  1) 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 Historical documents and materials are crucial in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 Historical documents are, therefore,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.
  2) 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 J\"onk\"oping county in Sweden. These are all extracted from the
historical map called "Generalstabskartan".
  3) The presented CNN performs well and achieves a $F_1$-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.
  4) 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the extraction and digitalisation of non-textual information in historical
documents, such as historical maps.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.
