Learning and Recognizing Archeological Features from LiDAR Data

Conrad M. Albrecht; Chris Fisher; Marcus Freitag; Hendrik F. Hamann; Sharathchandra Pankanti; Florencia Pezzutti; Francesca Rossi

doi:10.1109/BigData47090.2019.9005548

Learning and Recognizing Archeological Features from LiDAR Data

Conrad M. Albrecht
, Chris Fisher
, Marcus Freitag
, Hendrik F. Hamann
, Sharathchandra Pankanti
, Florencia Pezzutti
, Francesca Rossi

Office of the Dean SOMAS

IBM
Colorado State University

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

34 Scopus citations

Abstract

We present a remote sensing pipeline that processes LiDAR (Light Detection And Ranging) data through machine deep learning for the application of archeological feature detection on big geo-spatial data platforms such as e.g. IBM PAIRS Geoscope [1], [2].Today, archeologists get overwhelmed by the task of visually surveying huge amounts of (raw) LiDAR data in order to identify areas of interest for inspection on the ground. We showcase a software system pipeline that results in significant savings in terms of expert productivity while missing only a small fraction of the artifacts.Our work employs artificial neural networks in conjunction with an efficient spatial segmentation procedure based on domain knowledge. Data processing is constraint by a limited amount of training labels and noisy LiDAR signals due to vegetation cover and decay of ancient structures. We aim at identifying geo-spatial areas with archeological artifacts in a supervised fashion allowing the domain expert to flexibly tune parameters based on her needs.

Original language	English
Title of host publication	Proceedings - 2019 IEEE International Conference on Big Data, Big Data 2019
Editors	Chaitanya Baru, Jun Huan, Latifur Khan, Xiaohua Tony Hu, Ronay Ak, Yuanyuan Tian, Roger Barga, Carlo Zaniolo, Kisung Lee, Yanfang Fanny Ye
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	5630-5636
Number of pages	7
ISBN (Electronic)	9781728108582
DOIs	https://doi.org/10.1109/BigData47090.2019.9005548
State	Published - Dec 2019
Event	2019 IEEE International Conference on Big Data, Big Data 2019 - Los Angeles, United States Duration: Dec 9 2019 → Dec 12 2019

Publication series

Name	Proceedings - 2019 IEEE International Conference on Big Data, Big Data 2019

Conference

Conference	2019 IEEE International Conference on Big Data, Big Data 2019
Country/Territory	United States
City	Los Angeles
Period	12/9/19 → 12/12/19

Keywords

LiDAR data processing
archeology
machine learning
remote sensing applications

Access to Document

10.1109/BigData47090.2019.9005548

Cite this

Albrecht, C. M., Fisher, C., Freitag, M., Hamann, H. F., Pankanti, S., Pezzutti, F., & Rossi, F. (2019). Learning and Recognizing Archeological Features from LiDAR Data. In C. Baru, J. Huan, L. Khan, X. T. Hu, R. Ak, Y. Tian, R. Barga, C. Zaniolo, K. Lee, & Y. F. Ye (Eds.), Proceedings - 2019 IEEE International Conference on Big Data, Big Data 2019 (pp. 5630-5636). Article 9005548 (Proceedings - 2019 IEEE International Conference on Big Data, Big Data 2019). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/BigData47090.2019.9005548

Albrecht, Conrad M. ; Fisher, Chris ; Freitag, Marcus et al. / Learning and Recognizing Archeological Features from LiDAR Data. Proceedings - 2019 IEEE International Conference on Big Data, Big Data 2019. editor / Chaitanya Baru ; Jun Huan ; Latifur Khan ; Xiaohua Tony Hu ; Ronay Ak ; Yuanyuan Tian ; Roger Barga ; Carlo Zaniolo ; Kisung Lee ; Yanfang Fanny Ye. Institute of Electrical and Electronics Engineers Inc., 2019. pp. 5630-5636 (Proceedings - 2019 IEEE International Conference on Big Data, Big Data 2019).

@inproceedings{bf4cda59e81b4df68a11e0d0165fb01b,

title = "Learning and Recognizing Archeological Features from LiDAR Data",

abstract = "We present a remote sensing pipeline that processes LiDAR (Light Detection And Ranging) data through machine deep learning for the application of archeological feature detection on big geo-spatial data platforms such as e.g. IBM PAIRS Geoscope [1], [2].Today, archeologists get overwhelmed by the task of visually surveying huge amounts of (raw) LiDAR data in order to identify areas of interest for inspection on the ground. We showcase a software system pipeline that results in significant savings in terms of expert productivity while missing only a small fraction of the artifacts.Our work employs artificial neural networks in conjunction with an efficient spatial segmentation procedure based on domain knowledge. Data processing is constraint by a limited amount of training labels and noisy LiDAR signals due to vegetation cover and decay of ancient structures. We aim at identifying geo-spatial areas with archeological artifacts in a supervised fashion allowing the domain expert to flexibly tune parameters based on her needs.",

keywords = "LiDAR data processing, archeology, machine learning, remote sensing applications",

author = "Albrecht, \{Conrad M.\} and Chris Fisher and Marcus Freitag and Hamann, \{Hendrik F.\} and Sharathchandra Pankanti and Florencia Pezzutti and Francesca Rossi",

note = "Publisher Copyright: {\textcopyright} 2019 IEEE.; 2019 IEEE International Conference on Big Data, Big Data 2019 ; Conference date: 09-12-2019 Through 12-12-2019",

year = "2019",

month = dec,

doi = "10.1109/BigData47090.2019.9005548",

language = "English",

series = "Proceedings - 2019 IEEE International Conference on Big Data, Big Data 2019",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "5630--5636",

editor = "Chaitanya Baru and Jun Huan and Latifur Khan and Hu, \{Xiaohua Tony\} and Ronay Ak and Yuanyuan Tian and Roger Barga and Carlo Zaniolo and Kisung Lee and Ye, \{Yanfang Fanny\}",

booktitle = "Proceedings - 2019 IEEE International Conference on Big Data, Big Data 2019",

