The measure of similarity of the building shape is crucial to the cartographic generalization process. Its research provides information on the contour of the building as a foundation for map analysis and the identification of spatial elements. Moreover, it is applied in many aspects, such as shape matching, shape retrieval, building simplification and building selection. With the development of neural networks, graph contrastive learning learns more discriminative representations by comparing positive samples from the same graph with negative samples from different graphs. Based on the advantages of the graph contrastive learning model,the study proposes a building shape similarity measurement model with the support of graph contrastive learning model, which aims to train a graph encoder to narrow the difference between positive samples and increase the gap between negative samples.The contrastive loss function and graph augmentation strategy are used to implement this operation. The following is the model's implementation process. Firstly, the vector building shapes are converted to the graph data structure and the point and edge features of the shapes are extracted.Secondly, two distinct views are generated as input to the encoder by applying various augmentation means, such as node dropping, edge removing, edge adding, and feature masking, to each graph. After that, the augmented graphs are then given to the graph encoder, which establishes each graph's feature encoding through the training process. Finally, the shape classification is achieved by a nonlinear classifier, and the extracted shape coding can be used to study shape similarities. The results indicated the shape classification accuracy of 96.7% using OSM shape data as training and testing samples. Furthermore, feature and node direction analysis, graph augmentation analysis, and parameter sensitivity analysis were carried out.The experimental results show that the classification accuracy rates of the HU moment method, Fourier method, and GCAE method are 22.9%, 44.4%, and 92.5%, respectively. Therefore, the method proposed in this paper outperforms traditional methods and deep learning in shape recognition capability.With a 95.7% shape classification accuracy, three areas of Hong Kong were chosen for shape matching and shape classification. And conducted shape matching tests on 9 typical shapes, finding that the similarity values of similar shapes were much greater than those of dissimilar shapes, consistent with visual perception.The graph contrastive learning model has effectively enhanced the recognition capability of complex shapes, providing technical support for applications such as cartographic generalization, spatial queries, shape matching, and shape retrieval.

[Significance] As a representative of new-quality productivity, the low-altitude economy is gradually emerging as a new engine for economic growth. This economy is based on the development and utilization of low-altitude airspace resources. While bringing development opportunities to geospatial information technology, it also poses entirely new challenges. [Progress and Analysis] In this paper, we introduce the division of low-altitude airspace resources and highlight typical drone application scenarios in the context of the low-altitude economy. Subsequently, we analyze the broad application prospects of geospatial information technology in key areas of the low-altitude economy, including the refined utilization of airspace resources, the construction of low-altitude environments, the planning, construction, and operation of new air traffic infrastructure, as well as the safe and efficient operation and regulatory oversight of drones. We emphasize that the geospatial information industry will benefit from development opportunities such as the integration and innovation of emerging scientific and technological advancements, growing market demand, policy support, industrial guidance, and industrial upgrading and transformation. [Prospect] Finally, we briefly address the challenges geospatial information technology must overcome to meet the development needs of the low-altitude economy. These include advancements in spatio-temporal dimension elevation, map and location-based services, high-frequency and rapid data acquisition systems, all-time and all-domain capabilities, and ubiquitous intelligent technologies. These areas will also serve as future directions for development and breakthroughs in geospatial information technology.

[Objectives] With the application of knowledge graph techniques in the field of Geographical Information Science (GIS), the Geographical Knowledge Graph (GeoKG) has become a key research direction. GeoKGs often lack sufficient geographic knowledge coverage, which can negatively impact downstream applications. Therefore, reasoning techniques are essential for GeoKG to complete missing knowledge, identify inconsistencies, and predict trends in geographic phenomena. Unlike reasoning techniques applied to general knowledge graphs, reasoning on GeoKGs must handle the unique and complex spatial and temporal characteristics of geographic phenomena. This paper comprehensively introduces and summarizes recent advances in GeoKG reasoning. [Analysis] First, it introduces the relevant concepts and problem definitions of GeoKG reasoning. Second, it analyzes the two core tasks of GeoKG reasoning: knowledge completion and prediction. The reasoning model for knowledge completion primarily fills gaps in the graph to ensure knowledge integrity, while the reasoning model for prediction aims to forecast future trends based on existing geographic data. These two models are optimized for different application scenarios, with different focuses in processing geographic data. [Prospect] Finally, the paper explores future development trends in GeoKG reasoning, highlighting areas such as processing complex relationships in spatiotemporal data, reasoning with multi-scale geographic knowledge, fusing multimodal data, and enhancing the interpretability and intelligence of reasoning models. Additionally, the integration of GeoKGs with large-scale pre-trained models is expected to become a key area of focus.

