[Objectives] With accelerating urbanization and a surge in vehicle numbers, urban traffic systems face immense pressure. Intelligent transportation systems, a vital component of smart cities, are widely employed to improve urban traffic conditions, with traffic speed prediction being a key research focus. However, the complex coupling relationships and dynamically varying characteristics of urban traffic network nodes pose challenges for existing traffic speed prediction methods in accurately capturing dynamic spatio-temporal correlations. Spatio-temporal graph neural networks have proven to be among the most effective models for traffic speed prediction tasks. However, most methods heavily rely on prior knowledge, limiting the flexibility of spatial feature extraction and hindering the dynamic representation of road network topology. Recent approaches, such as adaptive adjacency matrix construction, address the limitations of static graphs. However, they often overlook the synergy between dynamic features and static topology, making it difficult to fully capture the complex fluctuations in traffic flow, which in turn limits prediction accuracy and adaptability. [Methods] To address these challenges, this study formulates urban traffic speed prediction as a multivariate time-series forecasting problem and proposes a traffic speed prediction model based on a Multivariate Time-series Dynamic Graph Neural Network (MTDGNN). Leveraging real-time traffic information and predefined static graph structures, the model adaptively generates dynamic traffic graphs to capture spatial dependencies through a graph learning layer and integrates them with static road network graphs to capture spatial dependencies from multiple perspectives. Meanwhile, the alternating use of graph convolution and temporal convolution modules constructs a multi-level spatial neighborhood and temporal receptive field, fully exploring the spatial and temporal features of traffic data. [Results] The MTDGNN model was tested on real traffic data from 397 road sections in eastern Beijing, collected between April 1, 2017, and May 31, 2017. Its prediction results were compared against nine benchmark models and seven ablation models. Compared to benchmark models, MTDGNN reduced the average MAE by at least 2.24% and the average RMSE by at least 3.98%. [Conclusions] Experimental results demonstrate that the MTDGNN model achieves superior prediction accuracy in MAE, RMSE, and MAPE evaluation metrics, highlighting its robustness and effectiveness in complex traffic scenarios.

[Objectives] Cancer is the leading cause of death in most countries worldwide, posing a significant threat to human longevity and public health. This study explores the spatiotemporal distribution characteristics of mortality rates for five major types of cancer worldwide and provides predictions for future trends. [Methods] Aiming at five major cancer types (lung, colorectal, gastric, liver, and pancreatic cancer) in 200 countries from 2011 to 2019, this study used GBD and World Bank data to extract spatial heterogeneity of the factors affecting cancer mortality using the MGWR model. The ARIMA model was used to extract temporal trend characteristics of various cancer mortality rates. Such spatial-temporal information was integrated into a Bayesian spatial-temporal model to predict and evaluate the global mortality risk for the five types of cancer. [Results] Results revealed that global death rate for all five cancer types increased, with an average rise of 17.2 deaths per 100 000 people in 2019 compared to 2011. Over 72.8% of countries exhibited a high relative risk of cancer death (RR>1), indicating significant spatial clustering. [Conclusions] Regions such as Europe, Central Asia, North America, and East Asia and the Pacific experienced faster increases in cancer death rates compared to Africa and South Asia. Compared to low- and middle-income countries, middle-high- and high-income countries showed a more pronounced upward trend in cancer mortality and a higher relative risk. Key factors influencing global cancer mortality included the percentage of the population aged 65 years and older, smoking, alcohol consumption, low physical activity, high sugar diets, GDP per capita, GNI per capita, and health expenditure per capita. By integrating the advantages of different geographical spatial-temporal analysis methods, this study developed an innovative spatiotemporal prediction model of disease risk that integrates spatial-temporal grouping variables and multiple influencing factors. This proposed model is highly flexible, interpretable, and better suited for quantifying non-stationarity spatial-temporal relationships. While the structured spatial and temporal effects increase computational demands, the model effectively assesses cancer mortality risk across regions, offering robust insights into the spatiotemporal dynamics of disease. This approach deepens the integration of geospatial modeling technology and epidemiological research, providing significant scientific contributions to global cancer research, prevention, and control planning.

[Objectives] City-wide traffic flow prediction plays a crucial role in intelligent transportation systems. Traditional studies partition road networks into grids, represent them as graph structures with grids as nodes, and use graph neural networks for region-level prediction. However, this region-based approach overlooks the relationships between individual roads, making it difficult to reflect traffic flow changes of roads. Methods based on road segment data can better capture spatial connections between roads and enable more accurate traffic flow predictions. However, mapping trajectory data to roads presents challenges such as redundant data and trajectory mismatches, and traffic flow data after mapping is sparse. Existing methods struggle to effectively capture the spatial correlation in sparse traffic conditions. [Methods] To address these issues, this study proposes an Attention Spatio-Temporal Neural Network (ASTNN) model for road-level sparse traffic flow prediction. The model first preprocesses trajectory data and applies Hidden Markov Model (HMM)-based map matching to obtain road-level traffic flow data. It then introduces an adaptive compact 2D image representation method to model the road network as a 2D image, where road segments are represented as pixel points. Based on an analysis of the spatial and temporal characteristics of traffic flow, two new attentional spatiotemporal blocks are proposed: Attentional Spatio-Temporal Memory Block (ASTM block) for mining temporal correlations and attentional spatial-temporal focusing block (ASTF block) for extracting spatial sparse features. By integrating these two blocks with external information, ASTNN is constructed to achieve road-level traffic flow prediction. [Results] This study uses Chengdu taxi trajectory data as a case study. After preprocessing trajectory data and mapping traffic flow, the proposed model is validated on a five-level road network within Chengdu’s third ring area. Results indicate that the proposed data processing method reduces trajectory-to-road network matching time by 73.6%. In the comparative experiments with existing models, such as Convolutional Neural Network (CNN), Convolutional Long Short-Term Memory (ConvLSTM), Gated Recurrent Unit (GRU), and Spatial-Temporal Neural Network (STNN), ASTNN achieves the highest prediction accuracy in terms of Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and R-squared (R²). Furthermore, the study confirms the significant improvement in prediction accuracy when incorporating temperature data into ASTNN, providing new insights for optimizing model performance. [Conclusions] The ASTNN model proposed in this study provides an effective framework for city-wide, road-level sparse traffic flow prediction, offering valuable insights for intelligent transportation systems.

