Figure 5 . Diagram of the workflow to automatically map rock
glaciers using DeepLabv3+ network. AI stands for artificial
intelligence.
4 Results
We compiled an inventory consisting of 413 rock glaciers across the West
Kunlun Mountains: 290 of them were mapped by the conventional method
based on interferograms and Google Earth images, the other 123 landforms
were identified by deep learning network with supplementary
modifications to the automatically delineated boundaries (Figure 1).
In this section, we first present the accuracy of the automated mapping
method. Then we analyze the features of all the mapped rock glaciers
from the geomorphological perspective. Finally, we summarize the
kinematic characteristics of the active rock glaciers measured by InSAR.
4.1 Performance of the automated mapping approach
After iteratively training and improving the model (Sect. 3.2), we
trained a model attaining a performance of IoU = 0.801 on both the
training and validation datasets (Figure 6).
Over the entire West Kunlun region, our trained model automatically
identified and delineated 337 landforms as rock glaciers, among which
123 rock glaciers were newly discovered, 49 predicted polygons were
false positives, the rest (165) were true positives but already present
in the InSAR-based sub-dataset. Figure 7a and b present the satisfactory
accuracy of automated delineation by comparing the deep learning mapped
rock glaciers with the manually mapped boundaries in the training and
validation datasets, respectively. And Figure 7b is an example just
passing the IoU threshold. The delineation accuracy was also acceptable
for the newly discovered rock glaciers in general, as shown in Figure
7c. However, we still conducted modifications to 100 out of the 123
landforms to ensure the quality of the mapping results after manual
inspection (Figure 7d). The modification was made based on the
Sentinel-2 optical images according to the geomorphic criteria presented
in the IPA guideline (RGIK, 2021).