Extracting Wetlands from Swiss Historical Maps with ConvolutionalNeural Networks
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Author / Producer
Date
2020
Publication Type
Conference Paper
ETH Bibliography
yes
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Abstract
Historical maps can serve as valuable resources for various kinds of researches such as ecology, land reclamation (Ngo et al, 2015), toponymy, history, etc. (Chiang, 2016). Specifically, extracting wetlands from historical maps facilitates researchers to investigate the spatio-temporal dynamics of the hydrological and ecological situation. Deep learning methods, especially Fully Convolutional Neural Networks (FCNN), provide an efficient and effective way to extract features from raster maps. To extract wetland areas from the Swiss Siegfried maps, we trained a U-Net based architecture and applied the learnt model to 573 map sheets across Switzerland. The pixel-wise prediction results were converted to polygons through a vectorization and generalization step. The vector wetland layers will be used for studies on land-cover change.
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Publication status
published
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Editor
Book title
Automatic Vectorisation of Historical Maps. International workshop organized by the ICA Commission on Cartographic Heritage into the Digital 13 March, 2020 Budapest. Proceedings
Journal / series
Volume
Pages / Article No.
33 - 38
Publisher
Department of Cartography and Geoinformatics, ELTE Eötvös Loránd University
Event
International Workshop on Automatic Vectorisation of Historical Maps (ICA 2020) (virtual)
Edition / version
Methods
Software
Geographic location
Date collected
Date created
Subject
Siegfried map, map feature extraction, Fully Convolutional Neural Networks, wetland reconstruction
Organisational unit
03466 - Hurni, Lorenz / Hurni, Lorenz
Notes
Due to the Coronavirus (COVID-19) the conference was conducted virtually.