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Testing Web Mapping and Active Learning to Approach Lidar Data

Published online by Cambridge University Press:  29 November 2019

Marion Forest Open the ORCID record for Marion Forest [Opens in a new window] ,
Laurent Costa ,
Andy Combey ,
Antoine Dorison  and
Grégory Pereira
Marion Forest*
Affiliation:
Department of Anthropology, Brigham Young University, 800 SWKT, Provo, UT84604, USA
Laurent Costa
Affiliation:
UMR7041 Archéologie et Science de l'Antiquité, Centre National de la Recherche Scientifique, Maison Archéologie et Ethnologie, 21 allée de l'Université, Nanterre Cedex F-92023, France
Andy Combey
Affiliation:
Institut des Sciences de la Terre, Université Grenoble Alpes, 1381 rue de la piscine, 38400, Saint Martin d'Heres, France
Antoine Dorison
Affiliation:
UFR Histoire de l'Art et Archéologie, University Paris 1 Panthéon-Sorbonne, 3 rue Michelet, 75006Paris, France
Grégory Pereira
Affiliation:
UMR8096 Archéologie des Amériques, Centre National de la Recherche Scientifique, 21 allée de l'Université, Nanterre Cedex F-92023, France
*
(corresponding author, marion_forest@byu.edu)
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Abstract

After acquiring 91 km2 of lidar data from the Zacapu region, West Mexico, we confronted a series of issues that most archaeologists using this technology face. These include the large volume of data available, the limited training of potential “analysts,” the difficult development of a collective mapping tool and protocol, and the reliability of desk-based interpretation of archaeological features. In this article, we present an initiative conducted in 2015 and 2017 as an attempt to answer these methodological and pedagogical issues. We developed a web mapping platform to collectively interpret archaeological features using lidar-derived imagery and to train volunteer students to participate in this desk-based web mapping within a crowdsourcing framework. After evaluating the results of this initiative, we discuss the potential and limitations of this method for both lidar-based research and future training.

Después de adquirir 91 km2 de datos lidar para la región de Zacapu, Occidente de México, nos hemos enfrentados a una serie de problemas recurrentes en el uso de esta tecnología en arqueología. Incluyen el amplio volumen de datos, límites en la formación de “analistas” potenciales, dificultades en el desarrollo de protocolos de mapeo colectivos, y la confiabilidad de las interpretaciones de estructuras arqueológicas con base al mapeo realizado en gabinete. En este artículo, presentamos una iniciativa conducida en 2015 y 2017 para intentar responder a estas cuestiones metodológicas y pedagógicas. Hemos desarrollado una plataforma de mapeo web para interpretar colectivamente los objetos arqueológicos visibles en las imágenes generadas del lidar, y para entrenar estudiantes voluntarios a participar en este mapeo en un marco de crowdsourcing. Al seguir una discusión sobre el nivel de precisión del dato colectado, discutimos el potencial y las limitaciones del método para la investigación arqueológica usando lidar data y la formación de futuros arqueólogos.

Keywords

lidarremote sensingsurveyweb mappingcrowdsourcingeducationactive learningstudent trainingdatos lidarteledetecciónprospecciónmapeo webcrowdsourcingeducaciónaprendizaje activoentrenamiento estudiantil
Type
Articles
Information
Advances in Archaeological Practice , Volume 8 , Issue 1 , February 2020 , pp. 25 - 39
Copyright
Copyright 2019 © Society for American Archaeology

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