Automating global landslide detection with heterogeneous ensemble
  deep-learning classification
Alexandra Jarna Ganerød Gabriele Franch Erin Lindsay Martina Calovi
With changing climatic conditions, we are already seeing an increase in
extreme weather events and their secondary consequences, including landslides.
Landslides threaten infrastructure, including roads, railways, buildings, and
human life. Hazard-based spatial planning and early warning systems are
cost-effective strategies to reduce the risk to society from landslides.
However, these both rely on data from previous landslide events, which is often
scarce. Many deep learning (DL) models have recently been applied for landside
mapping using medium- to high-resolution satellite images as input. However,
they often suffer from sensitivity problems, overfitting, and low mapping
accuracy. This study addresses some of these limitations by using a diverse
global landslide dataset, using different segmentation models, such as Unet,
Linknet, PSP-Net, PAN, and DeepLab and based on their performances, building an
ensemble model. The ensemble model achieved the highest F1-score (0.69) when
combining both Sentinel-1 and Sentinel-2 bands, with the highest average
improvement of 6.87 % when the ensemble size was 20. On the other hand,
Sentinel-2 bands only performed very well, with an F1 score of 0.61 when the
ensemble size is 20 with an improvement of 14.59 % when the ensemble size is
20. This result shows considerable potential in building a robust and reliable
monitoring system based on changes in vegetation index dNDVI only.