[Significance] Multimodal remote sensing image matching has become a fundamental task in integrated Earth observation, enabling precise spatial alignment across heterogeneous image sources. [Progress] As the diversity of sensing modalities, acquisition geometries, and temporal conditions increases, traditional matching frameworks have proven inadequate for capturing complex variations in radiometric responses, geometric configurations, and semantic representations. This technological gap has driven a significant paradigm shift from handcrafted feature engineering to deep learning-based solutions, which now form the core of current research and application development. This paper provides a comprehensive and structured review of recent advances in deep learning methods for multimodal remote sensing image matching, with an emphasis on the evolution of methodological paradigms and technical frameworks. It establishes a clear dual-path classification: the single-session approach and the end-to-end approach. The former selectively replaces or enhances individual components of traditional pipelines, such as feature encoding or similarity estimation, using neural network modules. The latter integrates the entire matching process into a unified network architecture, enabling joint optimization of feature learning, transformation modeling, and correspondence inference within a closed loop. This progression reflects the field's transition from modular adaptation to holistic modeling, revealing a deeper integration of data-driven representation learning with geometric reasoning. The review further examines the development of architectural strategies supporting this evolution, including attention mechanisms, graph-based structures, hierarchical feature fusion, and modality-bridging transformations. These innovations contribute to improved robustness, semantic consistency, and adaptability across diverse matching scenarios. Recent trends also demonstrate a growing reliance on pretrained vision foundation models, which provide transferable feature spaces and reduce the dependence on large-scale labeled datasets. In addition to summarizing technical advancements, the paper analyzes representative datasets, performance evaluation strategies, and the current challenges that constrain real-world deployment. These include limited data availability, weak cross-scene generalization, computational inefficiency, and insufficient interpretability. [Prospect] By synthesizing methodological progress with practical demands, the review identifies key directions for future research, including the design of modality-invariant representations, physically-informed neural architectures, and lightweight solutions tailored for scalable, real-time image registration in complex operational environments.

[Significance] Street View Image-based Visual Place Recognition (SV-VPR) is a geographical location recognition technology that relies on visual feature information. Its core task is to predict and accurately locate unknown locations by analyzing the visual features of street view images. This technology must overcome challenges such as appearance changes under different environmental conditions (e.g., lighting differences between day and night, seasonal variations) and viewpoint differences (e.g., perspective deviations between vehicle-mounted cameras and satellite images). Accurate recognition is achieved through calculating image feature similarity, applying geometric constraints, and related methods. As an interdisciplinary field of computer vision and geographic information science, SV-VPR is closely related to visual positioning, image retrieval, SLAM, and more. It has significant application value in areas such as UAV autonomous navigation, high-precision positioning for autonomous driving, construction of geographical boundaries in cyberspace, and integration of augmented reality environments. It is particularly advantageous in GPS-denied environments. [Analysis] This paper systematically reviews the research progress of visual location recognition based on street view images, covering the following aspects: First, the basic concepts and classifications of visual place recognition technologies are introduced. Second, the foundational principles and categorization methods specific to street view image-based visual place recognition are discussed in depth. Third, the key technologies in this field are analyzed in detail. Furthermore, relevant datasets for street view image-based visual place recognition are comprehensively reviewed. In addition, evaluation methods and index systems used in this domain are summarized. Finally, potential future research directions for SV-VPR are explored. [Purpose] This review aims to provide researchers with a systematic overview of the technological development trajectory of SV-VPR, helping them quickly understand the current research landscape. It also offers a comparative analysis of key technologies and evaluation methods to support algorithm selection, and identifies emerging challenges and potential breakthrough areas to inspire innovative research.

[Significance] Cities globally face increasingly frequent multi-hazard risks, driving them pursuing more sustainable and resilient urban transportation systems. This paper presents a comprehensive systematic literature review of the application of spatial-temporal data in transportation system resilience studies. It highlights the pivotal role of spatial-temporal big data in understanding and enhancing the resilience of urban transportation systems under various hazard scenarios. Spatial-temporal big data, characterized by high temporal resolution and fine spatial granularity, has been increasingly applied to the field of transportation system resilience, providing essential support for decision-makers. [Progress] This study reveals two significant findings: Firstly, quantitative analysis of transportation system resilience is one of the most widely applied uses of spatial-temporal big data. However, real-time monitoring and early warning explorations are relatively rare. Most studies remain at the modelling and numerical simulation stage, indicating a need for more empirical studies using multi-source spatial-temporal big data. Moreover, compared to English literature, Chinese transportation system resilience studies are primarily qualitative and lack empirical research, indicating divergent research emphases between domestic and international scholars. Secondly, high-quality, multi-source spatial-temporal big data could facilitate more comprehensive spatial analysis in transportation system resilience studies. Improved data quality allows for deeper exploration from a microscopic perspective, focusing on individual behaviors and aligning closely with real-world needs. The concept of resilience has evolved from its previous post-disaster focus to a comprehensive life-cycle perspective encompassing pre-, during-, and post-disaster phases, transforming the study framework for transportation system resilience. [Prospect] As spatial-temporal big data technology advances and new transportation modes emerge, more innovations and breakthroughs in transportation system resilience studies are expected. Future research should further explore and utilize the potential of spatial-temporal big data in this field, amplifying the policy ramifications of abrupt-onset occurrences. Increased emphasis should be placed on research conducted at the scale of urban agglomerations. Simultaneously, a nuanced examination from a microscopic perspective is imperative to dissect the underlying causes and mechanisms contributing to variations in resilience among distinct groups. Despite the significant progress in transportation system resilience studies, there are still challenges in data collection, processing, and analysis. As technology progresses, researchers should leverage advanced algorithms, platforms, and tools to enhance data processing capabilities and analytical precision, facilitating more complex and detailed studies on transportation system resilience. This will provide a scientific basis for planning and managing urban transportation systems, significantly contributing to the overall resilience and sustainable development of cities.

[Objectives] The primary objective is to enhance the accuracy of vehicle trajectory prediction at intersections and address the challenges in predicting trajectories in multi-vehicle interaction scenarios. This is crucial for improving the safety and efficiency of autonomous driving and traffic management in complex urban intersections. [Methods] An Enhanced Adjacency Graph Convolutional Network-Transformer (EAG-GCN-T) vehicle trajectory prediction model is developed. The INTERACTION public dataset is employed, with data smoothing techniques applied to mitigate noise. Model comparison and validation experiments are conducted to assess performance. The model’s accuracy is evaluated by comparing error assessment indicators against different baseline models, analyzing interaction capabilities, generalization ability, and driving behavior recognition. The EAG-GCN-T model combines an Enhanced Adjacency Graph Convolutional Network (EAG-GCN) and a Transformer module. The EAG-GCN module accurately models spatial interactions between vehicles by considering relative speed and distance using an enhanced weighted adjacency matrix. The Transformer module captures temporal dependencies and generates future trajectories, improving spatiotemporal prediction ability. [Results] In long-term single-vehicle trajectory prediction, the Average Displacement Error (ADE) and Final Displacement Error (FDE) are reduced by 69.4%, 39.8%, and 33.3% and 71.9%, 32.5%, and 27.4% respectively, compared to CV, ARIMA, and CNN-LSTM models. In multi-vehicle interaction prediction, the FDE is reduced by 19.5% and 20.6% compared to the GRIP model. Compared with three interaction mechanisms, EAG-GCN-T achieves the lowest overall error across all time domains, with ADE/FDE values of 0.53 and 0.74, respectively. EAG-GCN-T achieves more reasonable Driving Area Compliance (DAC) and Trajectory Point Loss Rate (MR), demonstrating strong adaptability in ramps and roundabouts. The model accurately predicts driving behaviors such as following, lane-changing, evasion, and their impacts on trajectories, with predicted trajectories highly consistent with actual vehicle movements. [Conclusions] The EAG-GCN-T model effectively addresses vehicle trajectory prediction in multi-vehicle interaction scenarios at intersections. It demonstrates high accuracy, strong interactivity, and excellent generalization ability. This model provides a novel solution for vehicle trajectory prediction in intelligent transportation systems, offering significant potential for advancing autonomous driving and intelligent traffic management.

