Mapping archeological signs from airborne lidar data using deep neural networks: Primary results

Melda Küçükdemirci; Giacomo Landeschi; Nicolo Dell'Unto; Mattias Ohlsson

doi:10.4000/archeosciences.10179

Mapping archeological signs from airborne lidar data using deep neural networks: Primary results

Melda Küçükdemirci, Giacomo Landeschi, Nicolo Dell'Unto, Mattias Ohlsson

Research output: Chapter in Book/Report/Conference proceeding › Paper in conference proceeding › peer-review

Abstract

– 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.

Original language	English
Title of host publication	14th International Conference of Archaeological Prospection
Pages	291-293
Number of pages	3
Volume	45
Edition	1
DOIs	https://doi.org/10.4000/archeosciences.10179
Publication status	Published - 2021

Publication series

Name	ArcheoSciences
ISSN (Print)	1960-1360

Subject classification (UKÄ)

Computer and Information Sciences
Archaeology

Free keywords

Airborne LIDAR
Artificial intelligence
Deep neural networks
Feature extraction
Unet

Access to Document

10.4000/archeosciences.10179

Cite this

@inproceedings{23a4984edd1745e5ba9fdec714d0820e,

title = "Mapping archeological signs from airborne lidar data using deep neural networks: Primary results",

abstract = " – 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. ",

keywords = "Airborne LIDAR, Artificial intelligence, Deep neural networks, Feature extraction, Unet",

author = "Melda K{\"u}{\c c}{\"u}kdemirci and Giacomo Landeschi and Nicolo Dell'Unto and Mattias Ohlsson",

year = "2021",

doi = "10.4000/archeosciences.10179",

language = "English",

volume = "45",

series = "ArcheoSciences",

pages = "291--293",

booktitle = "14th International Conference of Archaeological Prospection",

edition = "1",

}

Mapping archeological signs from airborne lidar data using deep neural networks: Primary results. / Küçükdemirci, Melda; Landeschi, Giacomo ; Dell'Unto, Nicolo et al.
14th International Conference of Archaeological Prospection. Vol. 45 1. ed. 2021. p. 291-293 (ArcheoSciences).

Research output: Chapter in Book/Report/Conference proceeding › Paper in conference proceeding › peer-review

TY - GEN

T1 - Mapping archeological signs from airborne lidar data using deep neural networks

T2 - Primary results

AU - Küçükdemirci, Melda

AU - Landeschi, Giacomo

AU - Dell'Unto, Nicolo

AU - Ohlsson, Mattias

PY - 2021

Y1 - 2021

N2 - – 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.

AB - – 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.

KW - Airborne LIDAR

KW - Artificial intelligence

KW - Deep neural networks

KW - Feature extraction

KW - Unet

UR - https://www.scopus.com/pages/publications/85118757995

U2 - 10.4000/archeosciences.10179

DO - 10.4000/archeosciences.10179

M3 - Paper in conference proceeding

AN - SCOPUS:85118757995

VL - 45

T3 - ArcheoSciences

SP - 291

EP - 293

BT - 14th International Conference of Archaeological Prospection

ER -

Mapping archeological signs from airborne lidar data using deep neural networks: Primary results

Abstract

Publication series

Subject classification (UKÄ)

Free keywords

Access to Document

Other files and links

Fingerprint

Under the canopy: reading past agrarian landscapes through the lens of artificial intelligence and remote sensing

Laboratoriet för Digital Arkeologi DARK Lab

Cite this

Mapping archeological signs from airborne lidar data using deep neural networks: Primary results

Abstract

Publication series

Subject classification (UKÄ)

Free keywords

Access to Document

Other files and links

Fingerprint

Projects

Under the canopy: reading past agrarian landscapes through the lens of artificial intelligence and remote sensing

Equipment

Laboratoriet för Digital Arkeologi DARK Lab

Cite this