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Detection of ancient settlement mounds: archaeological survey based on the STRM terrain model / B.H. Menze in Photogrammetric Engineering & Remote Sensing, PERS, vol 72 n° 3 (March 2006)  

Public [article]  inPhotogrammetric Engineering & Remote Sensing, PERS > vol 72 n° 3 (March 2006) . - pp 321 - 326 Titre : Detection of ancient settlement mounds: archaeological survey based on the STRM terrain model Type de document : Article/Communication Auteurs : B.H. Menze, Auteur ; J.A. Ur, Auteur ; A.G. Sherrat, Auteur Année de publication : 2006 Article en page(s) : pp 321 - 326 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] apprentissage automatique [Termes IGN] carte archéologique [Termes IGN] classification [Termes IGN] MNS SRTM [Termes IGN] modèle numérique de terrain [Termes IGN] Proche-Orient [Termes IGN] site archéologique Résumé : (Auteur) In the present study we demonstrate the value of the SRTM three arcsecond terrain model for a virtual survey of archaeological sites : the detection and mapping of ancient settlement mounds in the Near East. These so-called "tells" are the result of millennia of occupation within the period from 8000-1000 BC, and form visible landmarks of the world's first farming and urban communities. The SRTM model provides for the first time an opportunity to scan areas not yet surveyed archaeologically on a supra-regional scale and to pinpoint probable tell sites. In order to map these historic monuments for the purpose of settlement-study and conservation, we develop a machine learning classifier which identifies probable tell sites from the terrain model. In a test, point-like elevations of a characteristic tell shape, standing out for more than 5 to 6 m in the DEM were successfully detected (85/133 tells). False positives (3271(600*1200) pixels) were primarily due to natural elevations, resembling tells in height and size. Numéro de notice : A2006-110 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.14358/PERS.72.3.321 En ligne : https://doi.org/10.14358/PERS.72.3.321 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=27837 [article]