The synthetic aperture radar (SAR) interferometry (InSAR) has been developed for more than 20 years for historical surface deformation reconstruction. In particular, the onboard Sentinel-1/A/B satellite, newly planned NASA-ISRO SAR (NISAR), and Germany Tandem-L will continue to provide unprecedented SAR data with an increased number of acquisitions. However, processing of real-time SAR data has been experiencing challenges regarding the InSAR deformation parameter estimation over a long time with the small baseline subsets (SBAS) InSAR technology. We use sequential adjustment for the estimation of the deformation parameters, which uses Bayesian estimation theory under the least square criteria to inverse long time-series deformation dynamically. Finally, both simulated and real Sentinel-1A SAR data verify the performance of the sequential estimation. It can be regarded as an effective data processing tool in the coming era of SAR big data.

Landslide extraction is one of the most popular topics in remote sensing. Numerous techniques have been proposed to manage the landslide identification problem. However, most aim to extract landslides that have already occurred or delineate the potential landslide manually. It is greatly important to identify and delineate potential landslides automatically, which has not been investigated. In this paper, we propose an automatic identification and delineation method, i.e., object-based image analysis (OBIA) of potential landslides by integrating optical imagery with a deformation map. We applied a deformation map generated by the interferometric synthetic aperture radar (InSAR) technique, rather than the digital elevation model (DEM) for landslide segmentation. Then, we used a classification and regression tree (CART) model with the spectral, spatial, contextual and deformation characteristics for landslide classification. For accuracy assessment, we implemented the evaluation indicators of recall and precision. The proposed method is verified in both specific landslide-prone regions (Jinpingzi and Shuanglongtan landslides) and a large catchment of the Jinsha River, China. By comparing our results with the ones using purely optical imagery, the precision of the Jinpingzi landslide is improved by 14.12%, and the recall and precision of the Shuanglongtan landslide are improved by 3.1% and 3.6%, respectively, and the recall for the large catchment is improved by 9.95%. Our method can improve delineation of potential landslides significantly, which is crucial for landslide early warning and prevention.

The Pusa landslide, in Guizhou, China, occurred on 28 August 2017, caused 26 deaths with 9 missing. However, few studies about the pre-event surface deformation are provided because of the complex landslide formation and failure mechanism. To retrieve the precursory signal of this landslide, we recovered pre-event deformation with multi-sensor synthetic aperture radar (SAR) imagery. First, we delineated the boundary and source area of the Pusa landslide based on the coherence and SAR intensity maps. Second, we detected the line-of-sight (LOS) deformation rate and time series before the Pusa landslide with ALOS/PALSAR-2 and Sentinel-1A/B SAR imagery data, where we found that the onset of the deformation is four months before landslide event. Finally, we conceptualized the failure mechanism of the Pusa landslide as the joint effects of rainfall and mining activity. This research provides new insights into the failure mechanism and early warning of rock avalanches.

The eastern Beijing plain has been suffering severe subsidence for the last decades, mainly associated with the long-term excessive extraction of groundwater resource. Since the end of 2014, the annual water supply in Beijing plain has reached several hundred million cubic meters because of the South-to-North Water Diversion (SNWD) Project, which has reduced the groundwater exploitation and changed the status of land subsidence. In this work, we first obtain the current spatiotemporal variations of land subsidence in the eastern Beijing plain by using progressive small baseline subsets (SBAS) InSAR time series analysis method with Sentinel-1 SAR data acquired from July 2015 to December 2021. Then, we analyze the correlations between InSAR-derived subsidence and groundwater level change by applying the cross wavelet method. The results show that two major subsidence zones are successfully detected with the maximum deformation rate of −150 mm/yr and maximum cumulative deformation of −950 mm. Besides, the ground deformation at different stages from 2016 to 2021 reveal that the area and magnitude of major deformation significantly slow down, even in the regions with severe subsidence, especially in the year of 2017, which is about two years later than the start time of SNWD Project in Beijing. Further, we find the InSAR-derived subsidence lags groundwater level change with about 1–2-month lagging time, indicating that the dynamic variation of groundwater level fluctuation may be the main factor affecting the uneven subsidence in the severe subsiding zones. Last, differential subsidence rates are identified at both sides of geological faults, such as Nankou-Sunhe fault and Nanyuan-Tongxian fault, from the observed deformation map, which could be explained that the groundwater flow is blocked when a fault is encountered. These findings can provide significant information to reveal the deformation mechanisms of land subsidence, establish the hydrogeological models and assist decision-making, early warning and hazard relief in Beijing, China.

High-precision monitoring of landslides is essential for understanding their kinematics and reducing landslide induced damage. The spatiotemporal deformation variations of the Three Bears landslide in northern California have not been systematically monitored and interpreted. In this study, we applied advanced time-series InSAR analysis methods to characterize the kinematics of the landslide covering two periods (2007–2011 and 2015–2017) with multi-track synthetic aperture radar (SAR) images acquired from L-band ALOS PALSAR-1/2 satellites. Our results show that the Three Bears landslide has been moving consistently, with the Line of Sight (LOS) deformation rate exceeding 300 mm/yr from 2007 to 2011 and around 250 mm/yr from 2015 to 2017. The east–west and vertical deformation components were inverted by integrating ascending and descending ALOS PALSAR-2 interferograms during the 2015–2017 period, indicating that the landslide was dominated by eastward movement and in a continuous deformation stage. Down-slope landslide motions observed from adjacent satellite tracks with slightly different radar look vectors were used to verify the accuracy of InSAR-derived results. Comparison between linearly detrended InSAR displacements and precipitation records indicate that the landslide tends to accelerate during the wet seasons. The results could allow us to better understand the kinematics of the landslide and provide significant evidence for evaluating the potential for catastrophic failure and the threat posed by such failure to human life and property. Combined with a proper geotechnical/geomechanical model, the results would also facilitate the design and implementation of mitigation measures.

Road intersections serve as critical topological nodes in transportation networks, and their rapid, accurate identification is essential for intelligent transportation systems and geographic information updates. However, direct detection of road intersections on large-scale high-resolution remote sensing imagery remains challenging due to intersection structural diversity, complex illumination conditions, and vegetation occlusion, which make it difficult to balance detection accuracy and computational efficiency. To address these challenges, Swin Transformer-GSConv-efficient IoU-YOLO (SW-GSE-YOLO), a novel lightweight and high-precision road intersection detection model, is proposed. The model incorporates three key innovations: 1) a Swin-C2PSA module that is designed by integrating the shifted-window self-attention mechanism of the Swin Transformer with the C2PSA structure, enabling efficient large-scale feature modeling while enhancing local detail and global contextual representation; 2) a GS3k2 lightweight network architecture is built upon GSConv and CSPNet, which reduces the parameter count while preserving feature extraction capability; and 3) the efficient intersection over union (EIoU) loss function is adopted to optimize bounding box regression and improve intersection center localization accuracy. To validate the proposed method, the RIS-42k dataset is constructed, comprising 42 372 annotated image patches of size $400\times 400$ pixels. Experimental results demonstrate that SW-GSE-YOLO outperforms recent mainstream lightweight detectors, including You Only Look Once (YOLO)v8–YOLOv12 and real-time DEtection TRansformer (RT-DETR). Compared with the YOLO11n baseline, it achieves improvements of 5.9%, 1.3%, 3.9%, and 14.6% in precision, recall, mAP at 0.5, and mAP at [0.5:0.95], respectively, while significantly reducing GPU memory usage and inference latency for large-scale image processing. Furthermore, SW-GSE-YOLO demonstrates robust localization accuracy and strong generalization capability across complex urban, suburban, and rural environments.