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revises application area categories
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lakillo committed Jun 2, 2024
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task,authors,year,application area,data type,technique,paper,code,data
classification for ceramic elemental analysis,Ruschioni et al,2023.0,chemical analysis,x-ray fluorescence,"LR, LDA, MLP, SVM, DT, RF, NB, KNN",[paper](https://doi.org/10.1016/j.jasrep.2023.103995),[code](https://github.com/dariomalchiodi/JAS-Tarquinia-classification),[data](https://github.com/dariomalchiodi/JAS-Tarquinia-classification)
regression for stable isotope analysis,Bataille et al,2020.0,chemical analysis,strontium,RF,[paper](https://doi.org/10.1016/j.palaeo.2020.109849),[code](https://ars.els-cdn.com/content/image/1-s2.0-S0031018220302947-mmc4.zip),[data](https://ars.els-cdn.com/content/image/1-s2.0-S0031018220302947-mmc1.xlsx)
regression for stable isotope analysis,Funck et al,2020.0,chemical analysis,strontium,RF,[paper](https://doi.org/10.1002/jqs.3262),,[data](https://onlinelibrary.wiley.com/doi/10.1002/jqs.3262)
regression for stable isotope analysis,Bataille et al,2018.0,chemical analysis,strontium,RF,[paper](https://doi.org/10.1371/journal.pone.0197386),[code](https://doi.org/10.1371/journal.pone.0197386.s001),
classification for ceramic elemental analysis,Charalambous et al,2016.0,chemical analysis,x-ray fluorescence,"KNN, DT, LVQ",[paper](https://doi.org/10.1016/j.jasrep.2015.08.010),,
classification for phytoliths,Berganzo-Besga et al,2022.0,ecofact analysis,RGB images,"CNN [VGG19, ResNet50v2]",[paper](https://doi.org/10.1016/j.jas.2022.105654),[code](https://ars.els-cdn.com/content/image/1-s2.0-S0305440322001121-mmc1.zip),
classification for contexts,Vos et al,2021.0,ecofact analysis,"geochemistry, phytolith",DT,[paper](https://doi.or g/10.1371/journal.pone.0248261),,[data](https://doi.org/10.1371/journal.pone.0248261.s001)
segmentation for carved reliefs,Ji et al,2023.0,image analysis,"RGB images, depth map, soft-edge images",CNN [DenseNet121],[paper](https://doi.org/10.3390/rs15040956),,
classification for ceramic sherds,Helden et al,2022.0,image analysis,RGB images,"CNN [VGG19, Mobilenetv2, ResNet50v2, Inceptionv3]",[paper](https://doi.org/10.5334/jcaa.92),,
classification for multiple artefact types,Resler et al,2021.0,image analysis,RGB images,CNN [EfficientNetB3],[paper](https://doi.org/10.1057/s41599-021-00970-z),,[data](http://www.antiquities.org.il/t/default_en.aspx)
classification for ceramic petrography,Lyons,2021.0,image analysis,RGB images,"CNN [VGG19, ResNet50]",[paper](https://doi.org/10.5334/jcaa.75),,
object detection for rock carvings,Tsigkas et al,2020.0,image analysis,RGB images,"CNN [YOLOv2, TinyYOLOv2]",[paper](https://doi.org/10.1016/j.patrec.2020.03.026),,
proposed null dataset for lithics,Eren et al,2023.0,lithic analysis,"tbc, qual and quant info from naturally fractured rocks",,[paper](https://doi.org/10.15184/aqy.2023.4),,
classification for lithics,Grove and Blinkhorn,2020.0,lithic analysis,"lithic types, period",NN,[paper](https://doi.org/10.1371/journal.pone.0237528),[code](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0237528#sec026),[data](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0237528#sec026)
classification for contexts,Vos et al,2021.0,ecofact analysis,"geochemistry, phytolith",DT,[paper](https://doi.org/10.1371/journal.pone.0248261),,[data](https://doi.org/10.1371/journal.pone.0248261.s001)
segmentation for carved reliefs,Ji et al,2023.0,artefact analysis,"RGB images, depth map, soft-edge images",CNN [DenseNet121],[paper](https://doi.org/10.3390/rs15040956),,
classification for ceramic sherds,Helden et al,2022.0,artefact analysis,RGB images,"CNN [VGG19, Mobilenetv2, ResNet50v2, Inceptionv3]",[paper](https://doi.org/10.5334/jcaa.92),,
classification for multiple artefact types,Resler et al,2021.0,artefact analysis,RGB images,CNN [EfficientNetB3],[paper](https://doi.org/10.1057/s41599-021-00970-z),,[data](http://www.antiquities.org.il/t/default_en.aspx)
classification for ceramic petrography,Lyons,2021.0,artefact analysis,RGB images,"CNN [VGG19, ResNet50]",[paper](https://doi.org/10.5334/jcaa.75),,
object detection for rock carvings,Tsigkas et al,2020.0,artefact analysis,RGB images,"CNN [YOLOv2, TinyYOLOv2]",[paper](https://doi.org/10.1016/j.patrec.2020.03.026),,
proposed null dataset for lithics,Eren et al,2023.0,artefact analysis,"tbc, qual and quant info from naturally fractured rocks",,[paper](https://doi.org/10.15184/aqy.2023.4),,
classification for lithics,Grove and Blinkhorn,2020.0,artefact analysis,"lithic types, period",NN,[paper](https://doi.org/10.1371/journal.pone.0237528),[code](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0237528#sec026),[data](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0237528#sec026)
