The Visible Infrared Imaging Radiometer Suite Day/Night Band (VIIRS/DNB) nighttime light data is a powerful remote sensing data source. However, due to stray light pollution, there is a lack of VIIRS data in mid-high latitudes during the summer, resulting in the absence of high-precision spatiotemporal continuous datasets. In this paper, we first select nine-time series interpolation methods to interpolate the missing images. Second, we construct image pixel-level temporal continuity constraints and spatial correlation constraints and remove the pixels that do not meet the constraints, and the eliminated pixels are filled with the focal statistics tool. Finally, the accuracy of the time series interpolation method and the spatiotemporally constrained interpolation method (STCIM) proposed in this paper are evaluated from three aspects: the number of abnormal pixels (NP), the total light brightness value (TDN), and the absolute value of the difference (ADN). The results show that the images simulated by the STCIM are more accurate than the nine selected time series interpolation methods, and the image interpolation accuracy is significantly improved. Relevant research results have improved the quality of the VIIRS dataset, promoted the application research based on the VIIRS DNB long-time series night light remote sensing image, and enriched the night light remote sensing theory and method system.

With increasing research on geological hazards and the development of geographic information technology, slope units play an increasingly important role in landslide susceptibility assessment and prevention work. The scientific and reasonable division of slope units directly impacts the accuracy and practicality of analysis results. Despite the significant progress in slope unit division techniques, most existing methods still have certain limitations, such as a strong dependence on manually set thresholds during the division process, resulting in low levels of automation and efficiency. To address this issue, a new parameter-free slope unit extraction algorithm that integrates terrain factors, called Terrain Factor Parameter-Free Slope Unit Division (TFPF-SU), is introduced. This eliminates the issue of manually setting parameter thresholds during the slope unit division process. This algorithm fully utilizes the terrain information provided by digital elevation models (DEMs) to accurately calculate the curvature, slope, and aspect data for each point. On the basis of the inherent consistency principles among slope, aspect, and curvature, object-oriented image segmentation technology is used to achieve slope unit division. We select Dongchuan District in Yunnan Province, China, as a test area to verify the TFPF-SU algorithm and conduct a detailed comparative analysis and validation of the results with those obtained via traditional hydrological analysis methods from both qualitative and quantitative perspectives. In the quantitative analysis, we utilize the size and shape of the slope units. The results indicate the following: ① the slope units obtained with the TFPF-SU method are more uniform in size, avoiding issues with oversized or irregularly shaped units; ② the slope unit shapes obtained with the TFPF-SU method are more reasonable, with about 70% of the units falling within a reasonable shape index range, compared to only about 32% with the hydrological method; and ③ the slope units produced by the TFPF-SU method align more closely with terrain authenticity, exhibiting a higher degree of topographical conformity.

Landslides are a common type of geological hazard, characterized by sudden onset, high destructiveness, and frequent occurrence, and are widely distributed in mountainous areas with complex terrain. In recent years, due to extreme weather and intensified human activities, both the frequency and intensity of landslide disasters in China have increased significantly, posing serious threats to human life, property, and socio-economic development. Although various methods for landslide susceptibility assessment have been proposed, the accuracy of existing models still needs improvement. In this context, this study takes the landslide-prone Dongchuan District of Kunming City, Yunnan Province, as a case study and proposes a coupled model that integrates an autoencoder and a feedforward neural network (AuFNN). The model uses the autoencoder to extract low-dimensional and highly discriminative feature representations, which are then used as input to the feedforward neural network to perform landslide susceptibility assessment. To evaluate the effectiveness of the proposed model, it is compared with four commonly used models, Support Vector Machine (SVM), Random Forest (RF), XGBoost, and Feedforward Neural Network (FNN), based on performance metrics such as the ROC curve, recall, and F1 score. The results indicate that the AuFNN model provides an alternative representation learning framework and achieves performance comparable to that of established machine learning models in landslide susceptibility assessment, as reflected by similar AUC, accuracy, and F1 score values.