Rainfall is the primary landslide triggering factor in China, and the spatial-temporal hazard prediction of rainfall-induced landslides is of great practical significance. Currently, most countries and regions establish landslide hazard prediction systems based on rainfall data only, resulting in low spatial precision of hazard prediction results and a high false alarm rate. This paper proposes a hazard prediction model that considers landslide triggering factors, landslide predisposing environment, and the spatial regularity of historical landslides based on multi-modal earth observation data. The proposed model has significantly improved the spatial-temporal hazard prediction performance of rainfall-induced natural terrain landslides in Hong Kong.

Ice roads serve as vital land transportation during the Arctic winter season. In the context of polar increased warming, there are great uncertainties for human activities on ice roads. In this paper, we integrate remote sensing techniques to quantify the potential ice road evolution in the Pan-Arctic and its impact on land accessibility from 1979 to 2017. We show that the potential ice roads have significantly decreased, with the fastest decrease in March to 2.34×104km2yr−1. Furthermore, the contribution of potential ice roads to port accessibility is most severely reduced in the Canadian Arctic, reaching 0.93 h yr−1. The results demonstrate that warmer winters are imposing severe stress on Arctic land access.

In the identification of geological hazards in a wide area, the rapid extraction of deformation areas in the InSAR phase becomes an important part of whether the hazards can be quickly and accurately identified. In practical applications, there is a significant difference in the size of deformation regions over a wide area. Therefore, this article uses the Bilateral Segmentation Network (Bisenet), which calculates in parallel through two branches. We first utilize a small step spatial path to preserve spatial information and generate high-resolution features. Meanwhile, a context path with a fast downsampling strategy is employed to obtain sufficient receptive fields. On top of these two paths, we utilize a new feature fusion module to effectively combine features. This greatly improves the extraction speed while preserving multilayer feature fusion and preserving information extraction results at different scales. A suitable balance has been achieved between speed and segmentation performance, which well meets the current work of identifying geological hazards in wide areas.

Slow-moving landslide detection is of vital importance in preventing and mitigating geohazards. Extracting abstract features from remote sensing images is crucial for achieving high-precision detection of slow-moving landslides. This study utilizes both activity features and terrain structure features for geohazard detection. We propose a pixel-level and a feature-level image fusion network, and investigate the multi-level fusion cooperative mechanism. We evaluate the performance of the two-level fusion and single-modal data base on the test data. The experimental results demonstrate that fusion of the activity characteristics and topographic characteristics can enhance the accuracy of identifying slow-moving landslides. Feature-level fusion outperforms pixel-level fusion for slow-moving landslides identification.

Recent advancements in Vison Foundation Models (VFMs) like the Segment Anything Model (SAM) have exhibited remarkable progress in natural image segmentation. However, its performance on remote sensing images is limited, especially in some application scenarios that require strong expert knowledge involvement, such as landslide detection. In this study, we proposed an effective segmentation model, namely LandslideNet, which is realized by embedding a tuning layer in a pre-trained encoder and adapting the SAM to the landslide detection scene for the first time. The proposed method is compared with traditional convolutional neural networks (CNN) on two well-known landslide datasets. The results indicate that the proposed model with fewer training parameters has better performance in detecting small-scale targets and delineating landslide boundaries, with an improvement of 6-7 percentage points in accuracy (F1 and mIoU) compared to mainstream CNN-based methods.

Tropospheric delay significantly hinders the accurate acquisition of high-precision surface deformation by Time series Interferometric Synthetic Aperture Radar (InSAR). The main challenge for current InSAR tropospheric delay estimation lies in effectively utilizing the time-dependent characteristics of the tropospheric delay for accurate atmospheric delay estimation. This paper develops a model to estimate the time-dependent and stochastic components of the delay based on periodic and random characteristics. The experiment demonstrates the effectiveness of the proposed method regardless of whether terrain-dependent delays dominate or random delays dominate at Danba-Xiaojin. The Std of the corrected decreases in 89/90 of the interferograms. The average and maximum improvement of Std is more than 40% and 80% respectively. From the time series, the proposed method can effectively suppress the periodic signals in both non-deformation and deformation regions and can obtain smoother time series. Overall, the proposed method outperforms the other four models for InSAR tropospheric delay correction.