IEEE Geoscience and Remote Sensing Magazine - March 2023 - 111

Station in Brazil. Here, the segmentation map does not include
any annotation. This could be explained by the observation
that this area exhibits human influence in the
form of roads and arable lands, which clearly does not
comply with the " undisturbedness " and " undevelopedness "
states of the wilderness areas. The last sample shows
Cape Coral, Florida. This is one of the samples we have
manually added to the dataset (notice the 9 at the beginning
of the ID). This sample shows that the model does
not annotate an urban area as wilderness, although there
are golf courses, inner city parks, football courts, and forest
areas on the perimeter. This sample also set forth the
ability of the model to capture the " disturbedness " and
" developedness " of an area. All things considered, this
experiment exhibits that the model is competent to discover
wilderness areas on a pixel scale. While doing so,
the model provides a more viable annotation of wilderness
areas as an output. With this observation, we go a
step further and analyze the models' output with the use
of explainable ML techniques to take a deeper look at the
models' decisions.
TABLE 3. THE CONFUSION MATRIX OF THE TEST DATASET
FOR THE CLASSIFICATION TASK. PERCENTAGES ARE
GIVEN IN BRACKETS.
PREDICTION
NONWILD
LABEL
Nonwild
Wild
1,062 [62.65%]
439 [17.79%]
WILD
633 [37.34%]
2,028 [82.2%]
6
4
2
-2
-4
-6
Sensit
Sensitivity
FIGURE 8. The activation space of the sensitivity analysis. Each axis
corresponds to the values of the respective channel in the activation
maps. The greener the vectors, the more they are sensitive to
wilderness characteristics; the more violet, the more sensitive to
nonwilderness characteristics. Low-density areas are not included
in the sensitivity mapping, and the corresponding vectors are not
shown here.
MARCH 2023 IEEE GEOSCIENCE AND REMOTE SENSING MAGAZINE
111
256
128
64
64
Input Image
32
Copy Convolution
256
256
256
256
256
256
12
128 128
128 128
64 64
32 32
U-Net
Pooling and Convolution
Bilinear Upsampling and Convolution
Activation Map
Fully Connected Linear Layer
FIGURE 7. For the sensitivity analysis, we use a neural network consisting of a modified U-Net and an image classifier. The activation map at the interface is essential for determining the sensitivities.
(Source: [21]; adapted slightly.)
1,536
Classifier
6
1
384
24
24
(µ)
256
256
128
64
32
16
32
64
8
128
256
256
84
27
8
First Channel
Activation Map
First Chan
atio
cond Channel
vation Map
Second Channel
Activation Map
Third Channel
Activation Map
1
0.5
5
-0.5
-0.5
-1
1
0.5
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