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image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao ZENODOarrow_drop_down
image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao
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Dataset . 2024
Data sources: ZENODO
ZENODO
Dataset . 2024
Data sources: Datacite
ZENODO
Dataset . 2024
Data sources: Datacite
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Archaeoscape: LiDAR archaeology ML dataset

Authors: École Française d'Extrême-Orient;

Archaeoscape: LiDAR archaeology ML dataset

Abstract

Archaeoscape We present Archaeoscape, a novel open-access dataset for archaeological research, spanning 888 km² in Cambodia with 31,141 expert-annotated archaeological features from the Angkorian period. Archaeoscape is over four times larger than comparable datasets, and the first ALS archaeology resource with open-access data, annotations, and models. This work has been presented at NeurIPS 2024 Track Datasets and Benchmarks as a Spotlight Poster. arXiv: https://arxiv.org/abs/2412.05203 openreview: https://openreview.net/forum?id=QpF3DFP3Td website: https://archaeoscape.ai/data/2024/ Description The 888 km² dataset is split into 23 non-overlapping parcels assigned to: Training set: 623 km², 16 parcels. Validation set: 97 km², 3 parcels. Test set: 168 km², 4 parcels. It includes high-resolution (0.5m) orthophotos and LiDAR-derived normalized Digital Terrain Models (nDTM), encompassing over 3.5 billion pixels with RGB values, elevation data, and polygonal annotations. The annotations cover 5 classes: Temple (827 instances, 0.2% pixels). From monumental complexes to small shrines. Mound (14,400, 8.6%). Earthen features indicating habitation, embankments, crafting sites. Hydrology (16,184, 10.4%). Hydro-engineering features like rivers, ponds, canals and reservoirs. Void (3,145, 2.5%). Ambiguous areas, excluded from evaluation. Background (78.3%). Regions lacking distinguishable archaeological features. To protect sensitive archaeological sites, the data is distributed without georeferencing and released through credentialized open access - users must provide their credentials and explicitly agree to the license terms prohibiting re-georeferencing, commercial use, and redistribution.

Keywords

Machine Learning, LiDAR, Segmentation, Archaeology, Computer Vision, Khmer

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
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