Landslides pose a greater potential risk to the Sichuan-Tibet Transportation Project, and extensive landslide inventory and mapping are essential to prevent and control geological hazards along the Sichuan-Tibet Transportation Corridor (STTC). Recently proposed landslide detection methods mainly focused on new landslides with high vegetation. In addition, there are still challenges in automatic detection of old landslides using optical images. In this paper, two methods, namely mask region-based convolutional neural networks (Mask R-CNN) and transfer learning Mask R-CNN (TL-Mask R-CNN), are presented for detecting and segmenting new and old landslides, respectively. An optical remote sensing dataset for landslide recognition along the Sichuan-Tibet Transportation Corridor (LRSTTC) is constructed as an evaluation benchmark. Our experimental results show that the recall rate and F1-score of the proposed method for new landslide detection can reach 78.47% and 79.80%, respectively. Transfer learning is adopted to detect old landslides, and our experimental results show that evaluation indices can be further improved by about 10%. Furthermore, TL-Mask R-CNN has been applied to identify ice avalanches based on the characteristics of landslides. It appears that our proposed methods can detect and segment landslides effectively along the STTC with the constructed LRSTTC dataset, which is essential for studying and preventing landslide hazards in mountainous areas.

Small Baseline Subset InSAR (SBAS InSAR) utilizes a series of SAR interferograms to generate a time series that can analyse the surface displacements of coherent points. Still, atmospheric errors in InSAR measurements can seriously affect the reliability of the surface displacement time series. In this paper, a new approach incorporating the Generic Atmospheric Correction Online Service for InSAR (GACOS) and principal component analysis (PCA) is proposed to reduce atmospheric errors in SBAS InSAR. Its application to Southern California, US suggests that the incorporation of GACOS and PCA can efficiently reduce atmospheric effects on InSAR observations and hence improve the accuracy of InSAR derived surface displacements. The overall standard deviations of the SAR interferograms were reduced from 4.97 rad to 2.02 rad after applying GACOS and PCA with the root mean square error (RMSE) reducing by 61.1% from 18 mm to 7 mm. In addition, comparisons between different PCA processing strategies suggest that the more principal components are removed, the smaller the standard deviations of the interferograms, but the lower the accuracy of InSAR derived surface displacements.

Emergency response after earthquakes, especially rapid access to building damage information, is of great significance to ensure timely rescue and reduce casualties. However, manual field surveys of building damages are inefficient and dangerous, and optical satellite data are more susceptible to cloud interference after earthquakes. Synthetic Aperture Radar (SAR) is now widely used in disaster response efforts due to its full-time and all-weather capability. According to the change in coherence between SAR images before and after earthquakes, it is possible to identify damaged buildings that cause coherence loss. However, the accuracy of traditional coherence-based damage detection methods is relatively low due to biases in coherence estimation and inconsistency in spatio-temporal baselines. In this study, we propose a new method to produce a post-earthquake Building Damage Proxy Map (BDPM) based on multi-temporal Sentinel-1 coherence, which incorporates homogeneous SAR pixel coherence estimation and histogram matching techniques. The former is used to reduce estimation biases and the latter to reduce the effect of baseline inconsistencies in adjacent coherence maps. We successfully applied this method to the 2022 Mw 6.2 Afghanistan earthquake, the 2023 strong earthquake sequence in Turkey, and the 2023 M s 6.2 Jishishan, China earthquake. We also verified its accuracy (over 80%) by comparing the BDPM with results from the United Nations Institute for Training and Research (UNITAR) and analyzed various factors affecting the distribution of damaged buildings. These analyses confirm the effectiveness of our method for generating BDPM using free medium-resolution Sentinel-1 data, which will greatly assist in earthquake emergency response.

SUMMARY: The Mw 6.8 Murghob earthquake is the third earthquake in an Mw 6.4+ sequence occurring in the Pamir initiated by the 2015 Sarez Mw 7.2 earthquake. It is of great significance to investigate their interactions and to assess future seismic hazards in the region. In this paper, we use Sentinel-1 radar interferometric data to retrieve coseismic deformation, invert for the slip distributions of the four events, and then investigate their interactions. The cumulative Coulomb failure stress changes (ΔCFS) suggest that the 2023 Murghob earthquake was promoted by the three prior earthquakes in the sequence. Pre-stress from historical earthquakes is a key factor in explaining the triggering mechanism of the two 2016 Mw 6.4+ earthquakes. Stress loading and unloading effects on major faults in the region indicate that future attention should be paid in (1) the segment of the Sarez-Karakul fault north of the Kokuibel Valley, (2) the segment of the Sarez-Murghab thrust fault west of the Sarez-Karakul fault and (3) the east segments of the Pamir thrust fault system, all with a large positive ΔCFS.