[Objectives] This study addresses the critical challenges in typhoon disaster knowledge services, which are often hindered by "massive data, scarce knowledge, and limited services." The core objective is to rapidly distill actionable knowledge from vast datasets to enhance disaster management efficacy and mitigate typhoon-related impacts. Large Language Models （LLMs）, renowned for their superior performance in natural language processing, are leveraged to deeply mine disaster-related information and provide robust support for advanced knowledge services. [Methods] This research establishes a typhoon disaster knowledge service framework encompassing three layers: data, knowledge, and service. [Results] For the data-to-knowledge layer, an LLM-driven （Qwen2.5-Max） automated method for constructing typhoon disaster Knowledge Graphs （KGs） is proposed. This method first introduces a multi-level typhoon disaster knowledge representation model that integrates spatiotemporal characteristics and disaster impact mechanisms. A specialized training dataset is curated, incorporating typhoon-related texts with explicit temporal and spatial attributes. By adopting a "pre-training + fine-tuning" paradigm, the framework efficiently transforms raw disaster data into structured knowledge. For the knowledge-to-service layer, an LLM-based intelligent question-answering system is developed. Utilizing the constructed typhoon disaster KG, this system employs Graph Retrieval-Augmented Generation （GraphRAG） to retrieve contextually relevant knowledge from the graph and generate user-specific disaster prevention and mitigation guidance. This approach ensures seamless conversion of structured knowledge into practical services, such as personalized evacuation plans and resource allocation strategies. [Conclusions] The study highlights the transformative potential of LLMs in typhoon disaster management and lays a foundation for integrating LLMs with geospatial technologies. This interdisciplinary synergy advances Geographic Artificial Intelligence (GeoAI) and paves the way for innovative applications in disaster service.

[Significance] Lunar remote sensing is a critical method to ensure the safety and success of lunar exploration missions while advancing lunar scientific research. It plays a significant role in understanding the Moon's geological evolution and the formation of the Earth-Moon system. Accurate lunar topographic maps are essential for mission planning, including landing site selection, navigation, and resource identification. These maps also provide valuable data for studying planetary processes and the history of the solar system. [Progress] In recent years, with growing global interest and investment in lunar exploration, remarkable progress has been made in remote sensing technology. These advancements have significantly improved the precision, resolution, and coverage of lunar topographic mapping. Various lunar remote sensing missions, such as China's Chang'e program, NASA's Lunar Reconnaissance Orbiter, and missions by other space agencies, have acquired substantial amounts of multi-source, multi-modal, and multi-scale data. This wealth of data has laid a solid foundation for technological breakthroughs. For instance, high-resolution laser altimetry, optical photogrammetry, and synthetic aperture radar have provided detailed datasets, enabling refined mapping of the Moon's surface. However, the dramatic increase in data volume, complexity, and heterogeneity presents challenges for effective processing, integration, and application in topographic mapping. This paper provides a comprehensive overview of the current state of lunar topographic remote sensing and mapping, focusing on the implementation and data acquisition capabilities of major lunar remote sensing missions during the second wave of lunar exploration. It systematically summarizes the latest research progress in key surveying and mapping technologies, including laser altimetry, which enables precise elevation measurements; optical photogrammetry, which reconstructs surface features using high-resolution imagery; and synthetic aperture radar, which provides unique insights into topographic and subsurface structures. [Prospect] In addition to reviewing recent advancements, the paper discusses future trends and challenges in the field. Key recommendations include enhancing sensor functionality and performance metrics to improve data quality, optimizing the lunar absolute reference framework for consistency and accuracy, leveraging multi-source data fusion for fine-scale modeling, expanding scientific applications of lunar topography, and developing intelligent and efficient methods to process massive amounts of remote sensing data. These efforts will not only support upcoming lunar exploration missions, such as China's manned lunar landing program scheduled for 2030, but also contribute to a deeper understanding of the Moon and its relationship with Earth.

[Objectives] Classical statistical inference typically relies on the assumptions of large sample sizes and independent, identically distributed (i.i.d.) observations, conditions that spatio-temporal data frequently violate, leading to inherent theoretical limitations in conventional approaches. In contrast, Bayesian spatio-temporal statistical methods integrate prior knowledge and treat all model parameters as random variables, thereby forming a unified probabilistic inference framework. This enables the incorporation of a broader range of uncertainties and offers robustness in modelling small samples and dependent structures, making Bayesian methods highly advantageous and increasingly influential in spatio-temporal analysis. [Progress] From the perspective of methodological evolution, this paper systematically reviews mainstream Bayesian spatio-temporal statistical models from two complementary perspectives: traditional Bayesian statistics and the Bayesian machine learning. The former includes Bayesian Spatio-temporal Evolutionary Hierarchical Models, Bayesian Spatio-temporal Regression Hierarchical Models, Bayesian Spatial Panel Data Models, Bayesian Geographically Weighted Spatio-temporal Regression Models, Bayesian Spatio-temporal Varying Coefficient Models, and Bayesian Spatio-temporal Meshed Gaussian Process Model. The latter includes Bayesian Causal Forest Models, Bayesian Spatio-temporal Neural Networks, and Bayesian Graph Convolutional Neural Networks. In terms of application, the review highlights representative studies across domains such as public health, environmental sciences, socio-economic and public safety, as well as energy and engineering. [Prospect] Bayesian spatio-temporal statistical methods need to achieve breakthroughs in multi-source heterogeneous data modeling, integration with deep learning, incorporation of causal inference mechanisms, and optimization of high-performance computing. These advances are essential to balance theoretical rigor with practical adaptability and to promote the development of a next-generation spatio-temporal modeling paradigm characterized by causal inference, adaptive generalization, and intelligent analysis.

[Objectives] With the deepening of urbanization and intensified market competition, long working hours have become a pervasive social issue, posing challenges to both workers' physical and mental health and to urban sustainable development. Current studies on urban residents' work activities predominantly rely on questionnaire survey data, which suffer from limited sample sizes and a lack of in-depth exploration into long working hours in megacities. [Methods] This research utilized mobile signaling data from Beijing, collected between November and December 2019, to identify stay points using a threshold rule method. Residential and workplace locations were determined through a time-window approach, and users' working hours were extracted. The study then examined the spatial distribution patterns of long-working-hours employees (defined as those working over 40 hours per week) and investigated spatial characteristics across various gender and age groups. Finally, the study also explored the characteristics of long working hours in different employment clusters in Beijing. [Results] The findings reveal that 47.1% of Beijing's workforce engages in long working hours (weekly working hours ≥40 hours), with an average weekly working duration of 48.86 hours. Spatial analysis demonstrates a polycentric agglomeration pattern, concentrated in major employment hubs such as the CBD, Financial Street, Zhongguancun, and Yizhuang. Significant disparities exist across gender and age groups. Male employees work an average of 49.62 hours per week, 1.5 hours more than their female counterparts (48.12 hours). Among male age groups, those aged 20~29 have the longest average weekly working hours at 50.68 hours. In contrast, although women aged 30~39 constitute the largest proportion of the female workforce (22.13%), their average weekly working hours are the lowest, at 47.59 hours. The characteristics of overtime work in different employment clusters show a clear pattern: the CBD and Zhongguancun have a higher number of overtime workers, while Yizhuang stands out with the highest proportion at 58.0%. Wholesale and logistics hubs such as Xinfadi and Majuqiao exhibit the most intensive work schedules, with average weekly working hours exceeding 50 hours. [Conclusions] This study provides rich empirical evidence for understanding the phenomenon of long working hours in Beijing. The results offer data-driven support for optimizing labor time policies, contributing to urban sustainable development and social equity.

