IEEE Geoscience and Remote Sensing Magazine - March 2023 - 108

patch should they be solely interested in the single temporal
information.
The final distribution of the AOIs sampled from the
WDPA polygons is given in Figure 4. Figure 5 shows the
percentage of ESA WorldCover classes falling in the AOIs.
BASELINE EXPERIMENTS
In the following experiments, we aim to discover the concept
of wilderness in two different ways: by semantic segmentation
and scene classification with a focus on sensitivity analysis.
Both experiments work toward providing initial efforts
in understanding wilderness. For both experiments, out
of 1,018 patches, we used 80%, 10%, and 10% of them for
training, validation, and test sets, respectively. The patch IDs
of the sets are made publicly available, and the distribution
of the sets is shown in Figure 4. For the semantic segmentation
experiment, we unify the three protected area classes
in the annotation sources to form binary wilderness masks.
SEMANTIC SEGMENTATION
EXPERIMENTAL SETUP
For this experiment, we adopt the U-Net architecture [33]
with ResNeSt 14d [34]. The model consists of approximately
WDPA
WDPA
Category Ia
Category Ib
WDPA
Category II
Applying Category-Wise
Minimum Area Threshold
Filtering Terrestrial Polygons
Köppen-Geiger Climate
Classification Map
Stratified Sampling
ESA WorldCover Map
Intersection Detection
Using Climate Map as a
Proxy to Calculate Class-Wise
ESAWorldCover Weights
Applying a Buffer
Fitting a Bounding
Box to Polygon
Export
Removing Close Polygons
Taking Centroids of Polygons
Sampled
WDPA
Sampled
WDPA
Category Ia
Category Ib
Sampled
WDPA
Category II
FIGURE 3. Sampling workflow: the guided stratified sampling of the WDPA polygons. The WDPA polygons that are varying in size and shape
are filtered through a semiautomated sampled process that is guided by the climate zones and the land cover types.
108
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