[Objectives] The geographic system is an integrated framework encompassing natural and human phenomena and their interrelationships on the Earth's surface. While Geographic Information Systems (GIS) can digitally process these geographic elements, they face challenges in addressing rapidly changing geographic contexts with complex 3D structures. This is primarily due to the lack of bi-directional interactions between physical and informational spaces, as well as their reliance on predefined rules and historical data. In this paper, we propose the concept of a “Geographic Intelligent Agent” as an advanced form of GIS, which integrates embodied intelligence, self-supervised learning, and multimodal language modeling to improve environmental perception, spatial understanding, and autonomous decision-making. [Methods] The architecture of the geographic intelligent agent consists of three core components: multimodal perception, an intelligent hub, and an action manipulation module. These components collectively acquire comprehensive environmental information through sensor networks, perform complex situatio reasoning using knowledge graphs and generative models, and enable real-time control and multilevel planning of the physical environment. To adapt to differences between virtual and real environments, the geographic intelligent agent is tested using the earth simulator and a test field platform, equipping it with stronger autonomous capabilities in complex and dynamic geographic contexts. [Results] This paper also demonstrates the implementation of geographic intelligent agent in spatial intelligence applications using the virtual digital human “EarthSage” as an example. [Conclusion] As a prototype of the geographic intelligent agent, "EarthSage" integrates modules such as the spatiotemporal Knowledge Ggraph (GeoKG) and a Cognitive Map Generation Model (GeoGPT), assisting users in obtaining intelligent spatial decision-making support in fields such as emergency management, urban planning, and ecological monitoring. This work exemplifies the transformation of GIS from a traditional information processing tool to an autonomous spatial intelligent system, marking a significant advancement in the field.

[Objectives] Forecasting is a key research direction in Geospatial Artificial Intelligence (GeoAI), playing a central role in integrating surveying, mapping, geographic information technologies, and artificial intelligence. It drives intelligent innovation and facilitates the application of spatial intelligence technologies across diverse real-world scenarios. [Progress] This study reviews the historical development of GeoAI-driven spatiotemporal forecasting, providing an overview of prediction models based on statistical learning, deep learning, and generative large models. In addition, it explores the mechanisms of spatiotemporal dependence embedding within these models and decouples general computational operators used for modeling temporal, spatial, and spatiotemporal relationships. [Prospect] The challenges faced by intelligent prediction models include sparse labeled data, lack of explainability, limited generalizability, insufficient model compression and lightweight design, and low model reliability. Furthermore, we discuss and propose four future trends and research directions for advancing geospatial intelligent prediction technologies: a generalized spatial intelligent prediction platform incorporating multiple operators, generative prediction models integrating multimodal knowledge, prior-guided deep learning-based intelligent prediction models, and the expansion of geospatial intelligent prediction models into deep predictive applications for Earth system analysis.

[Objectives] To systematically review recent advancements in text-to-image generation technology driven by large-scale AI models and explore its potential applications in urban and rural planning. [Discussion] This study provides a comprehensive review of the development of text-to-image generation technology from the perspectives of training datasets, model architectures, and evaluation methods, highlighting the key factors contributing to its success. While this technology has achieved remarkable progress in general computer science, its application in urban and rural planning remains constrained by several critical challenges. These include the lack of high-quality domain-specific data, limited controllability and reliability of generated content, and the absence of constraints informed by geoscience expertise. To address these challenges, this paper proposes several research strategies, including domain-specific data augmentation techniques, text-to-image generation models enhanced with spatial information through instruction-based extensions, and locally editable models guided by induced layouts. Furthermore, through multiple case studies, the paper demonstrates the value and potential of text-to-image generation technology in facilitating innovative practices in urban and rural planning and design. [Prospect] With continued technological advancements and interdisciplinary integration, text-to-image generation technology holds promise as a significant driver of innovation in urban and rural planning and design. It is expected to support more efficient and intelligent design practices, paving the way for groundbreaking applications in this field.

