Buildings Extraction from Historical Topographic Maps via a Deep Convolution Neural Network

Christos Xydas, Anastasios L. Kesidis, Kleomenis Kalogeropoulos, Andreas Tsatsaris

2022

Abstract

The cartographic representation is static by definition. Therefore, reading a map of the past can provide information, which corresponds to the accuracy, technology, as well as scientific knowledge of the time of their creation. Digital technology enables the current researcher to "copy" a historical map and "transcribe" it to today. In this way, a cartographic reduction from the past to the present is possible, with parallel visualization of new information (historical geodata), which the researcher has at his disposal, in addition to the background. In this work a deep learning approach is presented for the extraction of buildings within historical topographic maps. A deep convolution neural network based on the U-Net architecture is trained by a large number of images patches in a deep image-to-image regression mode in order to effectively isolate the buildings from the topographic map while ignoring other surrounding or overlapping information like texts or other irrelevant geospatial features. Several experimental scenarios on a historical census topographic map investigate the applicability of the method under various patch sizes as well as patch sampling methods. The so far results show that the proposed method delivers promising outcomes in terms of building detection accuracy.

Paper Citation

in Harvard Style

Xydas C., Kesidis A., Kalogeropoulos K. and Tsatsaris A. (2022). Buildings Extraction from Historical Topographic Maps via a Deep Convolution Neural Network. In Proceedings of the 17th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2022) - Volume 5: VISAPP; ISBN 978-989-758-555-5, SciTePress, pages 485-492. DOI: 10.5220/0010839700003124

in Bibtex Style

@conference{visapp22,
author={Christos Xydas and Anastasios L. Kesidis and Kleomenis Kalogeropoulos and Andreas Tsatsaris},
title={Buildings Extraction from Historical Topographic Maps via a Deep Convolution Neural Network},
booktitle={Proceedings of the 17th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2022) - Volume 5: VISAPP},
year={2022},
pages={485-492},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010839700003124},
isbn={978-989-758-555-5},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 17th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2022) - Volume 5: VISAPP
TI - Buildings Extraction from Historical Topographic Maps via a Deep Convolution Neural Network
SN - 978-989-758-555-5
AU - Xydas C.
AU - Kesidis A.
AU - Kalogeropoulos K.
AU - Tsatsaris A.
PY - 2022
SP - 485
EP - 492
DO - 10.5220/0010839700003124
PB - SciTePress