Abstract
The local environment and land usages have changed a lot during the past one hundred years. Historical documents and materials are crucial in understanding and following these changes. Historical documents are, therefore, an important piece in the understanding of the impact and consequences of land usage change. This, in turn, is important in the search of restoration projects that can be conducted to turn and reduce harmful and unsustainable effects originating from changes in the land-usage.This work extracts information on the historical location and geographical distribution of wetlands, from hand-drawn maps. This is achieved by using deep learning (DL), and more specifically a convolutional neural network (CNN). The CNN model is trained on a manually pre-labelled dataset on historical wetlands in the area of Jönköping county in Sweden. These are all extracted from the historical map called “Generalstabskartan”.The presented CNN performs well and achieves a F1-score of 0.886 when evaluated using a 10-fold cross validation over the data. The trained models are additionally used to generate a GIS layer of the presumable historical geographical distribution of wetlands for the area that is depicted in the southern collection in Generalstabskartan, which covers the southern half of Sweden. This GIS layer is released as an open resource and can be freely used.To summarise, the presented results show that CNNs can be a useful tool in the extraction and digitalisation of non-textual information in historical documents, such as historical maps. A modern GIS material that can be used to further understand the past land-usage change is produced within this research. Previously, no material of this detail and extent have been available, due to the large effort needed to manually create such. However, with the presented resource better quantifications and estimations of historical wetlands that have been lost can be made.
Highlights
Historical maps hold crucial information about the landscape of the past, which is an important part in understanding ecological changes over time (Saar et al, 2012)
In this work we show that a convolutional neural network (CNN) based method can be applied to historical maps in order to extract in formation concerning the occurrence of wetlands
Further more, it can be seen that areas that are known to be densely covered by wetlands today are the areas which are most densely covered by wetlands in the models predictions, based on the historical map
Summary
Historical maps hold crucial information about the landscape of the past, which is an important part in understanding ecological changes over time (Saar et al, 2012). Older historical maps are drawn by hand without any modern systems to aid, making the layout not fully consistent. It is a time consuming challenge to extract desired information from them. The conventional approach to extract such in formation is through manual annotation, with the help of various GISsoftware. This labour intensive approach causes most current studies of historical landscapes and ecologies to be limited in size and only focusing on smaller areas or regions of particular interest, such as the study by Cousins (2009). The different land cover, need to be extracted by methods that analyses the different textures in the map, or discover different land cover implicitly, by analysing the surrounding landscape
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