Automated Detection of Prehistoric Tumuli in the Sahara: A Deep Learning Approach to Satellite Imagery

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dc.contributor.authorBrucato, Alessiaen_US
dc.contributor.editorCampana, Stefanoen_US
dc.contributor.editorFerdani, Danieleen_US
dc.contributor.editorGraf, Holgeren_US
dc.contributor.editorGuidi, Gabrieleen_US
dc.contributor.editorHegarty, Zackaryen_US
dc.contributor.editorPescarin, Sofiaen_US
dc.contributor.editorRemondino, Fabioen_US
dc.date.accessioned2025-09-05T19:06:45Z
dc.date.available2025-09-05T19:06:45Z
dc.date.issued2025
dc.description.abstractThis study focuses on the automated satellite-based identification, classification, and spatial interpretation of prehistoric tumuli in the Central Western Sahara. These dry-stone structures lie in environmentally and geopolitically challenging areas for fieldwork and are therefore largely understudied. They are commonly interpreted as funerary monuments, ritual installations, or territorial markers. Regardless of individual function, they often represent the only evidence of Mid- and Late Holocene nomadic Saharan pastoralists (reflecting identities, subsistence strategies, and mobility patterns) and frequently appear as recurrent visitation points near environmentally attractive areas such as pathways, pastures, rivers, and water ponds. Among the various types of Saharan dry-stone buildings, this study selects four specific archaeological classes, previously documented through fieldwork and remote sensing surveys, that are highly visible and recognizable in satellite imagery. The research adopts an innovative methodological framework combining High- and Very High-Resolution satellite datasets (optical, multispectral, and SAR-derived DEM), image enhancement techniques (spectral indices), a Deep Convolutional Neural Network, clustering methods, spatial analyses, and Least Cost Path modelling to systematically detect, classify, and interpret these features at a regional scale.en_US
dc.description.sectionheadersPredictive Analysis, AI, Simulation, and Novel Computational Methods
dc.description.seriesinformationDigital Heritage
dc.identifier.doi10.2312/dh.20253386
dc.identifier.isbn978-3-03868-277-6
dc.identifier.pages6 pages
dc.identifier.urihttps://doi.org/10.2312/dh.20253386
dc.identifier.urihttps://diglib.eg.org/handle/10.2312/dh20253386
dc.publisherThe Eurographics Associationen_US
dc.rightsAttribution 4.0 International License
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectAutomated detection; Satellite Remote Sensing; Sahara; Prehistory; Tumuli
dc.subjectAutomated detection
dc.subjectSatellite Remote Sensing
dc.subjectSahara
dc.subjectPrehistory
dc.subjectTumuli
dc.titleAutomated Detection of Prehistoric Tumuli in the Sahara: A Deep Learning Approach to Satellite Imageryen_US
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