Landslides have a great impact on the normal traffic of highway, and maintaining the normal traffic of highway is the foundation of economic development, so landslide susceptibility mapping is very important. In this study, four counties, which locate in the central Ganzi Tibetan Autonomous Prefecture, Sichuan Province, China, are taken as the research region. Based on the 190 historical landslide disaster points in the region, six factors-elevation, slope, aspect, plan curvature, profile curvature and TWI (Topographic Wetness Index) - are finally selected for calculation. A landslide disaster is evaluated by two single models of CF (Certainty Factors) and IV (Information Value) models and four coupling models of CF-AHP (Analytic Hierarchy Process), CF-LR (Logistic Regression), IV-AHP and IV-LR models. The accuracy of the six models is evaluated by the ROC (Receiver Operating Characteristic) curve and the Sridevi Jadi parameters. The IV-AHP model has the highest value of 0.9189, which indicates that the IV-AHP model is more appropriate for landslide disaster assessment in the whole region. In the Sridevi Jadi parameters, the IV model have the highest value of 0.8696, showing that the IV model have the highest accuracy in landslide susceptibility assessment in high- and very high-susceptibility regions.

Point cloud data, known for their accuracy and ease of acquisition, are commonly used for reconstructing level of detail 2 (LoD-2) building models. However, factors like object occlusion can cause incompleteness, negatively impacting the reconstruction process. To address this challenge, this paper proposes a method for reconstructing LoD-2 building models from incomplete point clouds. We design a generative adversarial network model that incorporates geometric constraints. The generator utilizes a multilayer perceptron with a curvature attention mechanism to extract multi-resolution features from the input data and then generates the missing portions of the point cloud through fully connected layers. The discriminator iteratively refines the generator’s predictions using a loss function that is combined with plane-aware Chamfer distance. For model reconstruction, the proposed method extracts a set of candidate polygons from the point cloud and computes weights for each candidate polygon based on a weighted energy term tailored to building characteristics. The most suitable planes are retained to construct the LoD-2 building model. The performance of this method is validated through extensive comparisons with existing state-of-the-art methods, showing a 10.9% reduction in the fitting error of the reconstructed models, and real-world data are tested to evaluate the effectiveness of the method.

With the development of the urban economy and population growth, the demand for urban construction land continues to rise. Addressing the challenges of land protection and ecological security calls for the exploration of new approaches. This study emphasizes a sustainable solution by emphasizing in-depth exploration of existing land resources, moving away from the traditional “expanding the pie” model of urban development. Specifically, we selected land parcels in Shenzhen city from 2010 to 2020 that met the criteria for urban renewal and transformation as evaluation units, further categorized into residential, industrial, and commercial land for transformation. From multiple perspectives, including geological conditions, building conditions, agglomeration, social factors, and the degree of completeness of public facilities, we constructed an evaluation system comprising 23 indicators to quantify the potential for urban renewal of these units. Through the AET (AHP-EWM-TOPSIS) model analysis for assessing the potential of urban renewal and transformation, we classified the transformation parcels into different potential levels based on the optimized solutions from the model. Finally, we validated the results using the planned land units implemented in Shenzhen from 2010 to 2020, achieving an accuracy of 81% in matching the spatial distribution of potential levels with the actual situation. The comprehensive evaluation results from the model provide a basis for optimizing and enhancing sustainable urban renewal. This research contributes to the formulation of informed decisions and strategic urban development planning, enabling a more cautious and resource-efficient approach to address the challenges of urban expansion.

Reservoirs face growing challenges in safety and sustainable management, requiring systematic approaches that integrate monitoring, analysis, and decision support. To address this need, this study develops an integrated information system framework with a four-layer architecture, encompassing “perception,” “data,” “model,” and “application.” The perception layer establishes a multi-platform monitoring network based on fused multi-sensor data. The data layer manages heterogeneous information through correlation mechanisms at the physics, semantics, and application levels. The model layer supports decision-making through a cross-coupled analytical framework for the coordinated management of water safety, resources, environment, and ecology. Finally, the application layer utilizes virtual-physical mapping and dynamic reasoning to implement a closed-loop management system encompassing forecasting, warning, simulation, and planning. This framework was implemented and validated at the Ye Fan Reservoir in Hubei Province, China. By integrating components like “One Map,” flood dispatching, safety monitoring, early warning, video surveillance, and operational supervision, a three-dimensional perception network was constructed. This deployment significantly improved the precision, reliability, and scientific basis of reservoir operation. The integrated monitoring and management system presented in this paper, driven by heterogeneous sensor networks, provides an effective and generalizable solution for modern reservoir management, with the potential for extension to broader water resource and infrastructure systems.

Housing price is a major issue affecting people's lives, but also closely related to the interests of the people themselves. Housing prices are affected by various factors, such as economic factors, population size factors, social factors, national policy factors, the internal factors of real estate and environmental factors. With the deepening of urbanization and the agglomeration of urban population in China, housing prices have been further accelerated. The Chinese government has also introduced a series of policies to limit real estate transactions and affect property prices. This paper also aims to explore a time series analysis method to analyse the impact of real estate policies on real estate prices. Firstly, the article searches for policy factors related to real estate through government official channels such as state, Prefecture and city, and analyses key words related to policy by means of natural language processing. Then, the real estate registration volume, transaction volume and transaction house price data which are arranged into time series are modelled using ARIMA time series model, and the data are processed according to scatter plot, autocorrelation function and partial autocorrelation function graph of the model to identify its stationarity. Finally, the LPPL (logarithmic periodic power) model and MPGA (multi-population genetic algorithm) are used to fit and detect turning points of real estate registration data, and the time series detection algorithm is used to obtain the inflection time nodes of the sequence, and then the relationship between real estate policy and real estate transactions is analysed. Taking the real estate registration data in Wuhan as an example, this paper validates the above time series analysis method. The results show that some real estate policies (such as purchase restriction policy, public rental policy, etc.) have a certain impact on real estate transactions in a short time. Part of the real estate policy (such as graduate security, settlement policy, etc.) does not have a significant impact on real estate transactions. To sum up, the government's brutal blockade of macro-control of the housing market cannot fundamentally solve the housing difficulties of the people, but also standardize the real estate market trading mechanism, innovate the market trading mode, so as to promote the long-term development of the housing market.

In the process of addressing, when people use words to express indistinctly, they often draw simple sketches to assist expression, which helps people to form a simple spatial scene in the brain and correspond to the actual scene one by one, and finally locate and find the target address. How to establish an one-to-one mapping relationship between the spatial objects in the hand-drawn sketch and in the vector map is the key to the realization of map addressing and location, and this process is also the process of map matching. This paper aims to address difficult problems associated with the features of hand-drawn sketches and vector map matching in order to improve the use of all potential matching points designed for application in hand-drawn sketches and spatial relation matrix structures of vector maps. To accomplish this, we use the N-queen problem solving process and improve the tabu search algorithm. In the matching process under the constraint of a single spatial relationship, and the hierarchical matching process under the constraint of multiple spatial relations, this study verifies the quality of the spatial relationship and the feasibility and effectiveness of the matching method of hand-drawn sketches and vector maps using the improved tabu search algorithm.