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Mapping Archeological Signs From Airborne Lidar Data Using Deep Neural Networks: Primary Results

Melda Küçükdemirci, Giacomo Landeschi, Nicolo Dell’Unto et Mattias Ohlsson
p. 291-293

Résumé

– Complexity of large-scale Airborne LIDAR data: its processing, and interpretation emerges the necessity of automated analysis with novel techniques.

– Detection and documentation of archaeological ruins, hidden in the forests of the Swedish landscape.

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In recent decades, remote sensing technology has been frequently integrated into archaeological surveys to analyze past civilizations and their relations with the landscape in a broad sense. Today, thanks to technological developments, we can obtain high-resolution, dense, and large-scale Airborne LIDAR data in a short period. However, the complexity of these dataset, it's processing, and interpretation emerges the necessity of automated analysis with novel techniques. Recently, automated and semi-automated techniques using artificial intelligence have found their place in the field of remote sensing archaeology, and remarkable results have been published by several researchers. Lambers et al. (2019) highlight the integration of remote sensing and machine learning techniques in their papers. Trier et al. (2019) demonstrated that deep Convolutional Neural Networks (CNNs) have great potential for automated archaeological mapping. Caspari and Crespo (2019) present the CNNs for the larg...

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Référence papier

Melda Küçükdemirci, Giacomo Landeschi, Nicolo Dell’Unto et Mattias Ohlsson, « Mapping Archeological Signs From Airborne Lidar Data Using Deep Neural Networks: Primary Results »ArcheoSciences, 45-1 | 2021, 291-293.

Référence électronique

Melda Küçükdemirci, Giacomo Landeschi, Nicolo Dell’Unto et Mattias Ohlsson, « Mapping Archeological Signs From Airborne Lidar Data Using Deep Neural Networks: Primary Results »ArcheoSciences [En ligne], 45-1 | 2021, mis en ligne le 16 août 2021, consulté le 24 septembre 2021. URL : http://journals.openedition.org/archeosciences/10179 ; DOI : https://doi.org/10.4000/archeosciences.10179

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Auteurs

Melda Küçükdemirci

Corresponding author, Lund University, Digital Archaeology Lab (DARKLab), Department of Archaeology and Ancient History, Lund-Sweden

Articles du même auteur

Giacomo Landeschi

Lund University, Digital Archaeology Lab (DARKLab), Department of Archaeology and Ancient History, Lund-Sweden

Nicolo Dell’Unto

Lund University, Digital Archaeology Lab (DARKLab), Department of Archaeology and Ancient History, Lund-Sweden

Mattias Ohlsson

Lund University, Department of Theoretical Physics, Lund-Sweden

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Droits d’auteur

Article L.111-1 du Code de la propriété intellectuelle.

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