On February 6, 2023 (local time), two earthquakes (Mw7.8 and Mw7.7) struck central and southern Turkey, causing extensive damage to several cities and claiming a toll of 40,000 lives. In this study, we propose a method for seismic building damage assessment and analysis by combining SAR amplitude and phase coherence change detection. We determined building damage in five severely impacted urban areas and calculated the damage ratio by measuring the urban area and the damaged area. The largest damage ratio of 18.93% is observed in Nurdagi, and the smallest ratio of 7.59% is found in Islahiye. We verified the results by comparing them with high-resolution optical images and AI recognition results from the Microsoft team. We also used pixel offset tracking (POT) technology and D-InSAR technology to obtain surface deformation using Sentinel-1A images and analyzed the relationship between surface deformation and post-earthquake urban building damage. The results show that Nurdagi has the largest urban average surface deformation of 0.48 m and Antakya has the smallest deformation of 0.09 m. We found that buildings in the areas with steeper slopes or closer to earthquake faults have higher risk of collapse. We also discussed the influence of SAR image parameters on building change recognition. Image resolution and observation geometry have a great influence on the change detection results, and the resolution can be improved by various means to raise the recognition accuracy. Our research findings can guide earthquake disaster assessment and analysis and identify influential factors of earthquake damage.

In February 2023, Mw 7.8 and Mw 7.7 earthquakes struck southeastern Turkey. Generating a coseismic 3D deformation field that can directly reflect the characteristics of surface deformation is important for revealing the movement mode of a seismogenic fault and analyzing the focal mechanism. Optical image sub-pixel correlation (SPC) only captures deformation in the horizontal direction, and SAR image pixel offset tracking (POT) obtains range deformation that is not sensitive to north–south deformation signals. Thus, neither of them can capture the complete 3D deformation alone. Combining them may be able to allow the monitoring of 3D deformation. In this study, we used Sentinel-2 optical images to obtain the horizontal deformation (east–west and north–south) and Sentinel-1 and ALOS-2 data to extract the range and azimuth offsets. The least-squares method was used to fuse the optical and SAR offsets to obtain the 3D deformation field of the 2023 Turkey earthquake sequence, which indicates that the two events were both left-lateral strike-slip earthquakes. The surface deformation caused by the two large earthquakes is mainly in the east–west direction. In the vertical direction, the two earthquakes caused a small-magnitude uplift and subsidence. The findings in this paper can be used as a reference for the study of coseismic 3D deformation.

The joint action of human activities and environmental changes contributes to the frequent occurrence of landslide, causing major hazards. Using Interferometric Synthetic Aperture Radar (InSAR) technique enables the detailed detection of surface deformation, facilitating early landslide detection. The growing availability of SAR data and the development of artificial intelligence have spurred the integration of deep learning methods with InSAR for intelligent geological identification. However, existing studies using deep learning methods to detect landslides in InSAR deformation often rely on single InSAR data, which leads to the presence of other types of geological hazards in the identification results and limits the accuracy of landslide identification. Landslides are affected by many factors, especially topographic features. To enhance the accuracy of landslide identification, this study improves the existing geological hazard detection model and proposes a multi-source data fusion network termed MSFD-Net. MSFD-Net employs a pseudo-Siamese network without weight sharing, enabling the extraction of texture features from the wrapped deformation data and topographic features from topographic data, which are then fused in higher-level feature layers. We conducted comparative experiments on different networks and ablation experiments, and the results show that the proposed method achieved the best performance. We applied our method to the middle and upper reaches of the Yellow River in eastern Qinghai Province, China, and obtained deformation rates using Sentinel-1 SAR data from 2018 to 2020 in the region, ultimately identifying 254 landslides. Quantitative evaluations reveal that most detected landslides in the study area occurred at an elevation of 2500–3700 m with slope angles of 10–30°. The proposed landslide detection algorithm holds significant promise for quickly and accurately detecting wide-area landslides, facilitating timely preventive and control measures.

Many coastal cities reclaim land from the sea to meet the rapidly growing demand for land caused by population growth and economic development. Settlement in reclaimed land may delay construction and even damage infrastructures, so accurately predicting the settlement over reclaimed areas is important. However, the limited settlement observation and ambiguous final settlement estimation affect accurate settlement prediction in traditional methods. This study proposes a new strategy to solve these problems by using the Multitemporal Interferometric Synthetic Aperture Radar (MT-InSAR) method and takes the Xiamen Xiang’an International Airport, built on reclaimed land, as an example. The MT-InSAR is adopted to process the Sentinel-1 images to obtain the settlement history of the study area. The results show that settlement mainly occurs in the reclaimed areas, with the maximum average settlement rate exceeding 40 mm/y. We use the statistical properties of curve fitting to choose the best curve model from several candidate curve models to predict the settlement time series. The Asaoka method is used to identify the critical state between settlement and stability. We predict the consolidation time of the whole study area and reveal that the deformation rate is positively correlated with the consolidation time. The maximum remaining settlement time is over ten years since 24 December 2019. Therefore, manual compaction operations can be carried out to speed up settlement in the areas that need a long time to consolidate. The proposed method can be used to predict the settlement of similar reclaimed areas, and the predicted results can provide a reference for engineering construction.