Evidence map›Paper›PMID 42453637›Full record

ArticleNPJ heritage science2026

Multiscale anthropogenic feature detection in the Argentinian Andes: satellite machine learning, UAV, and pedestrian survey.

Manuel J H Peters, Amina Jambajantsan, Veronica Zuccarelli Freire, Laura Pey, José María Vaquer, Patrick Roberts

Abstract read
In one paragraph

Article in NPJ heritage science, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Manuel J H PetersDepartment of Coevolution of Land Use and Urbanisation, Max Planck Institute of Geoanthropology, Jena, Germany.
Amina JambajantsanMAPSS, Max Planck Institute of Geoanthropology, Jena, Germany.
Veronica Zuccarelli FreireDepartment of Coevolution of Land Use and Urbanisation, Max Planck Institute of Geoanthropology, Jena, Germany.
Laura PeyConsejo Nacional de Investigaciones Científicas y Técnicas (CONICET) - Instituto de Arqueología, Facultad de Filosofía y Letras, Universidad de Buenos Aires, Buenos Aires, Argentina.
José María VaquerConsejo Nacional de Investigaciones Científicas y Técnicas (CONICET) - Instituto de Arqueología, Facultad de Filosofía y Letras, Universidad de Buenos Aires, Buenos Aires, Argentina.
Patrick RobertsDepartment of Coevolution of Land Use and Urbanisation, Max Planck Institute of Geoanthropology, Jena, Germany.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The Cusi Cusi micro region in Argentina's Andes presents a unique challenge for cultural heritage research. This rugged area lies within the Puna of Jujuy at elevations from 3800 to 4200 metres above sea level. Archaeological, historical, and features indicate long term human presence, including livestock enclosures, agricultural terraces, and settlement structures. The region is situated within the 'Lithium Triangle' and faces challenges in resource ownership, Indigenous rights, and landscape change, highlighting the importance of documenting cultural and environmental heritage. We present an integration of satellite and unpiloted aerial vehicle (UAV) imagery with pedestrian survey datasets, machine learning (ML), and physical ground truthing to explore anthropogenic activities in the landscape. Satellite-based ML detects features while UAV data improves resolution and assessment of present activities. Targeted pedestrian survey validates results and provides potential dating. This combined approach identifies loci of activities and offers a framework for investigating other rugged regions.

Identifiers

PMID42453637
PMCPMC13364662

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.