Urban Climate News - FAU

Feature 12
Figure 4. Homogenous LCZ patches from classification and floristic heat island pattern UHI EIT.
improved more slowly (because of the high redundancy
in the dataset). RF and NN reached a plateau
at about 50 features while SVM performed less well
but still improved with larger datasets. Again NN
and RF outperformed the other classifiers. All predefined
feature sets were represented within the
top 20 selected features.
In the third experiment, different configurations
of the best classifiers NN and RF were tested with
one feature set. TIR as the best predefined set and
50 MRMR features were selected. Table 1 shows that
the results were slightly improved with more complex
classifiers. However, which classifier performed
best depended on the feature set.
Thermal differentiation with floristic UHI pattern
The floristic heat island pattern and the homogenous
patches of selected classes from the neuronal
network classification with 100 features (MRMR/
MIQ) are shown in Fig. 4. Thermal differences be-
Table 1: Results of experiment 3 – different NN and
RF classifiers. F: number of features, C: number of
classes. Best input parameters for respective classifier
and feature set are highlighted in blue colour.
hidden
layers / trees
tir_20 tir
MRMR/
MID 50
ISSUE NO. 46 DECEMBER 2012 INTERNATIONAL ASSOCIATION FOR URBAN CLIMATE
Neuronal Network
Random Forest
40,30,20
50,30,15
F
F,C
F,F,C
10 trees
30 trees, 20 feat
50 trees, 30 feat
60 trees, 20 feat
0.940
0.932
0.934
0.925
0.924
0.922
0.921
0.920
0.923
0.962
0.967
0.963
0.951
0.966
0.922
0.938
0.936
0.938
0.939
0.946
0.960
0.952
0.955
0.924
0.948
0.945
0.951