Deep Learning for Rapid Landslide Detection using Synthetic Aperture Radar (SAR) Datacubes

Abstract

With climate change predicted to increase the likelihood of landslide events, there is a growing need for rapid landslide detection technologies that help inform emergency responses. Synthetic Aperture Radar (SAR) is a remote sensing technique that can provide measurements of affected areas independent of weather or lighting conditions. Usage of SAR, however, is hindered by domain knowledge that is necessary for the pre-processing steps and its interpretation requires expert knowledge. We provide simplified, pre-processed, machine-learning ready SAR datacubes for four globally located landslide events obtained from several Sentinel-1 satellite passes before and after a landslide triggering event together with segmentation maps of the landslides. From this dataset, using the Hokkaido, Japan datacube, we study the feasibility of SAR-based landslide detection with supervised deep learning (DL). Our results demonstrate that DL models can be used to detect landslides from SAR data, achieving an Area under the Precision-Recall curve exceeding 0.7. We find that additional satellite visits enhance detection performance, but that early detection is possible when SAR data is combined with terrain information from a digital elevation model. This can be especially useful for time-critical emergency interventions. Code is made publicly available at https://github.com/iprapas/landslide-sar-unet.

Performance Improvement of Deep Convolutional Networks for Aerial Imagery Segmentation of Natural Disaster-Affected Areas

“…Research [41] achieved the highest mIoU value of 43%. Research [42] achieved an Area under the Precision-Recall curve (AUPRC) value exceeding 0.7.…”

“…Research [41] used augmentation consisting of random rotations and vertical and horizontal flips; the model was trained for 200 epochs with a dynamic learning rate of 0.001, Adam was used as optimization, the model was trained with four different batch sizes (16,32,64,128), and 30% of each dataset was used as validation data. Research [42] used the Adam optimizer with 100 epoc; the learning rate was 0.01. For the overall training setting, research [43] used the SGD optimizer, batch size set to 16, and used data augmentation random flipping, random resizing, and cropping.…”

“…Research [41] used three different datasets created by RapidEye, the Normalized Vegetation Index (NDVI), and the digital elevation model (DEM). Synthetic Aperture Radar (SAR) datacubes were used in the study [42]. Research [43] used Sentinel-2 and ALOS PALSAR data.…”

“…Other studies analyzed and evaluated the performance of popular semantic segmentation methods named DeepLabv3+, Pyramid Scene Parsing Network (PSPNet), and ENet on problems related to natural disaster datasets [27], detecting and segmenting important objects in aerial footage of disaster locations used Mask-Region Based Convolutional Neural Networks (Mask-RCNN) and PSPNet [28], segmentation of damage to buildings after a natural disaster used MSNet [29], a self-attention-based semantic segmentation named ReDNet on a disaster UAV dataset and compared with three other advanced segmentation models: ENet, DeepLabv3+, and PSPNet [30], flood detection based on CNN AlexNet to extract flood-related features from disaster zone images [31], semantic segmentation of aerial images for post-flood landscape understanding by applying three advanced semantic segmentation networks namely ENet, PSPNet, and DeepLabv3+ [32], detecting buildings damaged after an earthquake used a network model Convolutional neural network VGG-16, VGG-19, and NASNet [33], semantic segmentation of natural disaster datasets used self-attentionbased methods combined with Global Average Pooling and U-Net [34], semantic segmentation of post-flood datasets with U-Net, PSPNet, and DeepLabV3+ [35], detecting flooding used segmentation with three deep neural networks: PSPNet, DeepLabV3, and U-Net [36], and extracted residential buildings with a modified Mask R-CNN [37], semantic segmentation of volcanic ash eruptions used SegNet and U-Net convolutional neural networks for volcano monitoring in volcanic eruptions [38], landslide detection and identification used Lightweight Attention U-Net [39], finding buildings damaged by disasters used transfers-learning deep attention network (TDA-Net) [40], and semantic segmentation to detect landslides used U-Net [41], [42], and self-training method [43].…”