The escalating complexity of urban traffic congestion makes single-scale analysis inadequate for supporting effective top-down management strategies. However, existing researches face challenges in representing continuous, interpretable macro-scale patterns without excessively compromising the fidelity of micro-scale movement details. To address this, we proposed a bottom-up vector field-based framework powered by a data-driven flow model to extract coherent macro-scale congestion patterns from micro-scale mobility. We first constructed a network-constrained demand vector field using a path flow model to capture micro-scale mobility. Building on this, we developed congestion exposure fields to characterize the direction and intensity of individual congestion exposure. To inform meaningful spatiotemporal aggregation and improve interpretability, we incorporated irrotationality theory as a scale-aware criterion, identifying scales where micro-scale mobility exhibited stable and coherent structures suitable for macro-scale representation. At these scales, a congestion potential field was derived to reveal system-wide risk dynamics. A case study in Wuhan demonstrates that the proposed framework effectively reduces micro-scale noise and highlights coherent macro-scale patterns, which offer actionable insights into local traffic complexity by serving as real-time warnings for areas prone to congestion or exhibiting increased vulnerability. This work provides an interpretable approach for selecting meaningful aggregation scales, supporting integrative assessments of micro-macro traffic dynamics.

Accurate metric depth completion across wide depth ranges is critical for autonomous systems. However, existing methods often struggle to efficiently capture depth features at both close and long ranges, primarily due to the inadequate modeling of fine-grained depth cues specific to different depth ranges. To address these limitations, we propose AirDC, an adaptive iterative depth refinement framework for full-range metric depth completion. The core contributions of our model lie in the design of two key modules. Specifically, we first construct an adaptive fine-grained stereo-LiDAR feature fusion module to fundamentally strengthen the model's capacity to preserve original full-range depth information. Built upon metric-aligned depth volumes (i.e., a 3D representation composed of cubic voxels uniformly partitioned in real-world metric space), this module employs an adaptive sub-voxel depth attention mechanism to enhance sensitivity to subtle depth variations across the full range, thereby both avoiding long-range accuracy degradation introduced by conventional disparity conversion and alleviating the coarse near-range granularity inherent in metric depth representations. Second, we introduce an iterative hypothesis-guided depth refinement module to improve prediction accuracy while maintaining memory efficiency. By integrating multi-scale multi-modal guidance information from depth hypotheses, this module enables explicit and progressive refinement of the initial depth estimation with a small parameter overhead. Experiments on multiple mainstream real-world and synthetic benchmarks demonstrate that AirDC achieves state-of-the-art performance, providing an effective solution for full-range metric depth completion. The code and data are available at https://github.com/yunqidu/AirDC.

Multi-city human mobility prediction is critical for GIScience and urban computing. However, pronounced spatiotemporal heterogeneity and training instability on large-scale, multi-city datasets restrict model generalization. To address these challenges, we propose the Human Mobility Predictor (HuMob Predictor), a novel framework that enhances multi-city mobility prediction. Our model introduces a multi-level encoding module that captures cross-city heterogeneity and within-city spatial relationships. City encodings capture macro-level characteristics unique to each city, while absolute spatial encodings preserve geographic proximity across grid cells. By combining these encodings, HuMob Predictor learns universal mobility representations while retaining city-specific features. To stabilize training across cities, we adopt an incremental training strategy that gradually increases prediction difficulty, significantly improving convergence and cross-city generalization. Experiments on the GISCUP 2025 multi-city datasets demonstrate that HuMob Predictor achieves superior performance in individual mobility prediction.

Point cloud semantic segmentation is crucial in various applications such as autonomous driving, robotics, and virtual reality, aiming to assign labels to each point in a cloud to reflect spatial relationships and boundaries. While previous methods primarily focus on geometric features, they often overlook the auxiliary role of color information, especially in scenes where geometric structures are less distinct. In this paper, we propose the Color Point Cloud Enhancement (CPCE) method to effectively leverage color information for improved 3D scene understanding. CPCE introduces a color information enhancement module with multi-scale consistency, enriching point features throughout the encoder stages. Additionally, we develop a novel contrastive learning module that uses relative color coordinates for point cloud serialization, allowing for the capture of positive and negative samples from distant points with similar color textures. Furthermore, we design a contrastive learning module tailored for scenes with weak geometric structures, enhancing feature representation through color-augmented contrast. Our method achieved a 78.1% mIoU on the ScanNet dataset, outperforming existing models trained on a single dataset. These results highlight the effectiveness of CPCE in scenarios where traditional methods struggle, particularly in enhancing segmentation accuracy by utilizing color as a critical feature.