Urban green spaces are critical components of urban ecosystems, playing an irreplaceable role in improving the ecological environment and enhancing quality of life. High-precision identification of urban green spaces is fundamental for urban renewal and optimizing green infrastructure. However, research on the identification and spatial heterogeneity of green spaces in megacities remains relatively limited. This study, taking Xi'an as an example, integrates urban street view images and GF-2 (Gaofen-2) satellite imagery, employing methods such as ISODATA classification, K-Means classification, and convolutional neural networks to achieve multi-dimensional, downscaled, and high-precision identification and analysis of green spaces. The results indicate the following: (1) The K-Means classification method demonstrates significantly higher accuracy (84.5%) compared to the ISODATA classification method (62.4%) and more accurately maps the spatial characteristics and heterogeneity patterns of green spaces. The green space coverage identified by the K-Means method is 0.277 0, which is lower than the 0.360 7 identified by ISODATA. (2) The average Green View Index (GVI) of streets in Xi'an's main urban area is 0.156 0, indicating a generally good level of street greening. However, there is notable polarization across different roads, with 30% of sampling points having a GVI below 0.080 0. Overall, the GVI of higher-grade roads is greater than that of lower-grade roads, following the trend: primary roads > secondary roads > trunk roads > tertiary roads. (3) There is a positive correlation between the GVI of streets and the vegetation coverage in their surrounding areas in Xi'an's main urban area. However, this correlation weakens in certain road sections, reflecting differences between vertical cross-sections and overhead views of the streets. Combining these perspectives provides a more accurate assessment and quantification of urban green spaces. This study provides a reference for green space planning, green infrastructure construction, and smart management in Xi'an, as well as technical guidance for high-precision identification and spatial analysis of urban green spaces in other cities.

China is one of the countries most severely affected by geological disasters. Researching high-precision and highly reliable methods for monitoring and predicting landslide deformation holds practical significance for disaster prevention and mitigation efforts. Using the massive Outang landslide in the Three Gorges Reservoir Area as a case study, this paper addresses the issue of the atmospheric interference in extracting landslide deformation using time-series InSAR technology. To correct for atmospheric effects, the GACOS model is introduced and validated against GNSS observation data. To address the often-overlooked temporal-spatial analysis before landslide deformation prediction, the Moran index and Hurst index are calculated to analyze the spatiotemporal characteristics of landslide deformation. Recognizing that landslide deformation is influenced not only by historical deformation but also by various external factors, this paper proposes coupling landslide influencing factors with deformation data for prediction. A Long Short-Term Memory (LSTM) model, optimized by Variational Mode Decomposition (VMD) and the Sparrow Search Algorithm (SSA), is employed for the prediction. By decomposing landslide displacement data into trend, periodic, and random components using VMD, the LSTM network structure is constructed. SSA is used to determine the optimal number of hidden units, maximum training periods, and the initial learning rate of the LSTM model. Additionally, methods such as data normalization, regularization, and model evaluation are employed to enhance the performance and stability of the LSTM model. Finally, the model is trained using the influencing factors and decomposed displacement data to predict landslide deformation. The results indicate that: (1) From January 2021 to June 2023, the maximum and minimum deformation rates of the Outang landslide were -72.75 mm/a and 50.74 mm/a, respectively; (2) The deformation in the study area exhibits positive spatial autocorrelation, with the landslide in the settlement area showing a persistent trend; (3) The prediction error of the LSTM model optimized by VMD and SSA is only 0.37 mm, representing an 11.004% accuracy improvement compared to the standard LSTM model. Based on time-series InSAR technology and spatiotemporal analysis results, this paper constructs a high-precision prediction model for landslide deformation, incorporating multiple influencing factors. This model provides a valuable reference for the prevention and control of landslide disasters.

[Significance] Data resources have become pivotal in modern production, evolving in close synergy with advancements in artificial intelligence (AI) technologies, which continuously cultivate new, high-quality productive forces. Remote sensing data intelligence has naturally emerged as a result of the rapid expansion of remote sensing big data and AI. This integration significantly enhances the efficiency and accuracy of remote sensing data processing while bolstering the ability to address emergencies and adapt to complex environmental changes. Remote sensing data intelligence represents a transformative approach, leveraging state-of-the-art technological advancements and redefining traditional paradigms of remote sensing information engineering and its applications. [Analysis] This paper delves into the technological background and foundations that have facilitated the emergence of remote sensing data intelligence. The rapid development of technology has provided robust support for remote sensing data intelligence, primarily in three areas: the advent of the big data era in remote sensing, significant advancements in remote sensing data processing capabilities, and the flourishing research on remote sensing large models. Furthermore, a comprehensive technical framework is proposed, outlining the critical elements and methodologies required for implementing remote sensing data intelligence effectively. To demonstrate the practical applications of remote sensing data intelligence, the paper presents a case study on applying these techniques to extract ultra-high-resolution centralized and distributed photovoltaic information in China. [Results] By integrating large models with remote sensing data, the study demonstrates how remote sensing data intelligence enables precise identification and mapping of centralized and distributed photovoltaic installations, offering valuable insights for energy management and planning. The effectiveness of remote sensing data intelligence in addressing challenges associated with large-scale photovoltaic extraction underscores its potential for application in critical fields. [Prospect] Finally, the paper provides an outlook on areas requiring further study in remote sensing data intelligence. It emphasizes that high-quality data serves as the foundation for remote sensing data intelligence and highlights the importance of constructing AI-ready knowledge bases and recognizing the value of small datasets. Developing targeted and efficient algorithms is essential for achieving remote sensing intelligence, making the advancement of practical data intelligence methods an urgent research priority. Furthermore, promoting multi-level services for remote sensing data, information, and knowledge through data intelligence should be prioritized. This research provides a comprehensive technical framework and forward-looking insights for remote sensing data intelligence, offering valuable references for further exploration and implementation in critical fields.

