Details
| Originalsprache | Englisch |
|---|---|
| Titel des Sammelwerks | Computing Techniques For Spatio-Temporal Data in Archaeology And Cultural Heritage |
| Untertitel | Proceedings of the 2nd Workshop On Computing Techniques For Spatio-Temporal Data in Archaeology And Cultural Heritage co-located with 10th International Conference on Geographical Information Science (GIScience 2018) |
| Seiten | 21-35 |
| Seitenumfang | 15 |
| Publikationsstatus | Veröffentlicht - 2018 |
| Veranstaltung | 2nd Workshop On Computing Techniques For Spatio-Temporal Data in Archaeology And Cultural Heritage, COARCH 2018 - Melbourne, Australien Dauer: 28 Aug. 2018 → … |
Publikationsreihe
| Name | CEUR Workshop Proceedings |
|---|---|
| Herausgeber (Verlag) | CEUR Workshop Proceedings |
| Band | 2230 |
| ISSN (Print) | 1613-0073 |
Abstract
It is important to preserve archaeological monuments as they play a key role in helping us understand human history and their accomplishments for times with no or little written sources. The first step for this purpose is an efficient method for collecting and documenting information about objects of interest for archaeologists. Airborne laser scanning (ALS) is of great use in collecting and documenting detailed measurements from an area of interest. However, it is time consuming for scientists to manually analyze the collected ALS data. One possible way to automate this process is using deep neural networks. In this work, we propose a hierarchical Convolutional Neural Network (CNN) model to classify archaeological objects in ALS data. The data is acquired from the Harz mining Region in Lower Saxony, where a high density of different archaeological monuments including the UNESCO world heritage site Historic Town of Goslar, Mines of Rammelsberg, and the Upper Harz Water Management System can be found. To compare and validate our method, we run experiments on the same data set using two existing deep learning models. The first model is VGG-16; an image classification network pretrained on ImageNet2 data. The second model is a stacked autoencoders model. The results of the classification as analyzed in this paper show that our model is suitably tuned for this task as it achieves the best classification accuracy of around 91 percent, compared to 88 percent and 82 percent accuracy by the pretrained and stacked autoencoders models, respectively.
ASJC Scopus Sachgebiete
- Informatik (insg.)
- Allgemeine Computerwissenschaft
Ziele für nachhaltige Entwicklung
Zitieren
- Standard
- Harvard
- Apa
- Vancouver
- BibTex
- RIS
Computing Techniques For Spatio-Temporal Data in Archaeology And Cultural Heritage: Proceedings of the 2nd Workshop On Computing Techniques For Spatio-Temporal Data in Archaeology And Cultural Heritage co-located with 10th International Conference on Geographical Information Science (GIScience 2018). 2018. S. 21-35 (CEUR Workshop Proceedings; Band 2230).
Publikation: Beitrag in Buch/Bericht/Sammelwerk/Konferenzband › Aufsatz in Konferenzband › Forschung › Peer-Review
}
TY - GEN
T1 - Deep Learning for Archaeological Object Detection in Airborne Laser Scanning Data
AU - Kazimi, Bashir
AU - Thiemann, Frank
AU - Malek, Katharina
AU - Sester, Monika
AU - Khoshelham, Kourosh
N1 - Funding information: The project is funded by the Ministry of Science in Lower Saxony. The joint work with Kourosh Khoshelham has been supported by the DAAD. We gratefully acknowledge the support of NVIDIA Corporation with the donation of the Titan X Pascal GPU used for this research.
PY - 2018
Y1 - 2018
N2 - It is important to preserve archaeological monuments as they play a key role in helping us understand human history and their accomplishments for times with no or little written sources. The first step for this purpose is an efficient method for collecting and documenting information about objects of interest for archaeologists. Airborne laser scanning (ALS) is of great use in collecting and documenting detailed measurements from an area of interest. However, it is time consuming for scientists to manually analyze the collected ALS data. One possible way to automate this process is using deep neural networks. In this work, we propose a hierarchical Convolutional Neural Network (CNN) model to classify archaeological objects in ALS data. The data is acquired from the Harz mining Region in Lower Saxony, where a high density of different archaeological monuments including the UNESCO world heritage site Historic Town of Goslar, Mines of Rammelsberg, and the Upper Harz Water Management System can be found. To compare and validate our method, we run experiments on the same data set using two existing deep learning models. The first model is VGG-16; an image classification network pretrained on ImageNet2 data. The second model is a stacked autoencoders model. The results of the classification as analyzed in this paper show that our model is suitably tuned for this task as it achieves the best classification accuracy of around 91 percent, compared to 88 percent and 82 percent accuracy by the pretrained and stacked autoencoders models, respectively.
AB - It is important to preserve archaeological monuments as they play a key role in helping us understand human history and their accomplishments for times with no or little written sources. The first step for this purpose is an efficient method for collecting and documenting information about objects of interest for archaeologists. Airborne laser scanning (ALS) is of great use in collecting and documenting detailed measurements from an area of interest. However, it is time consuming for scientists to manually analyze the collected ALS data. One possible way to automate this process is using deep neural networks. In this work, we propose a hierarchical Convolutional Neural Network (CNN) model to classify archaeological objects in ALS data. The data is acquired from the Harz mining Region in Lower Saxony, where a high density of different archaeological monuments including the UNESCO world heritage site Historic Town of Goslar, Mines of Rammelsberg, and the Upper Harz Water Management System can be found. To compare and validate our method, we run experiments on the same data set using two existing deep learning models. The first model is VGG-16; an image classification network pretrained on ImageNet2 data. The second model is a stacked autoencoders model. The results of the classification as analyzed in this paper show that our model is suitably tuned for this task as it achieves the best classification accuracy of around 91 percent, compared to 88 percent and 82 percent accuracy by the pretrained and stacked autoencoders models, respectively.
KW - Archaeology
KW - Deep Learning
KW - Object Detection
UR - http://www.scopus.com/inward/record.url?scp=85055631304&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:85055631304
T3 - CEUR Workshop Proceedings
SP - 21
EP - 35
BT - Computing Techniques For Spatio-Temporal Data in Archaeology And Cultural Heritage
T2 - 2nd Workshop On Computing Techniques For Spatio-Temporal Data in Archaeology And Cultural Heritage, COARCH 2018
Y2 - 28 August 2018
ER -