IEEE Geoscience and Remote Sensing Magazine - March 2017 - 71
LCZ 1
Compact Highrise
LCZ A
Dense Trees
LCZ 2
Compact Midrise
LCZ B
Scattered Trees
LCZ 3
Compact Lowrise
LCZ C
Bush, Scrub
LCZ 4
Open Highrise
LCZ D
Low Plants
LCZ 5
Open Midrise
LCZ E
Bare Rock or
Paved
LCZ 6
Open Lowrise
LCZ F
Bare Soil or
Sand
LCZ 7
Lightweight Lowrise
LCZ G
Water
LCZ 8
Large Lowrise
LCZ 9
Sparsely Built
LCZ 10
Heavy Industry
0
important challenge for adaptive mod-
els that must be robust to acquisition
conditions of the data and different se-
mantic meanings of the LCZ classes (a
problem known as domain adaptation,
which is intensively studied in remote
sensing literature [5]). As an outcome
to the second step, the participants are
invited to upload classified maps of the
test cities on an evaluation server.
To promote an open system that is applicable globally,
classification is to be performed with open data, which
can be freely obtained for any urban area worldwide. For
the 2017 Data Fusion Contest, the organizing committee
has provided Landsat 8, Sentinel 2 (both resampled to the
target grid), and OpenStreetMap data (both vector and
resampled 5-m grids) for the cities considered (examples
of the data over the city of Berlin can be seen in Figure 3),
but the committee also welcomes participants to make use of
the higher resolution sources (the links for direct download
are provided in the data package) as well as alternative data
sources that are open and globally available. The use of pro-
prietary data is not permitted.
Variable Land Cover Properties
b
Bare Trees
s
Snow Cover
d
Dry Ground
w
Wet Ground
100 m
© AmericAn meteorologicAl Society.
(LCZ A-G). An example of an LCZ
map over the city of Bologna, Italy,
can be seen in Figure 2.
The 2017 Data Fusion Contest is
or ganized as a global land-use clas-
sification competition to foster the
development of methodologies that
generalize beyond the data they have
been trained with [4]. The contest in-
vites participants to develop models
that are able to perform well on new cities that are unseen
during training.
Classification is to be performed at coarse resolution
(100 m × 100 m resolution grids) for a set of cities, includ-
ing their direct surroundings. The contest is organized in
two steps. In the first step, participants receive a set of data
and training labels from a selection of cities (the training
cities, which are Berlin, Rome, Paris, Sao Paolo, and Hong
Kong). These data are used to train LCZ classification mod-
els. In the second step, participants receive data from a new
set of cities but this time with labels undisclosed (the test
cities hereafter). The test cities may come from different geo-
graphical, cultural, and climatic backgrounds, raising an
FIGURE 1. The LCZ classes considered in the 2017 Data Fusion Contest. (Image used with permission from [1].)
march 2017
ieee Geoscience and remote sensing magazine
71
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