This paper addresses the challenge of discovering spatio-temporally associated vehicles involved in crimes using Automatic Number Plate Recognition (ANPR) data, which is a crucial resource in public security work for obtaining vehicle trajectories. The significance of identifying associated vehicles in the context of group-crime prevention and control is emphasized. Practical experiences reveal that criminal groups often adopt subjective strategies to avoid suspicion, leading to unique spatio-temporal association patterns such as intentional long-distance following, which differ from traditional accompanying relationships and are difficult to detect with existing methods. Oriented to the actual needs of public security work, from the perspective of group-crime, to tackle this issue, the paper first analyzes the travel patterns of criminal group vehicles and categorizes them into three main spatio-temporal association modes: close-following mode, intentional long-distance following mode, and alternative-route mode. These modes reflect the different strategies used by criminals to avoid detection, ranging from maintaining close proximity to the peer vehicle to deliberately choosing different routes. Based on these patterns, the paper develops a data model using ANPR data. The study introduces spatio-temporal constraint parameters to better capture the association relationships between vehicles. These parameters include the monitoring point time constraint (Δt_i), point accompanying number (Num_Wx), continuous point accompanying number (Con_Num_Wx), intermittent accompanying distance (d), and accompanying duration (δt).The proposed method for discovering spatio-temporally associated vehicles leverages these parameters to identify potential criminal associations. The methodology involves preprocessing ANPR data to obtain vehicle trajectories, extracting candidate accompanying vehicle sets, calculating spatio-temporal constraint parameters for each candidate vehicle, and setting thresholds for these parameters to discover associated vehicles containing different spatio-temporal patterns. Finally, taking city B as an example, the relevant ANPR data of group-crimes vehicles are used for test and analysis, and the spatio-temporal constraint parameter thresholds are quantitatively evaluated based on the historical data of group-crime cases, based on which the spatio-temporal correlation vehicle analysis of a typical case is conducted, and when comparing this paper's method with the two methods of frequent sequence mining and calculating the concomitant probability, the effectiveness of this paper's method can reach up to 87.59% on average, which is better than the the comparison methods. The results show that the method can effectively identify vehicles engaged in long-distance following and alternative-route strategies, which are often missed by traditional methods. As a result, it is able to quickly target those involved in the crime and further narrow the scope of investigation. In conclusion, the paper presents a comprehensive method for discovering spatio-temporally associated vehicles using ANPR data, significantly enhancing the ability to detect vehicles with complex association patterns. This method not only broadens the application scope of spatio-temporal association discovery but also provides new insights and technical support for public security departments in addressing group-crimes.

In high-resolution remote sensing images, challenges such as blurred visual features of objects and different spectra for the same object arise. Segmenting similar ground objects and shaded ground objects in a single mode is difficult. Therefore, this paper proposes a remote sensing image segmentation model based on multi-modal feature extraction and hierarchical perception. The proposed model introduces a multi-modal feature extraction module to capture feature information from different modalities. Using the complementary information of IRRG and DSM, accurate pixel positions in the feature map are obtained, improving semantic segmentation of high-resolution remote sensing images. The coordinate attention mechanism fully fuses the features from different modalities to address issues of blurred visual features and different object spectra during image segmentation. The abstract feature extraction module uses MobileNetV3 with dual-path bottleneck blocks as the backbone network, reducing the number of parameters while maintaining model accuracy. The hierarchical perception network is introduced to extract deep abstract features, and the attention mechanism is improved by embedding scene perception of pixels. Leveraging the inherent spatial correlation of ground objects in remote sensing images, efficient and accurate class-level context modeling is achieved, minimizing excessive background noise interference and significantly improving the semantic segmentation performance. In the decoding module, the model uses multi-scale aggregation dual fusion for feature recovery, strengthening the connection between the encoder and the decoder. This combines low-level features with high-level abstract semantic features, enabling effective spatial and detailed feature fusion. Progressive upsampling is used for feature recovery, resolving the issue of blurred visual features and improving segmentation accuracy. Based on high-resolution remote sensing images from the ISPRS Vaihingen and Potsdam datasets, the experimental results demonstrate that MFEHPNet outperforms a series of comparison models, including C3Net, AMM-FuseNet, MMFNet, CMFet, CIMFNet, and EDGNet, across various performance indicators. In the ISPRS Vaihingen and Potsdam datasets, MFEHPNet achieves an overall accuracy of 92.21% and 93.45%, an average intersection ratio of 83.24 % and 83.94 %, and a Kappa coefficient of 0.85. The frequency-weighted intersection ratio is 89.24 % and 90.12%, respectively, significantly improving the semantic segmentation performance of remote sensing images and effectively addressing the issues of blurred feature boundaries and different spectra during segmentation.

