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GPS trajectories generated by moving objects provide researchers with an excellent resource for revealing patterns of human activities. Relevant research based on GPS trajectories includes the fields of location-based services, transportation science, and urban studies among others. Research relating to how to obtain GPS data (e.g., GPS data acquisition, GPS data processing) is receiving significant attention because of the availability of GPS data collecting platforms. One such problem is the GPS data classification based on transportation mode. The challenge of classifying trajectories by transportation mode has approached detecting different modes of movement through the application of several strategies. From a GPS data acquisition point of view, this paper macroscopically classifies the transportation mode of GPS data into single-mode and mixed-mode. That means GPS trajectories collected based on one type of transportation mode are regarded as single-mode data; otherwise it is considered as mixed-mode data. The one big difference of classification strategy between single-mode and mixed-mode GPS data is whether we need to recognize the transition points or activity episodes first. Based on this, we systematically review existing classification methods for single-mode and mixed-mode GPS data and introduce the contributions of these methods as well as discuss their unresolved issues to provide directions for future studies in this field. Based on this review and the transportation application at hand, researchers can select the most appropriate method and endeavor to improve them.

Evidence shows enhanced walking environment promotes overall physical activities and further alleviates the risk of chronic diseases and mental disorders. Current walkability research is limited by traditional GIS methods that fail to capture micro-level details and human perceptions. Additionally, existing image segmentation techniques return low accuracy when extracting complex street environment features. Therefore, we developed a hierarchical evaluation framework for urban walkability with high precision image segmentation techniques, and subjective measurements on four first-level indicators (greenness, openness, crowding, safety) and their corresponding second-level indicators. An entropy weight method was constructed to quantify the indicators based on questionnaires from 120 volunteers. Furthermore, we developed Detail-Strengthened High-Resolution Network (DS-HRNet), a deep learning model that demonstrates a 15% improvement in street scene segmentation performance compared to existing models. Using the newly developed deep learning model, we analyzed 113,900 street view images in central Wuhan City, China. Our walkability results revealed spatial heterogeneity across the city, characterized by substantial disparities between adjacent areas, particularly in commercial areas. Subsequent socioeconomic analysis demonstrated that better walkability exists in areas of higher socioeconomic status but lower proportion of non-local residents. This walkability inequality may further lead to health disparities through its influence on physical activity and social interaction.

With the emergence of intelligent transportation and smart city system, the issue of how to perform an efficient and reasonable clustering analysis of the mass vehicle trajectories on multi-camera monitoring videos through computer vision has become a significant area of research. The traditional trajectory clustering algorithm does not consider camera position and field of view and neglects the hierarchical relation of the video object motion between the camera and the scenario, leading to poor multi-camera video object trajectory clustering. To address this challenge, this paper proposed a hierarchical clustering algorithm for multi-camera vehicle trajectories based on spatio-temporal grouping. First, we supervised clustered vehicle trajectories in the camera group according to the optimal point correspondence rule for unequal-length trajectories. Then, we extracted the starting and ending points of the video object under each group, hierarchized the trajectory according to the number of cross-camera groups, and supervised clustered the subsegment sets of different hierarchies. This method takes into account the spatial relationship between the camera and video scenario, which is not considered by traditional algorithms. The effectiveness of this approach has been proved through experiments comparing silhouette coefficient and CPU time.

Crack detection is essential for the safety maintenance of road infrastructure. However, there are two major limitations to detecting road cracks accurately: (1) tiny cracks usually possess less distinctive features and are more susceptible to noises, so they are apt to be ignored; (2) most existing methods extract cracks with coarse and thicker boundaries, which needs further improvement. To address the above limitations, we propose CTCD-Net: a Cross-layer Transmission network for tiny road Crack Detection. Firstly, we propose a cross-layer information transmission module based on an attention mechanism to compensate for the disadvantage of unobvious features of tiny cracks. With this module, the feature information from upper layers is transmitted to the next one, layer by layer, to achieve information enhancement and emphasize the feature representation of tiny crack regions. Secondly, we design a boundary refinement block to further improve the accuracy of crack boundary locations, which refines boundaries by learning the residuals between the label images and the interim coarse maps. Extensive experiments conducted on three crack datasets demonstrate the superiority and effectiveness of the proposed CTCD-Net. In particular, our method largely improves the accuracy and completeness of tiny crack detection.

Given the popularization of GPS technologies, the massive amount of spatiotemporal GPS traces collected by vehicles are becoming a new kind of big data source for urban geographic information extraction. The growing volume of the dataset, however, creates processing and management difficulties, while the low quality generates uncertainties when investigating human activities. Based on the conception of the error distribution law and position accuracy of the GPS data, we propose in this paper a data cleaning method for this kind of spatial big data using movement consistency. First, a trajectory is partitioned into a set of sub-trajectories using the movement characteristic points. In this process, GPS points indicate that the motion status of the vehicle has transformed from one state into another, and are regarded as the movement characteristic points. Then, GPS data are cleaned based on the similarities of GPS points and the movement consistency model of the sub-trajectory. The movement consistency model is built using the random sample consensus algorithm based on the high spatial consistency of high-quality GPS data. The proposed method is evaluated based on extensive experiments, using GPS trajectories generated by a sample of vehicles over a 7-day period in Wuhan city, China. The results show the effectiveness and efficiency of the proposed method.

