IEEE Geoscience and Remote Sensing Magazine - March 2023 - 109

Training
Validation
Test
FIGURE 4. Distribution of the AOIs and training, validation, and test sets across continents. The points represent the center of each study
area and are enlarged for better visualization.
8 million parameters. As an optimization algorithm, we
use Adam with beta values of (0.9, 0.999) and an epsilon
value of 10−8. We select the learning rate as 0.0001 and
adopt the cosine annealing learning rate schedule with the
maximum number of iterations set to 10. We use the dice
loss to quantify the cost value and set the batch size as 32.
During the training process (e.g., on the fly), we randomly
extract patches with a shape of 512 × 512 pixels from
the input images and feed them to the model. We use the
three-band (B4, B3, and B2) Sentinel-2 image queried with
the single temporal subset seasons and utilize ImageNet
pretrained weights during the feature extraction phase.
Furthermore, we configure mixed-precision training to
shorten the training and inference time and decrease the
required amount of memory. We train the model on an
Nvidia Tesla V100 SCM3 32GB. The training process is terminated
with an early stopping callback with a patience
(the number of epochs without improvement in the metric
observed) of 8. The achieved overall pixel-wise classification
accuracy, intersection over union (IoU) score, and F1
score are shown in Table 2. The quantitative results are
shown in Figure 6.
SCENE CLASSIFICATION AND
SENSITIVITY ANALYSIS
In this experiment, we perform scene classification with
a subsequent sensitivity analysis. To this end, we use the
single temporal subset of the Sentinel-2 data and extract
tiles with a height and width of 256 pixels each. The tiles
are extracted in such a way that they are located exclusively
either within or without the WDPA regions without overlapping.
Thus, we get 24,101 tiles within WDPA regions
MARCH 2023 IEEE GEOSCIENCE AND REMOTE SENSING MAGAZINE
(label 1) and 17,795 tiles outside of WDPA regions (label 0).
The proportions of the training, validation, and test set remain
approximately the same.
100
90
60
50
40
30
20
10
Tree Cover
Shrubland
Grassland
Cropland
Bare/Sparse Vegetation
Built Up
Snow and Ice
Permanent Water Bodies
Herbaceous Wetland
Mangroves
Moss and Lichen
FIGURE 5. The pixel-level breakdown of final AOIs in terms of land
cover. The land cover information is obtained from the ESA WorldCover
map existing in the dataset.
TABLE 2. QUALITATIVE RESULTS (%): BASELINE MODEL FOR
THE SEMANTIC SEGMENTATION TASK.
MODEL
U-Net
BACKBONE
ResNeSt 14d
IOU ACCURACY
69.1
76.31
F1
81.73
109
Percentage

IEEE Geoscience and Remote Sensing Magazine - March 2023

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