Road selection has always been a significant research aspect of cartographic generalization, which is of great significance for spatial data linkage updating and multi-scale representation. The existing selection methods mainly include those based on stroke, semantic information, graph theory, road density, and artificial intelligence, but they only consider the features of a single level selection unit. Therefore, this paper proposes an automatic road selection method that integrates road segment and stroke features. Firstly, the road segment and stroke are used as basic units to construct a dual graph representing the spatial structure of the road network. Based on this, feature calculations are performed: length, degree, closeness centrality, betweenness centrality, and hierarchy are considered as road segment features, while length, the number of containing road segments, and the number of connections of road segment under the same stroke are regarded as stroke features. These stroke features are then integrated into the corresponding road segment unit. The obtained feature matrix is input into the GraphSAGE model for learning, which outputs the classification result of road segment. Finally, a method that increases the minimum number of nodes while considering stroke coherence is utilized to maintain the connectivity of the road network, thereby completing the road selection. Experiments were conducted using 1:250 000 and 1:500 000 scale road network data from Zhengzhou, Henan Province. The results indicate that the proposed method effectively integrates the features of road segments and strokes, overcoming the limitations of using a single road segment or stroke as the selection unit. Compared to the method in reference 17 and the comparative methods that use a road segment or stroke as the selection unit, the model's prediction accuracy improved by 6.36%, 7.36% and 3.13%, respectively. The results processed by the proposed connectivity preservation algorithm were more in line with the cognitive rules of road selection and could further improve the accuracy of selection. After connectivity processing, the proposed method improved the consistent road length by 125.45 km and 110.438 km, and the proportion of consistent road numbers by 8.72% and 20.43%, respectively, while better maintaining the overall and local key structures and density distribution of the road network. Compared with existing road selection methods, this method can better utilize multi-level road features to improve the effectiveness of road selection, providing a new approach for subsequent research in areas such as cartographic generalization and linkage updating.

[Significance] As a comprehensive observation of natural resource development and utilization, spatio-temporal big data on natural resources contains valuable knowledge about resource distribution, spatio-temporal process evolution, and interrelationships. [Progress] This paper examines spatio-temporal big data mining and knowledge services for natural resources, highlighting key data mining techniques and their critical applications in knowledge services. First, it introduces the core concepts, technical frameworks, and methodological processes of spatio-temporal clustering analysis, association mining, anomaly detection, predictive modeling, and geographic risk assessment, along with their applications in natural resource management and land-use decision-making. Second, a four-tier natural resource spatio-temporal knowledge service system is proposed, encompassing descriptive, diagnostic, predictive, and decision-making knowledge services, which provide essential support for applications such as resource status monitoring, land-use regulation, and disaster prevention and mitigation. Finally, the paper indicates that current natural resource management is transitioning from data aggregation and analysis to knowledge-driven intelligent services, forming an emerging research and application paradigm of big data, big analysis, big knowledge, and big services. [Prospect] Future efforts will focus on advancing collaborative data and knowledge mining technologies, addressing the standardization challenges in spatio-temporal knowledge bases and services, and exploring the potential of cutting-edge technologies such as generative large models in the natural resource domain to drive the information and intelligent transformation of natural resource management.