[Objectives] Existing semantic change detection methods fail to fully utilize local and global features in very high-resolution images and often overlook the spatial-temporal dependencies between bi-temporal remote sensing images, resulting in inaccurate land cover classification results. Additionally, the detected change regions suffer from boundary ambiguity, leading to low consistency between the detected and actual boundaries. [Methods] To address these issues, inspired by the Vision State Space Model (VSSM) with long-sequence modeling capabilities, we propose a semantic change detection network, CVS-Net, which combines Convolutional Neural Networks (CNNs) and VSSM. CVS-Net effectively leverages the local feature extraction capability of CNNs and the long-distance dependency modeling ability of VSSM. Furthermore, we embed a bi-directional spatial-temporal feature modeling module based on VSSM into CVS-Net to guide the network in capturing spatial-temporal change relations. Finally, we introduce a boundary-aware reinforcement branch to enhance the model's performance in boundary localization. [Results] We validate the proposed method on the SECOND and Fuzhou GF2 (FZ-SCD) datasets and compare it with five state-of-the-art methods: HRSCD.str4, Bi-SRNet, ChangeMamba, ScanNet, and TED. Comparative experiments demonstrate that our method outperforms these existing approaches, achieving a Sek of 23.95% and mIoU of 72.89% on the SECOND dataset, and a Sek of 23.02% and mIoU of 72.60% on the FZ-SCD dataset. In ablation experiments, as the proposed modules were progressively added, the SeK improved to 21.26%, 23.04%, and 23.95%, respectively, demonstrating the effectiveness of each module. Notably, compared with CNN-based, Transformer-based, and Mamba-based feature extractors,the proposed CNN-VSS feature extractor achieved the highest Sek, mIoU and Fscd, indicating its robust feature extraction capability and effective balance between local and global feature representation. Additionally, ST-SS2D improved the Sek score by 1.19% on average compared to other spatial-temporal modeling methods, effectively capturing the spatial-temporal dependencies of bi-temporal features and enhancing the model's ability to infer potential feature changes. Furthermore, the proposed edge-enhancement branch improved the consistency between detected and actual boundaries, achieving a consistency degree of 92.97%. [Conclusions] The proposed method significantly improves both the attribute and geometric accuracy of semantic change detection, providing technical references and data support for sustainable urban development and land resource management.

[Significance] The characteristics of the lunar surface, including its mineral compositions, geological formations, environmental factors, and temperature variations, are essential for advancing our understanding of the Moon. These features provide a wealth of scientific data for lunar research, such as resource distribution, environmental characteristics, and evolutionary history. Spectral imagers, which detect mineral compositions in a nondestructive way, play a crucial role in analyzing the mineral compositions of the lunar surface and have become key payloads in scientific exploration missions. With the increasing demand for high-precision lunar exploration data and advancements in spectral imaging technology, there is a growing trend toward acquiring lunar remote sensing data with higher spatial and spectral resolution across a broad spectral range. This trend is shaping the future of lunar orbit exploration, allowing for unprecedented detail in probing the Moon's surface. However, the higher resolution of spatial and spectral data also introduces significant challenges in data processing. [Progress] This paper begins by summarizing existing lunar spectral orbit data, including payload parameters and associated scientific findings. It then explores specific technical challenges in the data processing chain, such as pre-processing and the calculation of lunar surface parameters. Mapping surface compositions through spectral remote sensing is particularly complex due to the mixing of minerals within rocks, which can obscure clear spectral signatures. To address these challenges, various theoretical and empirical approaches have been developed. This paper proposes technical methods and potential solutions to overcome these obstacles.[Conclusions] In conclusion, detailed studies of lunar surface characteristics and the acquisition of high-resolution spectral data are vital for advancing lunar science. Lunar hyperspectral data are expected to support manned lunar exploration and scientific research by enabling the identification of various minerals on the Moon's surface and determining their abundance through hyperspectral observations. Advances in spectral imaging technology and the development of solutions for processing high-resolution data will significantly enhance lunar and planetary science capabilities. These efforts will pave the way for deeper insights into the Moon's geology and potential resource utilization.

[Objectives] Using deep learning methods for landslide identification can significantly improve efficiency and is of great importance for landslide disaster prevention and mitigation. The DeepLabV3+ algorithm effectively captures multi-scale features, thereby improving image segmentation accuracy, and has been widely used in the segmentation and recognition of remote sensing images. [Methods] We propose an improved model based on DeepLabV3+. First, the Coordinate Attention (CA) mechanism is incorporated into the original model to enhance its feature extraction capabilities. Second, the Atrous Spatial Pyramid Pooling (ASPP) module is replaced with the Dense Atrous Spatial Pyramid Pooling (DenseASPP) module, which helps the network capture more detailed features and expands the receptive field, effectively addressing the limitations of inefficient or ineffective dilated convolution. A Strip Pooling (SP) branch module is added in parallel to allow the backbone network to better leverage long-range dependencies. Finally, the Cascade Feature Fusion (CFF) module is introduced to hierarchically fuse multi-scale features, further improving segmentation accuracy. [Results] Experiments on the Bijie landslide dataset show that, compared with the original model, the improved model achieves a 2.2% increase in MIoU and a 1.2% increase in the F1 score. Compared with other mainstream deep learning models, the proposed model demonstrates higher extraction accuracy. In terms of segmentation quality, it significantly improves the overall accuracy in identifying landslide areas, reduces misclassification and omission, and yields more precise delineation of landslide boundaries. [Conclusions] Based on experiments using the landslide debris flow disaster dataset in Sichuan and surrounding areas, along with practical application verification, the proposed method demonstrates strong recognition capability across landslide images in diverse scenarios and levels of complexity. It performs particularly well in challenging environments such as areas with dense vegetation or proximity to rivers, showing strong generalization ability and broad applicability.

