Population spatialization reveals the distribution and quantity of the population in geographic space with gridded population maps. Fine-scale population spatialization is essential for urbanization and disaster prevention. Previous approaches have used remotely sensed imagery to disaggregate census data, but this approach has limitations. For example, large-scale population censuses cannot be conducted in underdeveloped countries or regions, and remote sensing data lack semantic information indicating the different human activities occurring in a precise geographic location. Geospatial big data and machine learning provide new fine-scale population distribution mapping methods. In this paper, 30 features are extracted using easily accessible multisource geographic data. Then, a building-scale population estimation model is trained by a random forest (RF) regression algorithm. The results show that 91% of the buildings in Lin’an District have absolute error values of less than six compared with the actual population data. In a comparison with a multiple linear (ML) regression model, the mean absolute errors of the RF and ML models are 2.52 and 3.21, respectively, the root mean squared errors are 8.2 and 9.8, and the R² values are 0.44 and 0.18. The RF model performs better at building-scale population estimation using easily accessible multisource geographic data. Future work will improve the model accuracy in densely populated areas.

The hidden Markov model-based map matching algorithm (HMM-MM) is an effective method for online vehicle navigation and offline trajectory position correction. Common HMM-MMs are susceptible to the influence of adjacent road segment endpoints and similar parallel roads, because the multi-index probability model may ignore some indexes when the probability of other indexes is high. This makes the map-matching result not meet the assumption that vehicles always travel the shortest or optimal path, and it cannot guarantee that the trajectory points can match to the nearest position of the maximum likelihood road segment, resulting in poor accuracy. In this paper, an IHMM-MM is proposed. IHMM-MM (1) modifies the definition of transition probability and no longer takes the straight-line distance between trajectory points as the reference for the shortest path length between candidate point pairs. (2) supplements the definition of observation probability and introduces the point-line relation function to screen and group candidate points. (3) adds additional logic outside the HMM probability model to consider the trajectory connectivity and fill in the key trajectory points where the vehicles travel. Experiments show that the IHMM-MM can effectively improve the sampling frequency of trajectory data and has better performance in complex urban road environments.

Conventional map-matching (MM) algorithms take blind eyes to the complexity in realistic traffic conditions and hence present significant limitations in distinguishing the detailed driving paths of vehicles within complex urban road networks. The popularity of vehicle-borne cameras and advances in image recognition technologies provide an opportunity to remedy the gap through integrating vehicle-borne image semantic information with MM algorithms. Following this logic, this article proposes a novel MM algorithm with semantic fusion from vehicle-borne images (VIS-MM) suited to the parallel road scenes. First, a multipath output algorithm is developed using the hidden Markov model to obtain candidate paths. Second, image recognition techniques are employed to extract vehicle-borne image semantics. Finally, the entropy weight method is performed to determine the most promising driving path among the candidate paths. The experimental results show that semantic fusion from vehicle-borne images contributes to a significant improvement of accuracy from 66.18% to 99.88% against the parallel road scenes. The proposed map-matching algorithm can be applied into the fields of unmanned autonomous navigation and crowdsourcing updating of high-definition maps.

Urban vitality, a multifaceted construct, is influenced by economic conditions and urban structural characteristics, and can significantly be impacted by public health emergencies. While extensive research has been conducted on urban vitality, prevailing studies often rely on singular data sources, limiting the scope for holistic assessment. Moreover, there is a conspicuous absence of longitudinal analyses on urban vitality’s evolution and a dearth of quantitative causal evaluations of the effects of public health emergencies. Addressing these gaps, this study devises a comprehensive framework for evaluating urban vitality, assessing Wuhan’s vitality from 2018 to 2020 across economic, social, spatial, and ecological dimensions. Utilizing a Difference-In-Difference (DID) model, the impact of public health emergencies is quantified. The findings indicate pronounced spatial variations in Wuhan’s urban vitality, with a gradational decline from the city center; public health emergencies exhibit differential impacts across vitality dimensions, detrimentally affecting economic, social, and spatial aspects, while bolstering ecological vitality. Moreover, high population and high public budget revenue are identified as factors enhancing urban vitality and bolstering the city’s resilience against sudden adversities. This study offers valuable insights for geographers and urban planners, contributing to the refinement of urban development strategies.

Urban villages (UVs) are the most typical urban informal settlements in China, and the study of an effective identification method for UVs can help to provide a reference for the development of locally adapted UV transformation policies. In order to reduce the cost of labeling and enhance transferability, this study integrates remote sensing and social sensing data and applies sample migration from a labeled area to a less labeled area based on the theory of transfer learning. There are two main results of this study: (1) This study constructed a feature system for UV identification based on multi-feature extraction using a block as a unit, and experiments based on Tianhe District achieved an overall accuracy of 90% and a kappa coefficient of 0.76. (2) Using Tianhe District as the source domain and Jiangan District as the target domain, samples from the source domain were reused based on the KMM, TCA, and CORAL algorithms. The CORAL+RF algorithm showed the best performance, where its overall accuracy reached 97.06% and its kappa coefficient reached 0.89, and its overall accuracy reached 91.17% and its kappa coefficient reached 0.67 in the case of no target domain labeling. To sum up, the identification method for UVs proposed in the present study provides theoretical references for identification methods for UVs in different geographical areas.

Accurately calibrating camera–LiDAR systems is crucial for achieving effective data fusion, particularly in data collection vehicles. Data-driven calibration methods have gained prominence over target-based methods due to their superior adaptability to diverse environments. However, current data-driven calibration methods are susceptible to suboptimal initialization parameters, which can significantly impact the accuracy and efficiency of the calibration process. In response to these challenges, this paper proposes a novel general model for the camera–LiDAR calibration that abstracts away the technical details in existing methods, introduces an improved objective function that effectively mitigates the issue of suboptimal parameter initialization, and develops a multi-level parameter optimization algorithm that strikes a balance between accuracy and efficiency during iterative optimization. The experimental results demonstrate that the proposed method effectively mitigates the effects of suboptimal initial calibration parameters, achieving highly accurate and efficient calibration results. The suggested technique exhibits versatility and adaptability to accommodate various sensor configurations, making it a notable advancement in the field of camera–LiDAR calibration, with potential applications in diverse fields including autonomous driving, robotics, and computer vision.