[Objectives] With the development of deep learning technology, the ability to monitor changes in natural resource elements using remote sensing images has significantly improved. While deep learning change detection models excel at extracting low-level semantic information from remote sensing images, they face challenges in distinguishing land-use type changes from non-land-use type changes, such as crop rotation, natural fluctuations in water levels, and forest degradation. To ensure a high recall rate in change detection, these models often generate a large number of false positive change polygons, requiring substantial manual effort to eliminate these false alarms. [Methods] To address this issue, this paper proposes a natural resource element change polygon purification algorithm driven by remote sensing spatiotemporal knowledge graph. The algorithm aims to minimize the false positive rate while maintaining a high recall rate, thereby improving the efficiency of natural resource element change monitoring. To support the intelligent construction and effective reasoning of the spatiotemporal knowledge graph, this study designed a remote sensing spatiotemporal knowledge graph ontology model taking into account spatiotemporal characteristics and developed a GraphGIS toolkit that integrates graph database storage and computation. This paper also introduces a vector knowledge extraction method based on the native spatial analysis of the GraphGIS graph database, a remote sensing image knowledge extraction method based on efficient fine-tuning of the SkySense visual large model, and a polygon purification knowledge extraction method based on the SeqGPT large language model. Under the constraints of the spatiotemporal ontology model, vector, image, and text knowledge converge to form a remote sensing spatiotemporal knowledge graph. Inspired by the manual operation methods for change polygon purification, this paper developed an automatic purification method of change polygons based on first-order logical reasoning within the knowledge graph. To improve the concurrent processing and human-computer interaction, this paper developed a remote sensing spatiotemporal knowledge graph management and service system. [Results] For the task of purifying natural resource element change polygons in Guangdong Province from March to June 2024, the proposed method achieved a true-preserved rate of 95.37% and a false-removed rate of 21.82%. [Conclusions] The intelligent purification algorithm and system for natural resource element change polygons proposed in this study effectively reduce false positives while preserving real change polygons. This approach significantly enhances the efficiency of natural resource element change monitoring.

Addressing the issue of Ultra-Wideband (UWB) signal obstruction by obstacles in indoor environments, which leads to Non Line of Sight (NLOS) errors, this paper presents a fusion positioning method based on Light Detection And Ranging (LiDAR) point cloud for identifying UWB NLOS. This method utilizes LiDAR point cloud information to assist in the identification of UWB NLOS and leverages UWB Line of Sight (LOS) ranging values to eliminate cumulative errors in the LiDAR Simultaneous Localization and Mapping (SLAM) positioning process, thereby enhancing the accuracy and robustness of indoor fusion positioning. Initially, the method processes the LiDAR point cloud using an octree, constructs the ranging direction based on the location information of UWB base stations, and extracts the relevant point cloud data in the ranging direction from the LiDAR point cloud. Subsequently, the 3D Alpha Shape algorithm is employed to extract contours of obstacles that may hinder UWB signal propagation within the extracted point clouds. Furthermore, by analyzing the spatial relationship between the extracted obstacle contours and the UWB ranging direction, the presence of NLOS conditions in UWB signals is effectively determined. Finally, NLOS ranging values identified during the UWB ranging process are excluded, and a tight integration approach is used with an Extended Kalman Filter (EKF) to fuse UWB LOS ranging values with LiDAR SLAM positioning data, eliminating cumulative errors in the LiDAR SLAM positioning outcomes, thereby enhancing the precision and robustness of fusion positioning. Experimental results demonstrate that this method significantly improves positioning accuracy in indoor environments, increasing the positioning accuracy by 96.13% compared to the positioning method that tightly combines the original UWB ranging values with LiDAR SLAM using EKF, with a positioning error of 0.067 m, achieving sub-meter level indoor positioning accuracy.

[Objectives] The outdoor pedestrian navigation road network is a vital component of maps and a crucial basis for outdoor activity route planning and navigation. It plays a significant role in promoting outdoor travel development and ensuring safety management. However, existing research on road network generation mainly focuses on the construction of urban vehicular navigation networks, with relatively less emphasis on hiking navigation road networks in complex outdoor environments. Moreover, existing methods primarily emphasize the extraction of two-dimensional geometric information of roads, while the reconstruction of real three-dimensional geometric and topological structures remains underdeveloped. [Methods] To address these limitations, this study proposes a method for constructing the three-dimensional outdoor pedestrian navigation road network maps using crowdsourced trajectory data. This approach leverages a road network generation layer and an elevation extraction layer to extract the two-dimensional structure and three-dimensional elevation information of the road network. In the road network generation layer, a trajectory density stratification strategy is adopted to construct the two-dimensional vector road network. In the elevation extraction layer, elevation estimation and optimization are performed to generate an elevation grid raster map, which is then matched with the two-dimensional road network to produce the three-dimensional hiking navigation road network. [Results] To demonstrate the effectiveness of the proposed approach, experiments were conducted using 1 170 outdoor trajectories collected in 2021 from Yuelu Mountain Scenic Area in Changsha through an online outdoor website. The constructed outdoor three-dimensional hiking road network map achieved an average positional offset of 4.201 meters in two-dimensional space and an average elevation estimation error of 7.656 meters. The results demonstrate that the proposed method effectively handles outdoor trajectory data with high noise and varied trajectory density distribution differences, generating high-quality three-dimensional hiking road network maps. [Conclusions] Compared to traditional outdoor two-dimensional road networks, the three-dimensional navigation road networks constructed this study provide more comprehensive and accurate map information, facilitating improved pedestrian path planning and navigation services in complex outdoor environments.

