In this paper, we use the small baseline set technology and the early geological hazard identification method based on the selection of Permanent Scatter (PS) and Distributed Scatter (DS) points to carry out the research on surface deformation monitoring caused by underground activities in mining cluster areas. We adopted the Small Baseline Subset InSAR (SBAS-InSAR) technique to process Sentinel-1A SAR images over the research area from March 2017 to May 2021. The deformation estimation technology based on the robustness of PS points and DS points can be used for early identification of high-density surface subsidence in a large area of mines. The surface subsidence information can be obtained quickly and accurately, and the advantages of using InSAR technology to monitor long-time surface subsidence in complex mining cluster areas was explored in this study. By comparing the monitoring data of the Global Navigation Satellite System (GNSS) ground monitoring equipment, the accuracy error of large-scale surface settlement information is controlled within 8 mm, which has high accuracy. Meanwhile, according to the spatial characteristics of cluster mining areas, it is analyzed that the relationship between adjacent mining areas through groundwater easily leads to regional associated large-area settlement changes. Compared with the D-InSAR (Differential InSAR) technology applied in mine monitoring at the early stage, this proposed method can monitor a large range of long time series and optimize the problem of decoherence to some extent in mining cluster areas. It has important reference significance for early monitoring and early warning of subsidence disaster evolution in mining intensive areas.

Collision accidents involving construction vehicles and workers frequently occur at construction sites. Computer vision (CV) technology presents an efficient solution for collision-risk pre-warning. However, CV-based methods are still relatively rare and need an enhancement of their performance. Therefore, a novel three-stage collision-risk pre-warning model for construction vehicles and workers is proposed in this paper. This model consists of an object-sensing module (OSM), a trajectory prediction module (TPM), and a collision-risk assessment module (CRAM). In the OSM, the YOLOv5 algorithm is applied to identify and locate construction vehicles and workers; meanwhile, the DeepSORT algorithm is applied to the real-time tracking of the construction vehicles and workers. As a result, the historical trajectories of vehicles and workers are sensed. The original coordinates of the data are transformed to common real-world coordinate systems for convenient subsequent data acquisition, comparison, and analysis. Subsequently, the data are provided to a second stage (TPM). In the TPM, the optimized transformer algorithm is used for a real-time trajectory prediction of the construction vehicles and workers. In this paper, we enhance the reliability of the general object detection and trajectory prediction methods in the construction environments. With the assistance afforded by the optimization of the model’s hyperparameters, the prediction horizon is extended, and this gives the workers more time to take preventive measures. Finally, the prediction module indicates the possible trajectories of the vehicles and workers in the future and provides these trajectories to the CRAM. In the CRAM, the worker’s collision-risk level is assessed by a multi-factor-based collision-risk assessment rule, which is innovatively proposed in the present work. The multi-factor-based assessment rule is quantitatively involved in three critical risk factors, i.e., velocity, hazardous zones, and proximity. Experiments are performed within two different construction site scenarios to evaluate the effectiveness of the collision-risk pre-warning model. The research results show that the proposed collision pre-warning model can accurately predict the collision-risk level of workers at construction sites, with good tracking and predicting effect and an efficient collision-risk pre-warning strategy. Compared to the classical models, such as social-GAN and social-LSTM, the transformer-based trajectory prediction model demonstrates a superior accuracy, with an average displacement error of 0.53 m on the construction sites. Additionally, the optimized transformer model is capable of predicting six additional time steps, which equates to approximately 1.8 s. The collision pre-warning model proposed in this paper can help improve the safety of construction vehicles and workers.