masked language modelling for archaeological text,Brandsen,2023.0,natural language processing,english language,BERT,[paper](https://doi.org/10.5281/zenodo.8300777),[model](https://huggingface.co/alexbrandsen/ArchaeoBERT),
named entity recognition for archaeological text,Brandsen,2023.0,natural language processing,english language,BERT,[paper](https://doi.org/10.5281/zenodo.8300777),[model](https://huggingface.co/alexbrandsen/ArchaeoBERT-NER),
masked language modelling for archaeological text,Brandsen,2023.0,natural language processing,dutch language,BERT,[paper](https://doi.org/10.5281/zenodo.8300777),[model](https://huggingface.co/alexbrandsen/ArcheoBERTje),
Expand All @@ -21,7 +16,7 @@ masked language modelling for archaeological text,Brandsen,2023.0,natural langua
named entity recognition for archaeological text,Brandsen,2023.0,natural language processing,german language,BERT,[paper](https://doi.org/10.5281/zenodo.8300777),[model](https://huggingface.co/alexbrandsen/bert-base-german-cased-archaeo-NER),
restoration/attribution for ancient Greek inscriptions,Assael et al,2022.0,natural language processing,"transcribed inscriptions, place, time",transformer,[paper](https://doi.org/10.1038/s41586-022-04448-z),[code](https://github.com/google-deepmind/ithaca),[data](https://github.com/sommerschield/iphi)
dataset for named entity recognition,Brandsen et al,2020.0,natural language processing,dutch language,named entity recognition,[paper](https://aclanthology.org/2020.lrec-1.562),,[data](https://doi.org/10.5281/zenodo.3544544)
classification for site dating,Reese,2021.0,site dating,"ceramic types, dendochronology dates",NN,[paper](https://doi.org/10.1016/j.jas.2021.105413),[code](https://github.com/kmreese-io/Reese_2021-JAS),[data]()
classification for site dating,Reese,2021.0,spatial predictive modelling,"ceramic types, dendochronology dates",NN,[paper](https://doi.org/10.1016/j.jas.2021.105413),[code](https://github.com/kmreese-io/Reese_2021-JAS),[data]()
dataset for maya site detection,Kokalj et al,2023.0,site detection,"lidar visualisations, lidar canopy height, SAR, optical satellite","object recognition, object detection, semantic segmentation",[paper](https://doi.org/10.1038/s41597-023-02455-x),,[data](https://doi.org/10.6084/m9.figshare.22202395)
segmentation for field systems,Küçükdemirci et al,2022.0,site detection,lidar DTMs,CNN [U-Net],[paper](https://onlinelibrary.wiley.com/doi/full/10.1002/arp.1886),,
classification for hollow roads,Verschoof-van der Vaart and Landauer,2021.0,site detection,lidar visualisations,CNN [ResNet34],[paper](https://doi.org/10.1016/j.culher.2020.10.009),,
Expand All @@ -31,3 +26,8 @@ regression for roman sites,Castiello and Tonini,2021.0,spatial predictive modell
regression for formative period sites,Yaworsky et al,2020.0,spatial predictive modelling,"environmental, topography","MaxEnt, RF",[paper](https://doi.org/10.1371/journal.pone.0239424),[code](https://doi.org/10.1371/journal.pone.0239424.s001),[data](https://doi.org/10.1371/journal.pone.0239424.s002)
classification for habitat suitability,Jones et al,2019.0,spatial predictive modelling,"climate, topography",RF,[paper](https://doi.org/10.1111/jbi.13684),,
classification for soil geochemistry,Oonk and Spijker,2015.0,spatial predictive modelling,soil geochemistry,"KNN, SVM, NN",[paper](https://doi.org/10.1016/j.jas.2015.04.002),,
classification for ceramic elemental analysis,Ruschioni et al,2023.0,artefact analysis,x-ray fluorescence,"LR, LDA, MLP, SVM, DT, RF, NB, KNN",[paper](https://doi.org/10.1016/j.jasrep.2023.103995),[code](https://github.com/dariomalchiodi/JAS-Tarquinia-classification),[data](https://github.com/dariomalchiodi/JAS-Tarquinia-classification)
regression for stable isotope analysis,Bataille et al,2020.0,spatial predictive modelling,"strontium, coordinates, geology, climate, environmental, anthropogenic",RF,[paper](https://doi.org/10.1016/j.palaeo.2020.109849),[code](https://doi.org/10.1016/j.palaeo.2020.109849),[data](https://doi.org/10.1016/j.palaeo.2020.109849)
regression for stable isotope analysis,Funck et al,2020.0,spatial predictive modelling,"strontium, coordinates, geology, climate, environmental",RF,[paper](https://doi.org/10.1002/jqs.3262),,[data](https://doi.org/10.1002/jqs.3262)
regression for stable isotope analysis,Bataille et al,2018.0,spatial predictive modelling,"strontium, geology, climate, environmental, topographic ",RF,[paper](https://doi.org/10.1371/journal.pone.0197386),[code](https://doi.org/10.1371/journal.pone.0197386.s001),
classification for ceramic elemental analysis,Charalambous et al,2016.0,artefact analysis,x-ray fluorescence,"KNN, DT, LVQ",[paper](https://doi.org/10.1016/j.jasrep.2015.08.010),,

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