[Objectives] Ship detection using Synthetic Aperture Radar (SAR) images has gained widespread recognition and application across various fields, including marine search and rescue, port reconnaissance, and territorial sea defense. Nevertheless, with the rapid advancement of on-orbit intelligent processing technologies, higher demands have emerged for real-time detection of ship targets in spaceborne SAR images. [Methods] To address challenges such as the diverse scales of ship targets in current SAR images, the complex background of shore-based vessels, and the limited hardware resources of various remote sensing platforms, this paper presents a lightweight SAR image ship detection model, LWM-YOLO. Firstly, we propose a Lightweight Backbone Network (LWCA) designed specifically for SAR image processing. The LWCA integrates an optimized backbone network with an attention mechanism, effectively reducing the model's complexity and parameter size while maintaining high performance and lowering computational demands. Secondly, to tackle the issue of diverse target scales in SAR images, we have constructed a lightweight feature fusion module, termed LGS-FPN. This module enhances the extraction of detailed information on ship targets in SAR images by efficiently fusing features from different scales, improving detection performance for ship targets of various sizes. Furthermore, the module minimizes computational complexity, ensuring that the model can operate smoothly without significant resource consumption. In addition to addressing the scale issue, we have also focused on optimizing localization accuracy. We introduce a detection architecture based on the MPD-Head, which leverages the strengths of the MPD-Head to improve detection performance for small ship targets in complex environments. Finally, we validate the proposed algorithm through comparative experiments with mainstream methods on the LS-SSDD and SSDD ship detection datasets. [Results] The results demonstrate that our algorithm achieved mean Average Precision (mAP) values of 74.7% and 97.3% on the respective datasets, representing improvements of 1.5 and 1.0 percentage points over the baseline model. Additionally, the parameter size of our model was reduced to 36% of the baseline model, and computational complexity decreased to 80%. [Conclusions] Compared to other mainstream algorithms, the proposed method demonstrates not only higher accuracy but also significant advantages in detection speed. These findings can provide robust support for intelligent target detection, space-based in-orbit applications, and related fields.

The monitoring of coastal natural and constructed wetlands is of great importance to the protection of coastal water environment and natural resources. In practice, dynamic ranges of coastal natural and constructed wetlands can be monitored by using satellite image synthesis to represent the processes of wetlands being affected by dynamic changes in tidal levels. They can also be achieved by developing remote sensing indexes that are effective in characterizing natural wetlands exhibiting certain spectrum characteristics and by using advanced numerical algorithms that are capable of segmenting constructed wetlands showing some distinct boundaries. Based on multi-source remote sensing and ground data, this paper has presented a novel method to extract natural and constructed wetlands by combining unsupervised and supervised classification methods. Specifically, based on the Landsat images on the Google Earth Engine (GEE) cloud platform, the Inundated Mangrove Forest Index (IMFI) and related indexes are derived as the characteristic variables for the Random Forest (RF) algorithm; the slope obtained from elevation is also used to reduce the misclassification of mangrove forests since the majority of mangrove forests tends to be distributed in areas with gentle topography; and furthermore the K-means clustering algorithm is used to automatically extract wetlands without morphological processing. Through the case study of the Guangdong-Hong Kong-Macao Greater Bay Area (GBA), the metrics of Producer's Accuracy (PA), User's Accuracy (UA), Overall Accuracy (OA) and Kappa coefficient are used to verify the effectiveness of the method through applications to long-term satellite images. The results show that: (1) Compared with other indexes, the IMFI can more effectively identify water, aquaculture ponds and tidal flats; (2) By combining the K-means clustering algorithm with the IMFI, the distinctions between wetland classes and between wetlands and other ground objects can be enhanced by segmenting constructed wetland and clustering tidal flats; (3) The average OA of the extraction method in the classification of coastal areas in the GBA over the past 34 years is 89.23% and the average Kappa coefficient is 0.873 1. The method can effectively solve the problems of misclassification and omission between wetland classes and between wetlands and water, with the accuracy slightly fluctuating over time. In the meantime, this method circumvents the influences of subjective threshold selection and is not limited to local and regional spatial scales. Taken together, the proposed method can provide technical supports for high-precision dynamic monitoring and early warning for the protection of coastal natural and constructed wetlands.