The design of urban clusters has played an important role in urban planning, but realizing the construction of these urban plans is quite a long process. Hence, how the progress is evaluated is significant for urban managers in the process of urban construction. Traditional methods for detecting urban clusters are inaccurate since the raw data is generally collected from small sample questionnaires of resident trips rather than large-scale studies. Spatiotemporal big data provides a new lens for understanding urban clusters in a natural and fine-grained way. In this article, we propose a novel method for Detecting and Evaluating Urban Clusters (DEUC) with taxi trajectories and Sina Weibo check-in data. Firstly, DEUC applies an agglomerative hierarchical clustering method to detect urban clusters based on the similarities in the daily travel space of urban residents. Secondly, DEUC infers resident demands for land-use functions using a naïve Bayes’ theorem, and three indicators are adopted to assess the rationality of land-use functions in the detected clusters—namely, cross-regional travel index, commuting direction index, and fulfilled demand index. Thirdly, DEUC evaluates the progress of urban cluster construction by calculating a proposed conformance indicator. In the case study, we applied our method to detect and analyze urban clusters in Wuhan, China in the years 2009, 2014, and 2015. The results suggest the effectiveness of the proposed method, which can provide a scientific basis for urban construction.

Non-motorized travelers, such as pedestrians and cyclists, are highly sensitive to infrastructures like stairs, tunnels, and overpasses that impede movement. Dynamically avoiding such barriers to find optimal paths remains a key challenge in route planning, as existing models often inadequately capture the interplay of road types, traveler behavior, and environmental dynamics. To address this, we propose UED-Q, a novel path planning method based on Q-learning, enhanced by Upper Confidence Bound (UCB)-guided exploitation and a dynamic learning rate tuning mechanism. UED-Q introduces a reward-shaping function that integrates road characteristics, user behavior, and real-time environmental changes. Evaluated on Wuhan’s real-world road network, UED-Q outperforms several advanced methods, including Q-learning with the Artificial Potential Field (QAPF), Combining Manhattan Distance and Q-Learning (CMDQL), Deep Q-Network (DQN), Dueling DQN, and A* in both static and dynamic scenarios. It reduces the average path length by 7.12% (vs. DQN), 4.26% (vs. A*), and 9.47% (vs. Dueling DQN), and cuts the estimated travel time by up to 8.36%. Notably, the convergence speed improves by 92.58% over DQN and 96.71% over Dueling DQN. This work delivers an efficient, empirically validated framework for intelligent non-motorized navigation, supporting sustainable urban mobility.

In this paper, we propose a novel approach for mining lane-level road network information from low-precision vehicle GPS trajectories (MLIT), which includes the number and turn rules of traffic lanes based on naïve Bayesian classification. First, the proposed method (MLIT) uses an adaptive density optimization method to remove outliers from the raw GPS trajectories based on their space-time distribution and density clustering. Second, MLIT acquires the number of lanes in two steps. The first step establishes a naïve Bayesian classifier according to the trace features of the road plane and road profiles and the real number of lanes, as found in the training samples. The second step confirms the number of lanes using test samples in reference to the naïve Bayesian classifier using the known trace features of test sample. Third, MLIT infers the turn rules of each lane through tracking GPS trajectories. Experiments were conducted using the GPS trajectories of taxis in Wuhan, China. Compared with human-interpreted results, the automatically generated lane-level road network information was demonstrated to be of higher quality in terms of displaying detailed road networks with the number of lanes and turn rules of each lane.

Cloud and snow detection is one of the most significant tasks for remote sensing image processing. However, it is a challenging task to distinguish between clouds and snow in high-resolution multispectral images due to their similar spectral distributions. The shortwave infrared band (SWIR, e.g., Sentinel-2A 1.55–1.75 µm band) is widely applied to the detection of snow and clouds. However, high-resolution multispectral images have a lack of SWIR, and such traditional methods are no longer practical. To solve this problem, a novel convolutional neural network (CNN) to classify cloud and snow on an object level is proposed in this paper. Specifically, a novel CNN structure capable of learning cloud and snow multiscale semantic features from high-resolution multispectral imagery is presented. In order to solve the shortcoming of “salt-and-pepper” in pixel level predictions, we extend a simple linear iterative clustering algorithm for segmenting high-resolution multispectral images and generating superpixels. Results demonstrated that the new proposed method can with better precision separate the cloud and snow in the high-resolution image, and results are more accurate and robust compared to the other methods.

The Impervious Surface (IS) is closely related to human activities and ecological environments, whose monitoring is vital for establishing safe, resilient, and sustainable human settlement environments. Synthetic Aperture Radar (SAR), with its timely, cloud-penetrating, and geometrically sensitive observational ability, provides a promising solution for large-scale, high-frequency IS extraction. However, accurately identifying the IS with clear boundaries in SAR amplitude images is a huge challenge due to speckle noise and the scattering heterogeneity of the IS. In response to the above challenge, we innovatively propose a Boundary Awareness Network (SARBA-Net) for IS extraction from SAR images. SARBA-Net employs a multi-scale Boundary Preserving Structure (BPS), which consists of multiple Boundary Awareness and Enhancement Modules (BAEMs) to progressively restore the boundary topology. Within BAEMs, a novel Boundary Contrast Loss (BCL) is designed to generate boundary class awareness, and a hard boundary anchor mining strategy further facilitates BCL to discriminate boundaries. Finally, a Boundary Consistency Auxiliary Loss (BCAL) is introduced in SARBA-Net to refine the boundary detail by the boundary duality between the ground truth and prediction. Extensive experiments on SAR images across diverse IS scenes, including dense urban, rural, road-dominated, and complex mountainous regions, show that SARBA-Net achieves remarkable IS segmentation performance in detail perception, shape preservation, and boundary refinement, with a 2.81% improvement in IoU and a reduction in boundary consistency error to 5.44%. These results demonstrate the effectiveness of SARBA-Net and highlight its potential for robust IS monitoring, particularly under cloudy and rainy conditions where optical imagery is limited.