Human mobility modeling and prediction are central research topics in GIScience. Although deep learning has led to significant advances in these fields, existing trajectory prediction models still face challenges in capturing the complexity of individual mobility behavior. Regression-based models often overestimate the diversity of human mobility, whereas classification models tend to underestimate it. This study attributes these biases to the models’ limitations in recognizing the spatial relationships among activity locations and mobility heterogeneity across individuals. To address these challenges, we propose the Spatial Preference Map-based Transformer (SPM-Former), explicitly integrating spatial proximity and mobility heterogeneity to enhance trajectory sequence prediction. To capture individual mobility characteristics, SPM-Former utilizes the Spatial Preference Map (SPM) to represent individuals’ spatial visitation preferences and adjacency relationships between locations. Then, we introduce two encoding modules to decode the information hidden within the SPM: one for encoding trajectory-level spatial-temporal information and another for embedding individual-level overall mobility features. Furthermore, we propose a novel optimization method, SPM-Loss, to assess prediction accuracy from the global spatial distribution perspective. Experimental results on a large-scale dataset from Japan demonstrate that SPM-Former outperforms state-of-the-art classification-based models, achieving approximately 3% and 20% improvements in trajectory sequence similarity and overall spatial feature similarity, respectively.

Mining traffic congestion dynamics presents difficulties in data structure and spatiotemporal analysis. Existing studies mainly provide insights from a supply perspective, with a restricted examination of why congestion occurs and how travel demands affect congestion. This study introduces an innovative framework to mine congestion dynamics from the perspective of human mobility. In human mobility modeling, we refine the conventional origin-destination representation of activity flow by introducing "path flow" (PF) which considers space-time paths and movement patterns. In congestion scenarios, congestion-related path flow (CPF) and congested path sub-flow (CPSF) are extended to track individuals’ congestion exposure and explore the correlations between congestion and human mobility. To finely classify congestion-related travel demands, a Bayesian inference approach, incorporating destination and spatiotemporal heterogeneities, is developed to deduce trip purposes. The experiments conducted in Wuhan demonstrate the availability and importance of PF in spatiotemporal dynamics analysis of human mobility. Interestingly, we find that 1) the job-housing relationship is imbalanced, with massive residents opting for cross-district living and working; 2) individuals tend to visit tertiary hospitals on weekends and secondary medical facilities with less congestion on weekdays. Notably, path flow can promote the fine-grained modeling of human mobility and provide theoretical support for many urban issues.

High-definition (HD) mapping is essential for autonomous driving and localization services, providing detailed lane-level road graphs for various applications. Current methodologies primarily segment the geometric structure of lane lines from remote sensing images and extract vectorized road graphs using heuristic methods. However, these approaches fail to adequately account for lane instance information and topological structures. Furthermore, the semi-automated process imposes constraints on the spatial scalability of HD maps. To overcome these limitations, we propose AutoLRG, a two-stage method for lane-level road graph construction. In lane geometry prediction, we propose a lane segmentation network based on directional supervision and multimodal fusion, incorporating an angle-direction loss and a cross-attention-based fusion module to enhance lane perception and connectivity. In lane instance modeling, we develop a Transformer-based lane decoder, which leverages an object detection architecture to extract vectorized lane instances and road vertices in an end-to-end manner. In lane topology construction, we introduce a "road segment–intersection" decoupled model, which establishes the connectivity relationships of intersection nodes based on traffic regulations to form a lane-level topological directed road graph. The ablation studies conducted on the two benchmark datasets (UrbanLaneGraph and OpenSatMap) have validated the effectiveness of the method. Comparative experiments with other methods demonstrate that our approach exhibits superior performance in lane segmentation, instance modeling, and topology construction. Code is available at https://github.com/EchoQiHeng/AutoLRG.