[Objectives] To address issues in existing remote sensing building extraction models, including poor feature representation ability due to redundancy, unclear building boundaries, and the loss of small buildings, [Methods] we propose a detail enhancement and cross-scale geometric feature sharing network (DCS-Net). This network consists of an Information Decoupling and Aggregation Module (IRDM), a Local Mutual Similarity Detail Enhancement Module (LMSE), and a Cross-scale Geometric Feature Fusing Module (CGFF), designed to guide small target inference. The IRDM module separates and reconstructs redundant features by assigning weights, thereby suppressing redundancy in both spatial and channel dimensions and promoting effective feature learning. The LMSE module enhances the accuracy and completeness of building edge information by dynamically selecting windows and specifying pixel clustering based on local mutual similarity between encoder-decoder features. The CGFF module computes the feature block relationships between the original image and various semantic-level feature maps to compensate for information loss, thereby improving the extraction performance of small buildings. [Results] The experiments in this paper are based on two public datasets: the WHU aerial dataset and the Massachusetts building detection dataset. The experimental results demonstrate the following: (1) Compared with existing building extraction algorithms such as UNet, PSPNet, Deeplab V3+, MANet, MAPNet, DRNet, Build-Former, MBR-HRNet, SDSNet, HDNet, DFFNet, and UANet, DCS-Net has achieved significant improvements across various evaluation metrics, demonstrating the effectiveness of the proposed method. (2) On the WHU dataset, the Intersection over Union (IoU), F1 score, and 95% Hausdorff Distance (95%HD) reached 92.94%, 96.35%, and 75.79%, respectively, outperforming the current best algorithm by 0.79%, 0.44%, and 1.90%. (3) On the Massachusetts dataset, the metrics were 77.13%, 87.06%, and 205.26, with improvements of 0.72%, 0.43%, and 13.84%, respectively. [Conclusions] These results indicate that DCS-Net can more accurately and comprehensively extract buildings from remote sensing images, significantly alleviating the issue of small building loss.

[Significance] Human mobility is closely tied to transportation, infectious disease spread, and public safety, making trajectory analysis and modeling a long-standing research focus. While numerous specialized trajectory models, such as interpolation, prediction, and classification models, have been developed using machine learning or deep learning, most are task-specific and trained on localized datasets, limiting their generalizability across tasks, regions, or trajectory data. Recent advances in generative AI have demonstrated the potential of foundation models in NLP and computer vision, motivating the need for a trajectory foundation model capable of learning universal patterns from large-scale mobility data to support diverse downstream applications. [Methods] This paper first reviews the research progress of various specialized trajectory models. It then categorizes trajectory modeling tasks into conventional tasks (e.g., trajectory similarity computation, interpolation, prediction, and classification) and generation task (i.e., trajectory generation), and elaborates on recent advances in trajectory foundation models for these two types of tasks. [Conclusions] The paper argues that trajectory foundation models for conventional tasks should enhance not only task generalization but also spatial and data generalization. Trajectory foundation models for generation task must address the challenge of spatial generalization, enabling the generation of large-scale trajectory data "from scratch" based on easily obtainable macro-level urban data or features. Furthermore, integrating trajectory data with other data types (e.g., text, maps, and other geospatial data) to construct multimodal geographic foundation models, as well as developing application-oriented trajectory foundation models for fields such as transportation, public health, and public safety, are promising research directions worthy of future exploration.

[Objectives] Geographic modeling aims to appropriately couple diverse geographic models and their specific algorithmic implementations to form an effective and executable model workflow for solving specific, unsolved application problems. This approach is highly valuable and in high demand in practice. However, traditional geographic modeling is designed with an execution-oriented approach, which plays a heavy burden on users, especially non-expert users. [Methods] In this position paper, we advocate not only for the necessity of intelligent geographic modeling but also achieving it through a so-called recursive geographic modeling approach. This new approach originates from the user's modeling target, which can be formalized as an initial elemental modeling question. It then reasons backward to resolve the current elemental modeling question and iteratively updates new elemental modeling questions in a recursive manner. This process enables the automatic construction of an appropriate geographic workflow model tailored to the application context of the user's modeling problem, thereby addressing the limitations of traditional geographic modeling. [Progress] Building on this foundational concept, this position paper introduces a series of intelligent geographic modeling methods developed by the authors. These methods aim to reduce the geographic modeling burden on non-expert users while assuring the appropriateness of automatically constructed models. Specifically, each proposed intelligent geographic modeling method is designed to solve a specific type of elemental question within intelligent geographic modeling. The elemental questions include: (1) how to determine the appropriate model algorithm (or its parameter values) within the given application context, (2) how to select the appropriate covariate set as input for a model without a predetermined number of inputs (e.g., a soil mapping model without predetermined environmental covariates as inputs), (3) how to determine the structure of a model that integrates multiple coupled modules (e.g., a watershed system model incorporating diverse process simulation modules), and (4) how to determine the proper spatial extent of input data for a geographic model when a specific area of interest is assigned by the user. The key to solving these elemental questions lies in the effective utilization of geographic modeling knowledge, particularly application-context knowledge. However, since application-context knowledge is typically unsystematic, empirical, and implicit, we developed case formalization and case-based reasoning strategies to integrate this knowledge within the proposed methods. Based on the recursive intelligent geographic modeling approach and the correspondingly methods, we propose an application schema for intelligent geographic modeling and computing. This schema is grounded in domain modeling knowledge, particularly case-based application-context knowledge, and leverages the “Data-Knowledge-Model” tripartite collaboration. A prototype of this approach has been implemented in an intelligent geospatial computing system called EGC (EasyGeoComputing). [Prospect] Finally, this position paper discusses the emerging role of large language models in geographic modeling. Their potential applications, relationships with the research presented here, and prospects for future research directions are explored.

[Objectives] Small object detection is of great significance in both military and civil applications. However, due to challenges such as low resolution, high noise environments, target occlusion, and complex backgrounds, traditional detection methods often struggle to achieve the necessary accuracy and robustness. The problem of detecting small objects in complex scenes remains highly challenging. Therefore, this paper proposes a hybrid feature and multi-scale fusion algorithm for small object detection. [Methods] First, a Hybrid Conv and Transformer Block (HCTB) is designed to fully utilize local and global context information, enhancing the network's perception of small objects while optimizing computational efficiency and feature extraction capability. Second, a Multi-Dilated Shared Kernel Conv (MDSKC) module is introduced to extend the receptive field of the backbone network using dilated convolutions with varying expansion rates, thereby enabling efficient multi-scale feature extraction. Finally, the Omni-Kernel Cross Stage Model (OKCSM), constructed based on the concepts of Omni-Kernel and Cross Stage Partial, is integrated to optimize the small target feature pyramid network. This approach helps preserve small object information and significantly improves detection performance. [Results] Ablation and comparison experiments were conducted on the VisDrone2019 and TinyPerson datasets. Compared to the baseline model YOLOv8n, the proposed method improves precision, recall, mAP@₅₀, and mAP@_50:95 by 1.3%, 3.1%, 3%, and 1.9%, respectively on VisDrone2019, and by 3.6%, 1.3%, 2.1%, and 0.7%, respectively on TinyPerson. Additionally, the model size and GFLOPs are only 6.3 MB and 11.3 G, demonstrating its efficiency. Furthermore, compared with classical algorithms, such as HIC-YOLOv5, TPH- YOLOv5, and Drone-YOLO, the proposed algorithm demonstrates significant advantages and superior performance. [Conclusions] The algorithm effectively improves detection accuracy, confirming its strong performance in addressing small object detection in complex scenes.

