its area of interest.
Forest-Non-Forest map (FNF) has been used as ground truth for models based on Sentinel images input.
Models classifying forest and non-forests pixels over the area using open datasets to predict where deforestation could have occurred.
arXiv Detail & Related papers (2024-09-16T14:26:41Z) - Pansharpening of PRISMA products for archaeological prospection [1.2116854758481392]
This research assesses the usability of pansharpened PRISMA satellite products in geo-archaeological prospections.
Three pan-sharpening methods (GSA, MTF-GLP and HySure) are compared quantitatively and qualitatively and tested over the archaeological landscape of Aquileia (Italy)
arXiv Detail & Related papers (2024-04-08T12:29:46Z) - SatBird: Bird Species Distribution Modeling with Remote Sensing and
Citizen Science Data [68.2366021016172]
We present SatBird, a satellite dataset of locations in the USA with labels derived from presence-absence observation data from the citizen science database eBird.
We also provide a dataset in Kenya representing low-data regimes.
We benchmark a set of baselines on our dataset, including SOTA models for remote sensing tasks.
arXiv Detail & Related papers (2023-11-02T02:00:27Z) - Spatial Implicit Neural Representations for Global-Scale Species Mapping [72.92028508757281]
Given a set of locations where a species has been observed, the goal is to build a model to predict whether the species is present or absent at any location.
Traditional methods struggle to take advantage of emerging large-scale crowdsourced datasets.
We use Spatial Implicit Neural Representations (SINRs) to jointly estimate the geographical range of 47k species simultaneously.
arXiv Detail & Related papers (2023-06-05T03:36:01Z) - A General Purpose Neural Architecture for Geospatial Systems [142.43454584836812]
We present a roadmap towards the construction of a general-purpose neural architecture (GPNA) with a geospatial inductive bias.
We envision how such a model may facilitate cooperation between members of the community.
arXiv Detail & Related papers (2022-11-04T09:58:57Z) - Neuroevolution-based Classifiers for Deforestation Detection in Tropical
Forests [62.997667081978825]
Millions of hectares of tropical forests are lost every year due to deforestation or degradation.
Monitoring and deforestation detection programs are in use, in addition to public policies for the prevention and punishment of criminals.
This paper proposes the use of pattern classifiers based on neuroevolution technique (NEAT) in tropical forest deforestation detection tasks.
arXiv Detail & Related papers (2022-08-23T16:04:12Z) - Potato Crop Stress Identification in Aerial Images using Deep
Learning-based Object Detection [60.83360138070649]
The paper presents an approach for analyzing aerial images of a potato crop using deep neural networks.
The main objective is to demonstrate automated spatial recognition of a healthy versus stressed crop at a plant level.
Experimental validation demonstrated the ability for distinguishing healthy and stressed plants in field images, achieving an average Dice coefficient of 0.74.
arXiv Detail & Related papers (2021-06-14T21:57:40Z) - Automatic Large Scale Detection of Red Palm Weevil Infestation using
Aerial and Street View Images [0.0]
The spread of the Red Palm Weevil has dramatically affected date growers, homeowners and governments.
Early detection of palm tree infestation has been proven to be critical in order to allow treatment that may save trees from irreversible damage.
Here, we present a novel method for surveillance of Red Palm Weevil infested palm trees utilizing state-of-the-art deep learning algorithms.
arXiv Detail & Related papers (2021-04-06T15:35:26Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.