Thaw slumping is a periglacial process that occurs on slopes in cold environments, where the ground becomes unstable and the surface slides downhill due to saturation with water during thawing. In this study, GaoFen-1 remote sensing and fused multi-source feature data were used to automatically map thaw slumping landforms in the Beilu River Basin of the Qinghai–Tibet Plateau. The bi-directional cascade network structure was used to extract edges at different scales, where an individual layer was supervised by labeled edges at its specific scale, rather than directly applying the same supervision to all convolutional neural network outputs. Additionally, we conducted a 5-year multi-scale feature analysis of small baseline subset interferometric synthetic aperture radar deformation, normalized difference vegetation index, and slope, among other features. Our study analyzed the performance and accuracy of three methods based on edge object supervised learning and three preconfigured neural networks, ResNet101, VGG16, and ResNet152. Through verification using site surveys and multi-data fusion results, we obtained the best ResNet101 model score of intersection over union of 0.85 (overall accuracy of 84.59%).The value of intersection over union of the VGG and ResNet152 are 0.569 and 0.773, respectively. This work provides a new insight for the potential feasibility of applying the designed edge detection method to map diverse thaw slumping landforms in larger areas with high-resolution images.

Landslide susceptibility maps (LSMs) are valuable tools typically used by local authorities for land use management and planning activities, supporting decision-makers in urban and infrastructure planning. To address this, we proposed a refined method for landslide susceptibility assessment, which comprehensively considered both static and dynamic factors. Neural network methods were used for susceptibility analysis. Land use and land cover (LULC) change and InSAR deformation were then integrated into the traditional susceptibility zoning to obtain a refined susceptibility map with higher accuracy. Validation was conducted on the improved landslide susceptibility map using site landslide data. The results showed that the LULC were proven to be the core driving factors for landslide occurrence in the study area. The GRU model achieved the highest model performance (AUC = 0.886). The introduction of InSAR surface deformation and land use and land cover change data could rationalize the inappropriateness of traditional landslide susceptibility zoning, correcting the false positive and false negative areas in the traditional landslide susceptibility map caused by human activities. Ultimately, 12.25% of the study area was in high-susceptibility zones, with 3.10% of false positive and 0.74% of false negative areas being corrected. The proposed method enabled refined analysis of landslide susceptibility over large areas, providing technical support and disaster prevention and mitigation references for geological hazard susceptibility assessment and land management planning.

As an important part of urban public spaces, the urban street is an important place for people’s lives, communication, and activities. Its vitality directly affects the vitality of the whole city. It is of great practical significance to examine the current situation and theoretical development of urban streets in China and reconstruct the vitality of streets from the perspective of the traffic users. Based on the “people-oriented” design concept of street traffic, this study took street sections and intersections as the main research object, proposed an overall layout of traffic, space, and facilities. A complete set of humanized and green traffic design methods was constructed to enhance the street vitality. First, through the analysis of the concept of road and street, the traffic connotations of the vital street was determined, the characteristics of the vital street was investigated, and the constituent elements of the vital street was summarized. Then, with a focus on people’s needs, the vital street traffic design methods were mainly divided into two parts: vital street section traffic design and vital street intersection design. Finally, Zhongshan Avenue in Wuhan City was chosen for empirical analysis. Through a field questionnaire survey, the traffic characteristics and traffic demands of residents on Zhongshan Avenue were analyzed, and the traffic design of Zhongshan Avenue was carried out in combination with the traffic design method proposed in this study. The results showed that people’s satisfaction with the renovated Zhongshan Avenue reached 90%. Buses, subways, bicycles, and walkways have become the four major green transportation modes for people to arrive at and leave Zhongshan Avenue, and the renovated Zhongshan Avenue has become a vibrant living street.

North China Plain is the largest agricultural production center in China and wheat-maize rotation is a widespread cultivation practice in this area. As gross primary production (GPP) is a proxy of land productivity, research on its spatial-temporal dynamics helps understand the variation of grain production in wheat-maize rotation. Here, Moderate Resolution Imaging Spectroradiometer (MODIS) data and ground observation data were combined to drive Vegetation Photosynthesis Model (VPM) in GPP estimation over wheat-maize rotation area during 2000–2015. Annual GPP has increased by 540.95 g C m⁻² year⁻¹ from 2000 to 2015, while total annual GPP has grown ∼150% than that of 2000. Moreover, annual GPP showed an increasing trend in the consecutively wheat-maize rotation area between 2000–2015. A strong linear relationship between GPP estimates and grain production demonstrated the potential of using VPM model to evaluate grain production in wheat-maize rotation area of Henan province, China.