}

Albrecht, CM, Fisher, C, Freitag, M, Hamann, HF, Pankanti, S, Pezzutti, F & Rossi, F 2019, Learning and Recognizing Archeological Features from LiDAR Data. in C Baru, J Huan, L Khan, XT Hu, R Ak, Y Tian, R Barga, C Zaniolo, K Lee & YF Ye (eds), Proceedings - 2019 IEEE International Conference on Big Data, Big Data 2019., 9005548, Proceedings - 2019 IEEE International Conference on Big Data, Big Data 2019, Institute of Electrical and Electronics Engineers Inc., pp. 5630-5636, 2019 IEEE International Conference on Big Data, Big Data 2019, Los Angeles, United States, 12/9/19. https://doi.org/10.1109/BigData47090.2019.9005548

Learning and Recognizing Archeological Features from LiDAR Data. / Albrecht, Conrad M.; Fisher, Chris; Freitag, Marcus et al.
Proceedings - 2019 IEEE International Conference on Big Data, Big Data 2019. ed. / Chaitanya Baru; Jun Huan; Latifur Khan; Xiaohua Tony Hu; Ronay Ak; Yuanyuan Tian; Roger Barga; Carlo Zaniolo; Kisung Lee; Yanfang Fanny Ye. Institute of Electrical and Electronics Engineers Inc., 2019. p. 5630-5636 9005548 (Proceedings - 2019 IEEE International Conference on Big Data, Big Data 2019).

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

TY - GEN

T1 - Learning and Recognizing Archeological Features from LiDAR Data

AU - Albrecht, Conrad M.

AU - Fisher, Chris

AU - Freitag, Marcus

AU - Hamann, Hendrik F.

AU - Pankanti, Sharathchandra

AU - Pezzutti, Florencia

AU - Rossi, Francesca

PY - 2019/12

Y1 - 2019/12

N2 - We present a remote sensing pipeline that processes LiDAR (Light Detection And Ranging) data through machine deep learning for the application of archeological feature detection on big geo-spatial data platforms such as e.g. IBM PAIRS Geoscope [1], [2].Today, archeologists get overwhelmed by the task of visually surveying huge amounts of (raw) LiDAR data in order to identify areas of interest for inspection on the ground. We showcase a software system pipeline that results in significant savings in terms of expert productivity while missing only a small fraction of the artifacts.Our work employs artificial neural networks in conjunction with an efficient spatial segmentation procedure based on domain knowledge. Data processing is constraint by a limited amount of training labels and noisy LiDAR signals due to vegetation cover and decay of ancient structures. We aim at identifying geo-spatial areas with archeological artifacts in a supervised fashion allowing the domain expert to flexibly tune parameters based on her needs.

AB - We present a remote sensing pipeline that processes LiDAR (Light Detection And Ranging) data through machine deep learning for the application of archeological feature detection on big geo-spatial data platforms such as e.g. IBM PAIRS Geoscope [1], [2].Today, archeologists get overwhelmed by the task of visually surveying huge amounts of (raw) LiDAR data in order to identify areas of interest for inspection on the ground. We showcase a software system pipeline that results in significant savings in terms of expert productivity while missing only a small fraction of the artifacts.Our work employs artificial neural networks in conjunction with an efficient spatial segmentation procedure based on domain knowledge. Data processing is constraint by a limited amount of training labels and noisy LiDAR signals due to vegetation cover and decay of ancient structures. We aim at identifying geo-spatial areas with archeological artifacts in a supervised fashion allowing the domain expert to flexibly tune parameters based on her needs.

KW - LiDAR data processing

KW - archeology

KW - machine learning

KW - remote sensing applications

UR - https://www.scopus.com/pages/publications/85081385643

U2 - 10.1109/BigData47090.2019.9005548

DO - 10.1109/BigData47090.2019.9005548

M3 - Conference contribution

AN - SCOPUS:85081385643

T3 - Proceedings - 2019 IEEE International Conference on Big Data, Big Data 2019

SP - 5630

EP - 5636

BT - Proceedings - 2019 IEEE International Conference on Big Data, Big Data 2019

A2 - Baru, Chaitanya

A2 - Huan, Jun

A2 - Khan, Latifur

A2 - Hu, Xiaohua Tony

A2 - Ak, Ronay

A2 - Tian, Yuanyuan

A2 - Barga, Roger

A2 - Zaniolo, Carlo

A2 - Lee, Kisung

A2 - Ye, Yanfang Fanny

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2019 IEEE International Conference on Big Data, Big Data 2019

Y2 - 9 December 2019 through 12 December 2019

ER -

Albrecht CM, Fisher C, Freitag M, Hamann HF, Pankanti S, Pezzutti F et al. Learning and Recognizing Archeological Features from LiDAR Data. In Baru C, Huan J, Khan L, Hu XT, Ak R, Tian Y, Barga R, Zaniolo C, Lee K, Ye YF, editors, Proceedings - 2019 IEEE International Conference on Big Data, Big Data 2019. Institute of Electrical and Electronics Engineers Inc. 2019. p. 5630-5636. 9005548. (Proceedings - 2019 IEEE International Conference on Big Data, Big Data 2019). doi: 10.1109/BigData47090.2019.9005548

Learning and Recognizing Archeological Features from LiDAR Data

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