[Objectives] Forests, as the backbone of terrestrial ecosystems, play crucial roles in climate regulation and soil and water conservation. Among the many threats to forests, the impact of forest fires is becoming increasingly severe. Analyzing the factors influencing forest fires is essential for preventing forest fires and formulating relevant strategies. [Methods] This study focuses on China, using multi-source data related to fires, vegetation, climate, topography, and human activities to analyze the spatial heterogeneity of forest fire driving forces from multiple perspectives. [Results] The findings reveal that: (1) At a global scale, the spatial distribution of forest fires is most influenced by FVC, with an explanatory power of 0.130 2, while climate factors exert a relatively strong influence. The interaction between driving factors is enhanced, and forest fire occurrence results from the combined influence of multiple factors. Moreover, a nonlinear relationship and impact threshold exist between these driving factors and the probability of forest fire occurrence. (2) At a local scale, climate and vegetation serve as key driving factors behind forest fires, significantly explaining their spatial distribution across different zones. Temperature is the most influential factor in the Cold Temperate Needle-leaf Forest region, the Temperate Coniferous and Broad-leaved Mixed Forest region, and the Alpine Vegetation of the Tibetan Plateau region, with explanatory powers of 0.313, 0.41, and 0.052, respectively. In contrast, wind speed is the dominant factor in the Warm Temperate Broad-leaved Forest region, with an explanatory power of 0.279. [Conclusions] The primary driving factors and their interactions vary across different regions, quantitatively confirming the spatial heterogeneity of forest fire driving forces. This research contributes to a national-scale understanding of forest fire drivers and fire hazard distribution in China, assisting policymakers in designing fire management strategies to mitigate potential fire risks.

[Objectives] Urban public transportation service quality is an important factor affecting residents' travel choices and quality of life, but the current development and reform of urban public transportation in China still has shortcomings, and it is necessary to incorporate public perception into the decision-making basis and improve service quality from the perspective of residents. Previous studies have two main limitations: first, they rely on traditional analysis methods based on traffic surveys, which fail to capture the regional differences in perceived service quality; second, they use big data from social media platforms, which are prone to information bias, polarization, and other issues, and do not reflect the public's real needs. Moreover, they mostly focus on public opinion analysis, without providing specific and feasible optimization paths. [Methods] To address these gaps, this paper proposes a method that combines public network participation and semantic analysis. It uses internet big data to extract online messages related to urban public transportation from the online interactive platform between government and citizens and analyzes their spatiotemporal features and perceived service quality. It also conducts spatial analysis and explores the service efficiency of the public transportation system in relation to the transportation facility distribution. Based on this, it offers optimization suggestions. The paper selects Wuhan as a case study, which is one of the national central cities and an important megacity in the middle reaches of the Yangtze River. The urban development area in Wuhan is a key zone for urbanization and a major hub for public travel activities, covering 15 functional zones. It has a complete public transportation facility allocation, including all the subway lines and stations, and most of the bus lines and stations in the city. [Results] The main findings are as follows: (1) The quality of public network participation data can reflect the spatiotemporal patterns of actual travel activities and has high credibility; (2) The emotional expression of the public varies across individuals and regions and the perceived service quality dimensions can be categorized into five topics: "public transportation planning and construction", "public transportation travel conditions", "residential community bus configuration", "public transportation route setting", and "public transportation operation service". Furthermore, the perceived service quality exhibits spatial imbalance and agglomeration; (3) Corresponding optimization suggestions are made for the road system in the main urban area, subway stations in the far urban area, and bus routes at the junction of the main urban area and far urban area. [Conclusions] The research results of this paper provide a new method for fine-grained identification and optimization of spatial differences in urban public transportation perceived service quality, and also demonstrate the application value of public network participation data in facilitating government decision-making.

[Objectives] The soil freeze-thaw process is a sensitive indicator of ecosystem and climate change in the Qinghai-Tibet Plateau. Studying its spatial and temporal variation characteristics is crucial for understanding the climate change and its ecological impacts in this region. [Methods] Based on ERA5-LAND surface temperature data from 2003 to 2022, the spatial and temporal variation characteristics of soil freezing start time, freezing end time, freezing duration, and freezing days in the Qinghai-Tibet Plateau were analyzed using the Theil-Sen slope estimation method and the Mann-Kendall test. The dominant factors influencing the spatial differentiation of freeze-thaw parameters were analyzed using the Geodetector method. Finally, correlation analysis was performed to explore the relationships between freeze-thaw parameters and the main driving factors. [Results] (1) The soil freeze-thaw process in the Qinghai-Tibet Plateau showed a trend of delayed freezing start times from northwest to southeast. Conversely, freezing end times, durations, and freezing days were significantly shortened, affecting 74.05% of the regional pixel area. (2) The four soil freeze-thaw parameters varied significantly across different regions over time. In permafrost regions, freezing initiation was delayed by 12.7 days, while freezing days were reduced by 20.4 days, showing the fastest changes. (3) Temperature was the dominant factor influencing the spatial differentiation of freezing days in the Qinghai-Tibet Plateau, with precipitation and NDVI also playing significant roles. The interaction of any two factors had a greater explanatory power than a single factor, with temperature’s influence being particularly significant when combined with other factors. (4) Across the Plateau, the four freeze-thaw parameters were significantly correlated with temperature changes but not with precipitation or NDVI. These parameters also showed clear gradient differences with altitudes: as altitude increased, freezing start times occurred earlier, end times were delayed, and freezing durations and freezing days were prolonged. [Conclusions] The results provide valuable data for further understanding the relationship between the soil freeze-thaw process and climate change. They also offer a theoretical basis for monitoring and predicting ecological and environmental changes in the Qinghai-Tibet Plateau.

[Objectives] Although the use of street view data to calculate the Green View Index (GVI) has emerged as a method for evaluating urban greening levels, systematic research on the spatiotemporal dynamics of GVI remains limited. [Methods] This study explores the spatiotemporal characteristics and influencing factors of urban GVI using street view big data, providing a new method for assessing urban street greening levels. This study proposes the GSENet semantic segmentation model for calculating and analyzing the GVI in Lanzhou's main urban area. The GSENet model incorporates a GSE-Block feature calibration module within its encoder, combining spatial and channel attention mechanisms. The decoder adopts an efficient self-attention module (Mix-transformer), which introduces a scaling factor and replaces the fully connected layer with a 1×1 convolution, combining the global modeling capability of Transformers with the local processing ability of convolution. Using the GSENet model, this study calculates the GVI of Lanzhou's main urban area based on Baidu Street View data and explores its spatiotemporal variation patterns through hotspot analysis, statistical analysis, and correlation analysis. [Results] The results reveal several key findings: (1) Utilizing ResNet50 as the backbone, the GSENet model achieves a Mean Intersection over Union (MIOU) of 74.7%, outperforming mainstream models such as PSPNet and DeepLabV3. The model demonstrates superior performance in identifying large-area categories such as vegetation and buildings, achieving an F1 score of 0.95. (2) Between 2019 and 2023, the average GVI increased by 2.3% compared to the period from 2014 to 2018. Notably, 70.9% of the sampled points showed a positive GVI trend, although only 8.4% experienced an increase greater than 10%. Anning District recorded the most substantial improvement, with a GVI rise of 3.5%, while Chengguan District saw the smallest growth, at only 1.9%. Spatial analysis identified that the central-western and northeastern parts of the study area experienced significant GVI increases, particularly in regions surrounding universities. In contrast, GVI declined notably in commercial centers and transportation hubs. (3) The influence of street view features and social factors on GVI changes exhibits spatiotemporal heterogeneity. Building density shows a negative correlation with GVI changes. The correlation between road width and GVI changes is relatively weak while the correlation between population density and GVI changes varies across different scales, with a stronger positive correlation at the street scale. [Conclusions] The experimental results highlight the effectiveness of this research in enhancing the perceived greening of urban streets. Furthermore, the findings provide valuable insights for urban planners aiming to optimize green space distribution and improve urban environments.

