Machine learning-ready remote sensing data for Maya archaeology

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

doi:10.1038/s41597-023-02455-x

Cited by 3 publications

(1 citation statement)

References 51 publications

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“…Semi-and fully automated feature extraction has a growing history in archaeological research (Câmara et al 2022;Davis 2019;Fiorucci et al 2020;Lambers and Traviglia 2016). Researchers have successfully developed methods to identify a range of archaeological feature types, including mounds and earthworks (Berganzo-Besga et al 2021;Caspari and Crespo 2019;Cerrillo-Cuenca 2017;Kokalj et al 2023;Meyer-Heß, Pfeffer, and Juergens 2022;Orengo et al 2020;Sărășan et al 2020), charcoal kilns and hearths (Bonhage et al 2021;Davis and Lundin 2021;Trier, Reksten, and Løseth 2021), roadways (Verschoof-van der Vaart and Landauer 2020), agricultural and other subsistence features (Bickler and Jones 2021;Küçükdemirci et al 2022;Trier and Pilø 2012) and craters from wartime artillery (Magnini, Bettineschi, and De Guio 2017), among others.…”

Section: Automated Feature Extraction In Archaeologymentioning

confidence: 99%

Bathymetric LiDAR and Semi‐Automated Feature Extraction Assist Underwater Archaeological Surveys

Davis,

Cook Hale,

Hale

et al. 2024

Archaeological Prospection

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Critical data concerning key developments in global human history now lie submerged on continental shelves where investigations confront significant challenges. Whereas underwater excavations and surveys are expensive and weather dependent and require specialized training and equipment, remote sensing methods can improve chances for success offshore. A refinement in one method, a semi‐automated analysis protocol that can help to identify Pleistocene and Holocene era archaeological deposits in bathymetric LiDAR datasets, is presented here. This method employs contour mapping to identify potential archaeological features in shallow water environments in Apalachee Bay, Florida. This method successfully re‐identified multiple previously recorded archaeological sites in the study region and detected at least four previously undocumented archaeological sites. These results suggest that this procedure can expand on methods to identify and record submerged archaeological deposits in sediment‐starved, shallow‐water environments.

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Section: Automated Feature Extraction In Archaeologymentioning

confidence: 99%

Bathymetric LiDAR and Semi‐Automated Feature Extraction Assist Underwater Archaeological Surveys

Davis,

Cook Hale,

Hale

et al. 2024

Archaeological Prospection

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Deep learning and satellite remote sensing for biodiversity monitoring and conservation

Pettorelli,

Williams,

Schulte to Bühne

et al. 2024

Remote Sens Ecol Conserv

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In the context of the current nature crisis, being able to reliably and cost‐effectively track subtle changes in the biosphere across adequate spatial and temporal extents and resolutions is crucial. Deep learning represents a group of versatile approaches to image processing tasks that are increasingly combined with satellite remote sensing imagery to monitor biodiversity and inform ecology and conservation, yet an overview of the opportunities and challenges associated with this development has so far been lacking. Here, we provide an interdisciplinary perspective on current research and technological developments associated with satellite remote sensing and deep learning that have the potential to make a difference in biodiversity monitoring and wildlife conservation; highlight challenges to the broader adoption of these approaches by experts operating at the interface between satellite remote sensing and ecology and conservation; and discuss how these can be overcome. By enabling the leveraging of big data and by providing new ways to learn about biodiversity and its dynamics, deep learning approaches promise to become a powerful tool to help address current monitoring needs and knowledge gaps. In certain situations, deep learning approaches may moreover substantially reduce the time and resources required to analyse satellite imagery. However, issues relating to capacity building, reference data access, environmental costs as well as model interpretability, robustness and alignment need to be addressed to successfully capitalize on these opportunities.

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Convolutional neural networks for accurate identification of mining remains from UAV-derived images

Fernández-Alonso,

Fernández-Lozano,

García-Ordás

2023

Appl Intell

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A new deep learning system is proposed for the rapid and accurate identification of anthropogenic elements of the Roman mining infrastructure in NW Iberia, providing a new approach for automatic recognition of different mining elements without the need for human intervention or implicit subjectivity. The recognition of archaeological and other abandoned mining elements provides an optimal test case for decision-making and management in a broad variety of research fields. A new image dataset was created by obtaining UAV images from different anthropic features. A convolutional neural network architecture was implemented, achieving recognition results of close to 95% accuracy. This methodological approach is suitable for the identification and accurate location of ancient mines and hydrologic infrastructure, providing new tools for accurate mapping of mining landforms. Additionally, this novel application of deep learning can be implemented to reduce potential risks caused by abandoned mines, which can cause significant annual human and economic losses worldwide.

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Machine learning-ready remote sensing data for Maya archaeology

Cited by 3 publications

References 51 publications

Bathymetric LiDAR and Semi‐Automated Feature Extraction Assist Underwater Archaeological Surveys

Bathymetric LiDAR and Semi‐Automated Feature Extraction Assist Underwater Archaeological Surveys

Deep learning and satellite remote sensing for biodiversity monitoring and conservation

Convolutional neural networks for accurate identification of mining remains from UAV-derived images

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