Existing traffic monitoring approaches cannot completely cover all road segments in real-time, leading to massive amounts of missing traffic data, which limits the implementation of intelligent transportation systems. Most existing methods lack deep mining of the unique spatiotemporal characteristics of traffic flows, resulting in difficulty in application to urban traffic with complex topologies and variable states. In this paper, we propose a novel Spatio-Temporal constrained Low-Rank Tensor Completion (ST-LRTC) method, which adopts a manifold embedding approach to depict the local geometric structure of spatiotemporal domains. Specifically, under the low-rank assumption, the method introduces temporal constraints based on the continuity and periodicity of traffic flow and a spatial constraint matrix reflecting the traffic flow transmission mechanism. We embed low-dimensional spatiotemporal constraint matrices into the low-rank tensor completion solving process to fully utilize the global features and local spatiotemporal characteristics of the traffic tensor. Experiments were performed using traffic data from Xi’an, China, and the results indicated that ST-LRTC outperformed state-of-the-art methods under various missing rates and patterns. Thorough experiments have demonstrated that the incorporation of spatiotemporal analysis can enhance the adaptability of the tensor completion model to complex urban scenarios, which guarantees better monitoring, diagnosis, and optimization of urban traffic states.

Xue Yanga, Kathleen Stewartb, Mengyuan Fangc & Luliang Tangc*a School of Geography and Information Engineering, China University of Geosciences, Wuhan, Chinab Department of Geographical Sciences, University of Maryland, College Park, MD, USAc State Key Laboratory for Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan, ChinaXue Yang received the Ph.D. degree from Wuhan University, Wuhan, China, in 2018. She is currently an associate Professor with China University of Geosciences, Wuhan. Her research interests include intelligent transportation system, spatiotemporal data analysis, and information mining. Homepage: http://grzy.cug.edu.cn/yangxue1/zh_CN/index.htmEmail: Kathleen Stewart is currently a Professor in the Department of Geographical Sciences and Director of the Center for Geospatial Information Science. She works in the area of geographic information science with a particular focus on geospatial dynamics. She is interested in mobility and spatial access, often in a big geospatial data context and using approaches that lie in the expanding field of spatial data science. Homepage: https://geog.umd.edu/facultyprofile/stewart/kathleenEmail: Mengyuan Fang received Bsc degree from Wuhan University, Wuhan, China, 2014. He is currently a Ph.D candidate at the State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University. His research addresses the issue of traffic congestion detection and prediction using big trace data.Email: Luliang Tang received the Ph.D. degree from Wuhan University, Wuhan, China, in 2007. He is currently a Professor with Wuhan University. His research interests include space–time GIS, GIS for transportation, and change detection. Homepage: http://www.lmars.whu.edu.cn/index.php/js/298.htmlEmail: CONTACT Luliang Tang [email protected] lack of associating pedestrian networks, i.e. the paths and roads used for non-vehicular travel, with information about semantic attribution is a major weakness for many applications, especially those supporting accurate pedestrian routing. Researchers have developed various algorithms to generate pedestrian walkways based on datasets, including high-resolution images, existing map databases, and GPS data; however, the semantic attribution of pedestrian walkways is often ignored. The objective of our study is to automatically extract semantic information including incline values and the different categories of pedestrian paths from multi-source spatial data, such as crowdsourced GPS tracking data, land use data, and motor vehicle road (MVR) networks. Incline values for each pedestrian path were derived from tracking data through elevation filtering using wavelet theory and a similarity-based map-matching method. To automatically categorize pedestrian paths into five classes including sidewalk, crosswalk, entrance walkway, indoor path, and greenway, we developed a hierarchical strategy of spatial analysis using land use data and MVR networks. The effectiveness of our proposed method is demonstrated using real datasets including GPS tracking data collected by volunteers, land use data acquired from OpenStreetMap, and MVR network data downloaded from Gaode Map.

Cloud detection is a crucial procedure in remote sensing preprocessing. However, cloud detection is challenging in cloud–snow coexisting areas because cloud and snow have a similar spectral characteristic in visible spectrum. To overcome this challenge, we presented an automatic cloud detection neural network (ACD net) integrated remote sensing imagery with geospatial data and aimed to improve the accuracy of cloud detection from high-resolution imagery under cloud–snow coexistence. The proposed ACD net consisted of two parts: 1) feature extraction networks and 2) cloud boundary refinement module. The feature extraction networks module was designed to extract the spectral–spatial and geographic semantic information of cloud from remote sensing imagery and geospatial data. The cloud boundary refinement module is used to further improve the accuracy of cloud detection. The results showed that the proposed ACD net can provide a reliably cloud detection result in cloud–snow coexistence scene. Compared with the state-of-the-art deep learning algorithms, the proposed ACD net yielded substantially higher overall accuracy of 97.92%. This letter provides a new approach to how remote sensing imagery and geospatial big data can be integrated to obtain high accuracy of cloud detection in the circumstance of cloud–snow coexistence.

