Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Mapping horizontal and vertical urban densification in Denmark with
  Landsat time-series from 1985 to 2018: a semantic segmentation solution   Landsat imagery is an unparalleled freely available data source that allows
reconstructing horizontal and vertical urban form. This paper addresses the
challenge of using Landsat data, particularly its 30m spatial resolution, for
monitoring three-dimensional urban densification. We compare temporal and
spatial transferability of an adapted DeepLab model with a simple fully
convolutional network (FCN) and a texture-based random forest (RF) model to map
urban density in the two morphological dimensions: horizontal (compact, open,
sparse) and vertical (high rise, low rise). We test whether a model trained on
the 2014 data can be applied to 2006 and 1995 for Denmark, and examine whether
we could use the model trained on the Danish data to accurately map other
European cities. Our results show that an implementation of deep networks and
the inclusion of multi-scale contextual information greatly improve the
classification and the model's ability to generalize across space and time.
DeepLab provides more accurate horizontal and vertical classifications than FCN
when sufficient training data is available. By using DeepLab, the F1 score can
be increased by 4 and 10 percentage points for detecting vertical urban growth
compared to FCN and RF for Denmark. For mapping the other European cities with
training data from Denmark, DeepLab also shows an advantage of 6 percentage
points over RF for both the dimensions. The resulting maps across the years
1985 to 2018 reveal different patterns of urban growth between Copenhagen and
Aarhus, the two largest cities in Denmark, illustrating that those cities have
used various planning policies in addressing population growth and housing
supply challenges. In summary, we propose a transferable deep learning approach
for automated, long-term mapping of urban form from Landsat images.

18
The categories of this paper are: econ
25
The categories of this paper are: patt-sol
15
The categories of this paper are: nucl-ex
3
The categories of this paper are: physics
34
The categories of this paper are: chem-ph
17
The categories of this paper are: q-fin
13
The categories of this paper are: quant-ph
33
The categories of this paper are: plasm-ph
8
The categories of this paper are: hep-ex
10
The categories of this paper are: q-bio
16
The categories of this paper are: stat
20
The categories of this paper are: acc-phys
29
The categories of this paper are: funct-an
24
The categories of this paper are: dg-ga
5
The categories of this paper are: gr-qc
7
The categories of this paper are: hep-th