In remote sensing images, the existence of the clouds has a great impact on the image quality and subsequent image processing, as the images covered with clouds contain little useful information. Therefore, the detection and recognition of clouds is one of the major problems in the application of remote sensing images. Present there are two categories of method to cloud detection. One is setting spectrum thresholds based on the characteristics of the clouds to distinguish them. However, the instability and uncertainty of the practical clouds makes this kind of method complexity and weak adaptability. The other method adopts the features in the images to identify the clouds. Since there will be significant overlaps in some features of the clouds and grounds, the detection result is highly dependent on the effectiveness of the features. This paper presented a cloud detection method based on feature extraction for remote sensing images. At first, find out effective features through training pattern, the features are selected from gray, frequency and texture domains. The different features in the three domains of the training samples are calculated. Through the result of statistical analysis of all the features, the useful features are picked up to form a feature set. In concrete, the set includes three feature vectors, respectively, the gray feature vector constituted of average gray, variance, first-order difference, entropy and histogram, the frequency feature vector constituted of DCT high frequency coefficient and wavelet high frequency coefficient, and the texture feature vector constituted of the hybrid entropy and difference of the gray-gradient co-occurrence matrix and the image fractal dimension. Secondly, a thumbnail will be obtained by down sampling the original image and its features of gray, frequency and texture are computed. Last but not least, the cloud region will be judged by the comparison between the actual feature values and the thresholds determined by the sample training process. Experimental results show that the clouds and ground objects can be separated efficiently, and our method can implement rapid clouds detection and cloudiness calculation.

Deformation along a subway rail network is related to the safe operation of the subway and the stability of construction facilities on the surface, making long-term deformation monitoring imperative. Long-term monitoring of surface deformation along the subway network and statistical analysis of the overall deformation situation are lacking in China. Therefore, targeting 35 Chinese cities whose subway mileage exceeds 50 km, we extracted their surface deformation along subway networks between 2018 and 2022, using spaceborne synthetic aperture radar (SAR) interferometry (InSAR) technology and Sentinel-1 satellite data. We verified the results with the continuous global navigation satellite system (GNSS) stations’ data and found that the root mean square error (RMSE) of the InSAR results was 3.75 mm/year. Statistical analysis showed that ground subsidence along the subways was more prominent in Beijing, Tianjin, and other areas in the North China Plain, namely Kunming (which is dominated by karst landforms), as well as Shanghai, Guangzhou, Qingdao, and other coastal cities. In addition, an analysis revealed that the severity of surface subsidence correlated positively with a city’s gross domestic product (GDP) with a Pearson correlation of 0.787, since the higher the GDP, the more frequent the construction and maintenance of subway, and the more commuters there are, which in turn exacerbates the disturbance to the surface. Additionally, the type of land cover also affects the ground deformation. Our findings provide a reference for constructing, operating, and maintaining the urban subway systems in China.

China, with its complex geology and diverse climate, is highly prone to landslides, endangering public safety and infrastructure. To address disaster prevention needs, this study comprehensively assesses national landslide susceptibility. We divided China into 37 geomorphic districts, diverging from traditional methods. By using a 2018–2022 surface deformation dataset, we introduced a rarely—considered dynamic aspect for more accurate mapping of landslide—prone areas. Nine key environmental factors were carefully considered, including terrain, geology, meteorology, hydrology, seismic activities, and engineering activities. Based on these innovative methods and data, we created a 40 m—resolution landslide susceptibility map (LSM) for the whole country. Our assessment showed high accuracy, with an AUC of 0.927, precision of 0.859, recall of 0.815, F1—score of 0.828 and Matthews correlation coefficient of 0.773. Seven high—risk regions, like the Tianshan Mountains and the southern Tibetan valleys, were analyzed. The study revealed regional differences in landslide occurrences and key influencing factors. The LSM and findings enrich landslide susceptibility theory and offer a valuable resource for engineering, disaster management, and mitigation in China, helping reduce potential landslide losses.

Keywords: Digital Aerial Imagery, Imagery Deformation, IMU Data Accuracy, Automatic Deformation Inspection, Statistical Characteristics, Cubic Spline Curve The geometric distortion of the push-broom digital aerial imagery can be rectified according to the data of the inertial measurement unit (IMU). The low precision of IMU data will cause the undulant wavelike twist deformations of the push-broom digital aerial images after geometric rectification, directly influencing the authenticity and liability of images and their practical applications. At present, the image deformation diagnosis mainly depends on the subjective judgement of human being, which costs much time and manpower. In the paper, an automatic deformation inspection method based on statistical characteristics for digital aerial imagery is proposed to inspect the distortion caused by the low IMU data accuracy. For the undulant wavelike deformation image has the characteristic of pixel displacement in the regularly same direction, there will be a lot of wave curves in the same direction appeared in the image after geometric correction. Therefore, in the method, the positions of the wave curves in the image will be located by the extreme points of curvature of the contour lines, and then the wavelike deformations can be judged automatically through the distribution statistics of the open directions of the wave curves. The specific implement method can be described as follows: firstly, the edges of the image are detected with Canny edge detector and the vector contour lines are obtained by tracing the edges to get contour lines and fitting them with the cubic spline curve method. Then, the extreme points of curvature of the contour lines are calculated, and some of these points are determined to be the vertexes of the wave curves by judging the positional relations between each extreme point and the points around it, thus constituting a vertex set. Afterwards, the perpendicular directions of the tangent of the vertexes are used as the directions of the wave curves, and then the direction histograms of all the wave curves in the image are obtained by statistical analysis. Finally, the existence of the deformation phenomenon in the image due to the low precision of IMU data is able to be judged based on whether the directions of the wave curves are centralized in a certain direction or not. Experimental results showed that the automatic deformation inspection method presented in this paper can detect the deformation of the digital aerial images effectively caused by low accuracy of IMU data with 95% accuracy.