Landsat images have played an important role in the field of Earth observation and geoinformatics. However, optical Landsat images are frequently contaminated by cloud cover, especially in tropical and subtropical regions, which limits the utilization of these images. To improve the utilization of Landsat images, in this study, we propose a novel spatiotemporal neural network with four modules: a cloud detection module, a spatial–temporal learning module, a spatial–temporal feature fusion module, and a reconstruction module. The results of the experiments demonstrate that the proposed method is quantitatively effective (root mean square error < 0.0179) and can achieve a better result for reconstructing Landsat images than some of the widely used existing deep learning methods and multitemporal methods. The proposed neural network method provides an effective tool for the removal of contiguous, thick clouds from satellite images, so as to improve the quality of subsequent remote sensing mapping and geoinformation extraction.

Short-term traffic prediction is of great importance to the management of traffic congestion, a pervasive and difficult-to-solve problem in many metropolises all over the world. However, existing studies on traffic prediction contain rough traffic information at the carriageway level that ignore the distinction between different turns in one intersection. With the aim of predicting traffic at road intersections from big trace data on a finer scale, this study proposes a novel method, the fine-grained traffic prediction method (FTPG) with a graph attention network (GAT), which predicts traffic information, including traffic flow speeds, traffic states, and average queue lengths, at the turn level. In the FTPG, a method for estimation of the queue starting point is proposed to improve the accuracy of traffic information detection. Furthermore, the topology is constructed under turn-level conditions, and a GAT-based method, the spatio-temporal residual graph attention network (ST-RGAN), is proposed to improve the prediction accuracy. Experiments are performed using taxi GPS trace data collected in the city of Wuhan and show that the proposed FTPG method can make predictions with fine-grained traffic information for road intersections accurately and robustly.

As an important component in transportation maps, three-dimensional (3D) structure information of grade-separated junctions is crucial for applications such as intelligent driving, route planning and traffic control. In order to acquire spatial layouts of road junctions, researchers have developed algorithms to extract planar structures from various data sources. However, it is less common to refine maps of grade-separated junctions with 3D structure information using tracking data. The objective of this study is to find an approach to extracting 3D structures of grade-separated junctions from vehicle trajectories. The proposed method is based on semantic segmentation and data fusion. Trajectories were divided into sections with different trends of elevation by detecting change points. The ranges and elevations of slopes and level sections were derived by seeking consensus among different trajectories using a data fusion technique. Based on semantic segmentation and aggregated elevations, we reconstructed detailed 3D junction structures. This method was validated on multiple crowdsourced trajectory datasets and compared to cluster center linking method. Experiments show that the proposed method had a higher overall accuracy of semantic segmentation than baseline method. The accuracy of vertical relationship at intersections is comparable to baseline. Despite large elevation discrepancy among trajectories, the performance of the proposed method was similar across crowdsourced trajectory datasets from open and commercial projects.

Pedestrian networks play an important role in various applications, such as pedestrian navigation services and mobility modeling. This paper presents a novel method to extract pedestrian networks from crowdsourced tracking data based on a two-layer framework. This framework includes a walking pattern classification layer and a pedestrian network generation layer. In the first layer, we propose a multi-scale fractal dimension (MFD) algorithm in order to recognize the two different types of walking patterns: walking with a clear destination (WCD) or walking without a clear destination (WOCD). In the second layer, we generate the pedestrian network by combining the pedestrian regions and pedestrian paths. The pedestrian regions are extracted based on a modified connected component analysis (CCA) algorithm from the WOCD traces. We generate the pedestrian paths using a kernel density estimation (KDE)-based point clustering algorithm from the WCD traces. The pedestrian network generation results using two actual crowdsourced datasets show that the proposed method has good performance in both geometrical correctness and topological correctness.

Detailed real-time road data are an important prerequisite for navigation and intelligent transportation systems. As accident-prone areas, road intersections play a critical role in route guidance and traffic management. Ubiquitous trajectory data have led to a recent surge in road map reconstruction. However, it is still challenging to automatically generate detailed structural models for road intersections, especially from low-frequency trajectory data. We propose a novel three-step approach to extract the structural and semantic information of road intersections from low-frequency trajectories. The spatial coverage of road intersections is first detected based on hotspot analysis and triangulation-based point clustering. Next, an improved hierarchical trajectory clustering algorithm is designed to adaptively extract the turning modes and traffic rules of road intersections. Finally, structural models are generated via K-segment fitting and common subsequence merging. Experimental results demonstrate that the proposed method can efficiently handle low-frequency, unstable trajectory data and accurately extract the structural and semantic features of road intersections. Therefore, the proposed method provides a promising solution for enriching and updating routable road data.

