A Layer of Late Victorian London: The Building Footprints from the 1:1,056 Ordnance Survey Map (1891–1896)
References
- Bartholomew, J. G. (1899). The Pocket Atlas and Guide to London. John Walker & Co.
- Chazalon, J., Carlinet, E., Chen, Y., Perret, J., Dumenieu, B., Mallet, C., & Géraud, T. (2021). ICDAR 2021 Competition on Historical Map Segmentation — Dataset. 10.1007/978-3-030-86337-1_46
- Chen, Y., Carlinet, E., Chazalon, J., Mallet, C., Duménieu, B., & Perret, J. (2021).
Vectorization of Historical Maps Using Deep Edge Filtering and Closed Shape Extraction . In J. Lladós, D. Lopresti & S. Uchida (Eds.), Document Analysis and Recognition – ICDAR 2021 (pp. 510–525). Springer. 10.1007/978-3-030-86337-1_34 - Chen, Y., Chazalon, J., Carlinet, E., Ngoc, M. Ô. V., Mallet, C., & Perret, J. (2024). Automatic vectorization of historical maps: A benchmark. PLOS ONE, 19(2),
e0298217 . 10.1371/journal.pone.0298217 - Cheng, B., Misra, I., Schwing, A. G., Kirillov, A., & Girdhar, R. (2022). Masked-attention mask transformer for universal image segmentation. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition (pp. 1280–1289). New Orleans, LA, USA. 10.1109/CVPR52688.2022.00135
- Göderle, W. T., Rampetsreiter, F., Macher, C., Mauthner, K., & Pimas, O. (2024). Deep learning for historical Cadastral maps and satellite imagery analysis: insights from Styria’s Franciscean Cadastre. Digital humanities quarterly, 18(3). 10.63744/asjzgcbkgkyy
- Hammond, L., & Rashidi, S. E. (2018). ‘Layers of London’ — a rich geographical palimpsest. Geography, 103(1), 42–45. 10.1080/00167487.2018.12094034
- Heitzler, M., & Hurni, L. (2020). Cartographic reconstruction of building footprints from historical maps: A study on the Swiss Siegfried map. Transactions in GIS, 24(2), 442–461. 10.1111/tgis.12610
- Herold, H., & Hecht, R. (2018).
3D Reconstruction of Urban History Based on Old Maps . In S. Münster, K. Friedrichs, F. Niebling & A. Seidel-Grzesińska (Eds.), Digital Research and Education in Architectural Heritage (pp. 63–79). Springer International Publishing. 10.1007/978-3-319-76992-9_5 - Hyde, R. (1975). Printed Maps of Victorian London: 1851-1900. Dawson.
- Intel (2020). Computer vision annotation tool.
https://cvat.ai/ - Litvine, A. D., Starzec, A., Younis, R., Faula, Y., Coustaty, M., Shaw-Taylor, L., & Églin, V. (2024). Built-up areas of nineteenth-century Britain. An integrated methodology for extracting high-resolution urban footprints from historical maps. Historical Methods: A Journal of Quantitative and Interdisciplinary History, 57(1), 1–19. 10.1080/01615440.2024.2324163
- McDonough, K., Beelen, K., Wilson, D. C., & Wood, R. (2024). Reading Maps at a Distance: Texts on Maps as New Historical Data. Imago Mundi, 76(2), 296–307. 10.1080/03085694.2024.2453336
- National Library of Scotland (2013). Ordnance Survey Maps, London, Large-Scale Town Plans, 1840s-1890s.
https://maps.nls.uk/os/townplans-england/london-1056-1890s.html - Oliveira, S. A., di Lenardo, I., Tourenc, B., & Kaplan, F. (2019).
A deep learning approach to Cadastral Computing . In DH2019. Utrecht, Netherlands.https://hal.science/hal-03988983v1 - Oliver, R. (2013). Ordnance Survey Maps: A Concise Guide for Historians, 3rd edn. Charles Close Society.
- OpenMMLab (2022). MMSegmentation: OpenMMLab semantic segmentation toolbox and benchmark.
https://github.com/open-mmlab/mmsegmentation - Petitpierre, R. (2026). Generalizable multiscale segmentation of heterogeneous map collections. 10.48550/arXiv.2603.05037
- Petitpierre, R., & Guhennec, P. (2023). Effective annotation for the automatic vectorization of cadastral maps. Digital Scholarship in the Humanities, 38(3), 1227–1237. 10.1093/llc/fqad006
- Petitpierre, R., di Lenardo, I., & Rappo, L. (2024).
Revealing the structure of land ownership through the automatic vectorisation of Swiss cadastral plans . In DigiHistCH24 Book of Abstracts. Basel, Switzerland.https://digihistch24.github.io/submissions/454/ . 10.5281/zenodo.13904641 - Vaienti, B., Petitpierre, R., di Lenardo, I., & Kaplan, F. (2023). Machine-Learning-Enhanced Procedural Modeling for 4D Historical Cities Reconstruction. Remote Sensing, 15(13), 3352. 10.3390/rs15133352
DOI: https://doi.org/10.5334/johd.602 | Journal eISSN: 2059-481X
Language: English
Page range: 111 - 111
Submitted on: May 28, 2026
Accepted on: Jul 2, 2026
Published on: Aug 13, 2026
Published by: Ubiquity Press
In partnership with: Paradigm Publishing Services
Keywords:
© 2026 Remi Petitpierre, Isabella di Lenardo, Polly Hudson, Hendrik Herold, Katherine McDonough, Robert Hecht, Beatrice Vaienti, Christopher Fleet, published by Ubiquity Press
This work is licensed under the Creative Commons Attribution 4.0 License.