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Identifying wetland areas in historical maps using deep convolutional neural networks
University of Skövde, School of Informatics. University of Skövde, Informatics Research Environment. Jönköping Artificial Intelligence Lab, Jönköping University, Sweden. (Skövde Artificial Intelligence Lab (SAIL))ORCID iD: 0000-0003-2128-7090
County Administrative Board of Jönköping, Sweden.
2022 (English)In: Ecological Informatics, ISSN 1574-9541, E-ISSN 1878-0512, Vol. 68, article id 101557Article in journal (Refereed) Published
Abstract [en]

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 Historical documents and materials are crucial in understanding and following these changes. Historical documents are, therefore, an important piece in the understanding of the impact and consequences of land usage change.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results show that CNNs can be a useful tool in the extraction and digitalisation of non-textual information in historical documents, such as historical maps.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. 

Place, publisher, year, edition, pages
Elsevier, 2022. Vol. 68, article id 101557
Keywords [en]
Analysis of historical maps, Convolutional neural networks, Wetland management, Wetland restoration, data set, environmental change, geographical distribution, GIS, model, Sweden
National Category
Computer Vision and Robotics (Autonomous Systems)
Research subject
Skövde Artificial Intelligence Lab (SAIL)
Identifiers
URN: urn:nbn:se:his:diva-20866DOI: 10.1016/j.ecoinf.2022.101557ISI: 000792769800006Scopus ID: 2-s2.0-85122747647OAI: oai:DiVA.org:his-20866DiVA, id: diva2:1632483
Note

CC BY 4.0

Corresponding author at: Jönköping Artificial Intelligence Lab, Jönköping University, Sweden. E-mail address: niclas.stahl@ju.se (N. Ståhl).

Available from: 2022-01-27 Created: 2022-01-27 Last updated: 2022-05-30Bibliographically approved

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Ståhl, Niclas

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