Lane-level road network updating is crucial for urban traffic applications that use geographic information systems contributing to, for example, intelligent driving, route planning and traffic control. Researchers have developed various algorithms to update road networks using sensor data, such as high-definition images or GPS data; however, approaches that involve change detection for road networks at lane level using GPS data are less common. This paper presents a novel method for automatic change detection of lane-level road networks based on GPS trajectories of vehicles. The proposed method includes two steps: map matching at lane level and lane-level change recognition. To integrate the most up-to-date GPS data with a lane-level road network, this research uses a fuzzy logic road network matching method. The proposed map-matching method starts with a confirmation of candidate lane-level road segments that use error ellipses derived from the GPS data, and then computes the membership degree between GPS data and candidate lane-level segments. The GPS trajectory data is classified into successful or unsuccessful matches using a set of defuzzification rules. Any topological and geometrical changes to road networks are detected by analysing the two kinds of matching results and comparing their relationships with the original road network. Change detection results for road networks in Wuhan, China using collected GPS trajectories show that these methods can be successfully applied to detect lane-level road changes including added lanes, closed lanes and lane-changing and turning rules, while achieving a robust detection precision of above 80%.

Kernel Density Estimation (KDE) is an important approach to analyse spatial distribution of point features and linear features over 2-D planar space. Some network-based KDE methods have been developed in recent years, which focus on estimating density distribution of point events over 1-D network space. However, the existing KDE methods are not appropriate for analysing the distribution characteristics of certain kind of features or events, such as traffic jams, queue at intersections and taxi carrying passenger events. These events occur and distribute in 1-D road network space, and present a continuous linear distribution along network. This paper presents a novel Network Kernel Density Estimation method for Linear features (NKDE-L) to analyse the space–time distribution characteristics of linear features over 1-D network space. We first analyse the density distribution of each linear feature along networks, then estimate the density distribution for the whole network space in terms of the network distance and network topology. In the case study, we apply the NKDE-L to analyse the space–time dynamics of taxis’ pick-up events, with real road network and taxi trace data in Wuhan. Taxis’ pick-up events are defined and extracted as linear events (LE) in this paper. We first conduct a space–time statistics of pick-up LE in different temporal granularities. Then we analyse the space–time density distribution of the pick-up events in the road network using the NKDE-L, and uncover some dynamic patterns of people’s activities and traffic condition. In addition, we compare the NKDE-L with quadrat method and planar KDE. The comparison results prove the advantages of the NKDE-L in analysing spatial distribution patterns of linear features in network space.

Lane-based road network information, such as the number and locations of traffic lanes on a road, has played an important role in intelligent transportation systems. In this paper, we propose a Collecting Lane-based Road Information via Crowdsourcing (CLRIC) method, which can automatically extract detailed lane structure of roads by using crowdsourcing data collected by vehicles. First, CLRIC filters the high-precision GPS data from the raw trajectories based on region growing clustering with prior knowledge. Second, CLRIC mines the number and locations of traffic lanes through optimized constrained Gaussian mixture model. Experiments are conducted with taxi GPS trajectories in Wuhan, China, and the results show that CLRIC is quantified and displays detailed road networks with the number and locations of traffic lanes comparing with the satellite image and human-interpreted situation.

In this paper, a bottom–up vehicle emission model is proposed to estimate real-time$\hbox{CO}_{2}$emissions using intelligent transportation system (ITS) technologies. In the proposed model, traffic data that were collected by ITS are fully utilized to estimate detailed vehicle technology data (e.g., vehicle type) and driving pattern data (e.g., speed, acceleration, and road slope) in the road network. The road network is divided into a set of small road segments to consider the effects of heterogeneous speeds within a road link. A real-world case study in Beijing, China, is carried out to demonstrate the applicability of the proposed model. The spatiotemporal distributions of$ \hbox{CO}_{2}$emissions in Beijing are analyzed and discussed. The results of the case study indicate that ITS technologies can be a useful tool for real-time estimations of$\hbox{CO}_{2}$emissions with a high spatiotemporal resolution.