[Objectives] With the enhancement of spatial resolution, remote sensing images contain increasingly intricate information, encompassing a vast array of spatial and semantic features. The effective extraction and integration of these features play a pivotal role in semantic segmentation performance. However, most existing approaches focus solely on feature fusion improvements while neglecting the consistency between spatial and semantic features. Additionally, these methods often overlook the precise extraction of edge information, which significantly impacts segmentation accuracy. [Methods] This paper proposes a semantic segmentation model for high-resolution remote sensing images based on multi-scale deep supervision. First, separate feature extraction branches are designed for spatial and semantic features to fully exploit their respective information. Second, a spatial redundancy reduction residual module is incorporated into the spatial branch, integrating wavelet transformation and coordinate convolution to enhance spatial feature extraction and better capture edge details. Third, a residual attention Mamba module is added to the semantic branch to facilitate global-level semantic feature extraction. Finally, a multi-scale feature fusion mechanism is applied, utilizing a large-kernel grouped feature extraction module to progressively merge spatial, semantic, and deep-level features while suppressing irrelevant information and activating meaningful features. Additionally, a deep supervision mechanism is employed by introducing auxiliary supervision heads at each feature fusion stage to enhance training efficiency. [Results] Comparison and ablation experiments were conducted on the ISPRS Potsdam and Vaihingen datasets with random sampling and data augmentation, The experimental results demonstrate that the proposed algorithm achieves an average Intersection over Union (IoU) of 83.43% on ISPRS Potsdam and 86.49% on the augmented Vaihingen dataset. Compared to nine state-of-the-art methods, including CGGLNet and CMLFormer, the proposed approach improves the average IoU by at least 5.00% and 3.00%, respectively. [Conclusions] The results verify that the proposed algorithm effectively extracts and integrates spatial and semantic features, thereby enhancing the accuracy of semantic segmentation in remote sensing images.

[Objectives] Points of Interest (POIs) have dual characteristics as geospatial entities and carriers of cultural information, serving as the data foundation for analyzing and identifying regional cultural expressions and functional traits. Identifying and analyzing the types and characteristics of tourism cultural scenes along the Grand Canal is of great significance for achieving differentiated and sustainable cultural tourism development. [Methods] By integrating POI data with scene theory, spatial entities are associated with cultural values, and quantitative statistics are combined with qualitative configuration analysis. A tourism-cultural amenity database was established using 476,968 POI records, categorized into 6 major categories and 24 sub-categories. The Delphi method was employed to determine scores for each subcategory related to tourism amenity scenes, which were then used to calculate the performance scores of tourism cultural scenes. Descriptive statistical analysis, K-means clustering, and hierarchical clustering were applied to identify types of tourism-cultural scenes. The clustering results were visualized on maps. Meanwhile, the characteristics, formation mechanisms, and corresponding countermeasures of these scene types were further analyzed. [Results] (1) The Jiangsu section of the Grand Canal exhibits distinctive local tourism-cultural characteristics, with strong regional identity and attractiveness. However, significant disparities exist in tourism-cultural value orientations, particularly in subcategories such as locality, glamour, exhibitionism, utilitarianism, and charisma, highlighting the heterogeneous features of tourism-cultural scenes in this area. (2) Cluster analysis classified 34 counties (cities or districts) along the Jiangsu section into four types: local scenes (10 regions), utilitarian scenes (8 regions), comfortable scenes (13 regions), and charming scenes (3 regions). Discriminant analysis validated the reliability of these clustering results. Each of the four scene types exhibits distinct characteristics. (3) The types of tourism-cultural scenes are influenced by the combined effects of multiple factors (economic development, urbanization, population, fiscal policy, transportation, and tourism resources), which can be summarized into three configuration-based influence paths. [Conclusions] This study introduces scene theory into cultural tourism research based on POI big data, offering a novel approach to promoting regionally differentiated and sustainable development of cultural tourism.

[Significance] Space is not merely a physical place, but a productive arena of social relations. Social phenomena are inherently endowed with spatial attributes, making the spatial perspective a critical pathway for understanding complex social issues. With the deepening "spatial turn" in the social sciences and continuous advancements in Geographic Information Systems (GIS)—particularly in data acquisition, spatial analysis and modeling, and spatial visualization—GIS has become an essential tool for addressing social issues. However, disciplinary differences in theoretical paradigms, methodological logic, and scale cognition between geography and the social sciences constrain their deeper integration. Existing literature lacks a systematic synthesis of integration trends, underlying challenges, and empowerment pathways, necessitating a comprehensive clarification of fusion mechanisms, core obstacles, and emerging opportunities. [Progress] This paper identifies five key advantages of GIS in empowering social science research: expanding spatial analytical thinking, supporting spatiotemporal data, enhancing survey techniques, enriching representational forms, and strengthening analytical capabilities. We review representative GIS applications in economics, political science, and sociology. From dimensions such as spatial cognition, data capacity, methodological adoption, and research hotspots, we distill application characteristics across these disciplines, revealing both commonalities and differences. While all three disciplines recognize spatial effects, their theoretical orientations shape distinct technical approaches—economics emphasizes causal identification, political science focuses on geopolitical structures, and sociology prioritizes contextual representation. Through a three-dimensional analysis—data, methodology, and cognition—we examine three major challenges in addressing social issues: the mismatch between data and research questions, the difficulty of integrating methods with causal mechanisms, and the contextual misalignment of place and scale, which reflect deeper issues of data suitability, methodological coherence, and the validity of spatial reasoning. [Prospects] The advancement of artificial intelligence, especially large models, injects new methodological momentum into GIS-based spatial analysis and brings threefold opportunities for addressing social issues. First, large models are driving spatial analysis from correlation-based description toward transparent causal inference; Second, multi-source data fusion and the generation of "silicon-based samples" help overcome the limitations of traditional survey data. Third, an emerging "space-survey" integrated framework is constructing a "spatial cognitive infrastructure" to support social research. Future efforts should establish a synergistic "large model-spatial analysis" paradigm that integrates these three opportunities. By simultaneously addressing challenges of data matching, method integration, and contextual misalignment, this paradigm can elevate GIS from a supportive tool to a core engine for theory generation and mechanism interpretation. This transformation will enhance the scientific value and practical effectiveness of GIS and spatial analysis in addressing complex social issues, fostering a bidirectional interaction between methodological innovation and theoretical advancement.

