Evidence map›Paper›PMID 37612295›Full record

ArticleScientific data2023

Machine learning-ready remote sensing data for Maya archaeology.

Žiga Kokalj, Sašo Džeroski, Ivan Šprajc, Jasmina Štajdohar, Andrej Draksler, Maja Somrak

Open access · goldAbstract readDataset
In one paragraph

Article in Scientific data, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
11.7field-weighted citation impact, top 2% of its field
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

2 citing papers in PubMed, 15 citations in OpenAlex.

  1. Article
  2. Article
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 at 2 institutions in 1 country.

Žiga KokaljResearch Centre of the Slovenian Academy of Sciences and Arts (ZRC SAZU), Novi trg 2, 1000, Ljubljana, Slovenia. ziga.kokalj@zrc-sazu.si.ORCID http://orcid.org/0000-0003-1712-0351
Sašo DžeroskiInformation and Communication Technologies, Jožef Stefan International Postgraduate School, Jamova cesta 39, 1000, Ljubljana, Slovenia.ORCID http://orcid.org/0000-0003-2363-712X
Ivan ŠprajcResearch Centre of the Slovenian Academy of Sciences and Arts (ZRC SAZU), Novi trg 2, 1000, Ljubljana, Slovenia.
Jasmina ŠtajdoharResearch Centre of the Slovenian Academy of Sciences and Arts (ZRC SAZU), Novi trg 2, 1000, Ljubljana, Slovenia.
Andrej DrakslerResearch Centre of the Slovenian Academy of Sciences and Arts (ZRC SAZU), Novi trg 2, 1000, Ljubljana, Slovenia.
Maja SomrakResearch Centre of the Slovenian Academy of Sciences and Arts (ZRC SAZU), Novi trg 2, 1000, Ljubljana, Slovenia.
Slovenian Academy of Sciences and Arts · SIJožef Stefan Institute · SI

Funding

European Space Agency (ESA) 4000130508/20/I-NBJavna Agencija za Raziskovalno Dejavnost RS (Slovenian Research Agency) J6-7085Javna Agencija za Raziskovalno Dejavnost RS (Slovenian Research Agency) P2-0406Javna Agencija za Raziskovalno Dejavnost RS (Slovenian Research Agency) P6-0079
6 · The paper itself

Abstract

In our study, we set out to collect a multimodal annotated dataset for remote sensing of Maya archaeology, that is suitable for deep learning. The dataset covers the area around Chactún, one of the largest ancient Maya urban centres in the central Yucatán Peninsula. The dataset includes five types of data records: raster visualisations and canopy height model from airborne laser scanning (ALS) data, Sentinel-1 and Sentinel-2 satellite data, and manual data annotations. The manual annotations (used as binary masks) represent three different types of ancient Maya structures (class labels: buildings, platforms, and aguadas - artificial reservoirs) within the study area, their exact locations, and boundaries. The dataset is ready for use with machine learning, including convolutional neural networks (CNNs) for object recognition, object localization (detection), and semantic segmentation. We would like to provide this dataset to help more research teams develop their own computer vision models for investigations of Maya archaeology or improve existing ones.

Identifiers

PMID37612295
PMCPMC10447422
OpenAlexW4386098076

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.