[Background] Traditional methods, due to their static receptive field design, struggle to adapt to the significant scale differences among cars, pedestrians, and cyclists in urban autonomous driving scenarios. Moreover, cross-scale feature fusion often leads to hierarchical interference. [Methodology] To address the key challenge of cross-scale representation consistency in 3D object detection for multi-class, multi-scale objects in autonomous driving scenarios, this study proposes a novel method named VoxTNT. VoxTNT leverages an equalized receptive field and a local-global collaborative attention mechanism to enhance detection performance. At the local level, a PointSetFormer module is introduced, incorporating an Induced Set Attention Block (ISAB) to aggregate fine-grained geometric features from high-density point clouds through reduced cross-attention. This design overcomes the information loss typically associated with traditional voxel mean pooling. At the global level, a VoxelFormerFFN module is designed, which abstracts non-empty voxels into a super-point set and applies cross-voxel ISAB interactions to capture long-range contextual dependencies. This approach reduces the computational complexity of global feature learning from O(N²) to O(M²) (where M << N, M is the number of non-empty voxels), avoiding the high computational complexity associated with directly applying complex Transformers to raw point clouds. This dual-domain coupled architecture achieves a dynamic balance between local fine-grained perception and global semantic association, effectively mitigating modeling bias caused by fixed receptive fields and multi-scale fusion. [Results] Experiments demonstrate that the proposed method achieves a single-stage detection Average Precision (AP) of 59.56% for moderate-level pedestrian detection on the KITTI dataset, an improvement of approximately 12.4% over the SECOND baseline. For two-stage detection, it achieves a mean Average Precision (mAP) of 66.54%, outperforming the second-best method, BSAODet, which achieves 66.10%. Validation on the WOD dataset further confirms the method’s effectiveness, achieving 66.09% mAP, which outperforms the SECOND and PointPillars baselines by 7.7% and 8.5%, respectively. Ablation studies demonstrate that the proposed equalized local-global receptive field mechanism significantly improves detection accuracy for small objects. For example, on the KITTI dataset, full component ablation resulted in a 10.8% and 10.0% drop in AP for moderate-level pedestrian and cyclist detection, respectively, while maintaining stable performance for large-object detection. [Conclusions] This study presents a novel approach to tackling the challenges of multi-scale object detection in autonomous driving scenarios. Future work will focus on optimizing the model architecture to further enhance efficiency.

[Objectives] The scale, distribution, travel mode structure, and traffic flow of passenger travel demand are the results of spatial interactions within the human social economy across different locations. The complexity of the social and economic operation systems dictates that travel demand prediction must start from the urban system to address the technical challenges of current travel demand forecasting. This paper analyzes the systematic nature of urban transportation and proposes an integrated simulation technology framework that incorporates land, population, housing, and transportation. It also summarizes traffic demand simulation and prediction technology based on urban systems and develops China's first urban system travel demand forecasting technology platform. [Methods] This technology covers sub-modules such as transportation demand distribution, transportation mode share and path allocation, land use simulation, population and employment distribution, real estate price, and carbon emissions to reflect the complete urban system. It includes a series of sub-module variables, including generalized travel cost, location accessibility, real estate price, job-housing relationship coefficients, and land use mixing degrees, to reflect the interactions among subsystems and the time lag effect. Additionally, core algorithms of sub-modules are designed to achieve urban system simulation and prediction. Using Beijing as a case study, the application of this technology platform is demonstrated. A comparison between the actual and simulated values for 2020 shows that the accuracy of simulated results for travel demand, traffic congestion situation, land use, and population distribution is above 85%. [Results] Applying this platform to Beijing, the travel demand, traffic flow, congestion index, population distribution, and land use projections for 2030 were predicted. According to the forecast results, from 2020 to 2030, the total number of traffic trips in Beijing will show a generally stable and slowly declining trend, with strong centripetal characteristics spatially, and trips within each suburb will become more balanced. There will be a slight decrease in the proportion of public transportation travel, a slight reduction in residents' average travel time, and more severe congestion compared to 2020. The expansion of land for residential areas, roads and transportation facilities, green spaces and squares, and commercial services will be more obvious. Resident population will show steady fluctuations, with finger-like extensions along major transportation corridors. [Conclusion] Overall, this paper advances urban transportation theory, innovates urban transportation simulation forecasting methods, and provides new technical support for urban and rural planning and urban transportation planning.

[Significance] As the basic unit of a city, the carbon emission levels and accuracy of community-scale accounting directly impact the overall effectiveness of emission reduction in the construction industry. This paper reviews the main methods of carbon accounting, evaluates their advantages and disadvantages, and proposes a new approach to enhance the accuracy and comprehensiveness of community carbon accounting using digital twin technology. [Progress] This paper first introduces three traditional carbon accounting methods, namely the carbon emission factor method, the mass balance method, and the direct measurement method, and discusses their applications. It then identifies digital twin technologies suitable for community-scale carbon accounting, including Building Information Modeling (BIM), Geographic Information System (GIS), and the Internet of Things (IoT). The paper analyzes current development trends, including: (i) expanding the scope of carbon accounting to the community level using digital twin technology, (ii) strengthening the integration and interoperability of digital twin systems, and (iii) establishing a community carbon accounting framework grounded in digital twin technology. It further proposes integrating BIM, GIS, and IoT into a unified system based on the city information model to build a comprehensive community carbon emission platform. [Prospect] Looking ahead, the application of digital twin technology holds promise for enabling accurate carbon accounting, emission forecasting, reduction pathway planning, and performance evaluation for communities of varying scales and geographical contexts. Furthermore, with advances in AI technology, it is anticipated that city information models for community carbon accounting will increasingly integrate AI agents, leveraging the power of big data, large models, and high-performance computing, to create intelligent carbon accounting systems for the smart city era.

[Objectives] Revealing the structural characteristics of tourist flow networks is a prerequisite for achieving complementary advantages and coordinated development among attractions.[Methods] In this study, we employs methods such as travel chain extraction, social network analysis, and community detection to construct a research framework to analyze multi-scale tourist flow networks based on large-scale mobile phone data. The structural characteristics of the tourist flow network in Changsha are explored at microscopic, mesoscopic, and macroscopic scales.[Results] (1) Microscopic scale: The tourist flow network of Changsha shows a significant centralization trend, where a few core attractions such as the Yuelu Mountain and Orange Island have great influences on the whole network. Only 33% of attractions show structural hole efficiency and effectiveness above average, while their constraint is below average, indicating prominent structural holes and limited overall connectivity and efficiency. (2) Mesoscopic scale: The tourist flows of Changsha are highly concentrated, showing obvious spatial clustering characteristics and forming six tourism communities. There are usually two core attractions in each community to drive tourists to visit the surrounding attractions. In addition, the development of tourism communities is unbalanced, with a highly large community centered on Yuelu Mountain and Orange Island. (3) Macroscopic scale: The spatial distribution of the tourist flow network presents the characteristics of single-core strong concentration and overall dispersion, showing a multi-layer structure with the city center as the core and spreading outwards. The global efficiency of the network is only 0.367, with some marginal attractions having poor accessibility. The core attraction plays limited "trickle-down" effects on marginal attractions.