Bicycle sharing offers the advantages of resource sharing, environmental sustainability, and low carbon emissions, and has been widely applied in recent years. Trajectory prediction of shared bicycles is crucial for the scientific and efficient planning of infrastructure. However, existing trajectory prediction mechanisms are relatively limited, and the influencing factors are often too narrow, leading to low prediction accuracy. This restricts the further growth and development of bicycle-sharing systems. Therefore, accurately predicting the travel trajectories of shared bicycles is essential for optimizing bicycle lanes, efficiently deploying and scheduling bicycle resources, improving road design, and addressing the "last mile" challenge in urban transportation. To better understand the spatio-temporal characteristics of shared bicycle travel and the influence of natural and weather factors on travel trajectories, and to improve prediction accuracy, this paper developes a trajectory prediction model that integrates natural and weather factors with a spatio-temporal attention residual bi-directional network (NWSTAR-BiLSTM). This study uses shared bicycle order and trajectory data from Xiamen, provided by the government’s open data platform, to analyze the spatio-temporal distribution of travel and the impact of natural and weather factors on trajectories. The model incorporates variables such as temperature, weather conditions, wind speed, and air quality, dividing the shared bicycle trajectory data into time series based on periodicity. Using an attention mechanism and residual learning, the prediction results are adjusted according to weather factors. The dataset is divided into training, testing, and validation sets in a 7:2:1 ratio, and the model undergoes training, parameter adjustment, and comparative validation. Experimental results show that the trajectory prediction accuracy of the NSTARWSTAR-BiLSTM model exceeds that of traditional models, such as LSTM, BiLSTM, CNN, Att-LSTM, and self-built comparative models (e.g., STAR-BiLSTM without natural and weather factors, WSTR-BiLSTM without the attention mechanism, and WSTA-BiLSTM without the residual network). The NSTARWSTAR-BiLSTM model not only inherits the strengths of traditional residual network models but also innovatively integrates the attention mechanism with multiple natural and weather factors, enhancing trajectory prediction accuracy. The model also demonstrates strong intelligent learning capabilities, with prediction accuracy improving as feedback increases.

[Significance] The extraction of Cartographic-Level Vector Elements (CLVE) is a critical prerequisite for the direct application of remote sensing image intelligent interpretation in real-world scenarios. [Analysis] In recent years, the continuous rapid advancement of remote sensing observation technology has provided a rich data foundation for fields such as natural resource surveying, monitoring, and public surveying and mapping data production. However, due to the limitations of intelligent interpretation algorithms, obtaining the necessary vector elements data for operational scenarios still heavily relies on manual visual interpretation and human-computer interactive post-processing. Although significant progress has been made in remote sensing image interpretation using deep learning techniques, producing vector data that are directly usable in operational scenarios remains a major challenge. [Progress] This paper, based on the actual data needs of operational scenarios such as public surveying and mapping data production, conducts an in-depth analysis of the rule constraints for different vector elements in remote sensing image interpretation across a wide range of operational contexts. It preliminarily defines "cartographic-level vector elements" as vector element data that complies with certain cartographic standard constraints at a specific scale. Centered on this definition, the content of the rule set for CLVE is summarized and analyzed from nine dimensions, including vector types, object shapes, boundary positioning, area, length, width, angle size, topological constraints, and adjacency constraints. Evaluation methods for CLVE are then outlined in four aspects: class attributes, positional accuracy, topological accuracy, and rationality of generalization and compromise. Subsequently, through literature collection and statistical analysis, it was observed that research on deep learning-based vector extraction, while still in its early stages, has shown a rapid upward trend year by year, indicating increasing attention in the field. The paper then systematically reviews three major methodological frameworks for deep learning-based vector extraction: semantic segmentation & post-processing, iterative methods, and parallel methods. A detailed analysis is provided on their basic principles, characteristics and accuracy of vector extraction, flexibility, and computational efficiency, highlighting their respective strengths, weaknesses, and differences. The paper also summarizes the current limitations of remote sensing intelligent interpretation methods aimed at CLVE in terms of cartographic-level interpretation capabilities, rule coupling, and remote sensing interpretability. [Prospect]Finally, future research directions for intelligent interpretation of CLVE are explored from several perspectives, including the construction of broad and open cartographic-level rule sets, the development and sharing of CLVE datasets, the advancement of multi-element CLVE extraction frameworks, and the exploration of the potential of multimodal coupled semantic rules.

[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.

Exploring the fairness of medical resource allocation and ensuring equal access for residents, especially vulnerable groups, to medical services is of great significance in promoting the construction of healthy communities, while consolidating and expanding the effective link between poverty alleviation achievements and rural revitalization efforts. This paper focuses on the population distribution characteristics and the differentiated needs of vulnerable groups. From the three dimensions of regional equity, spatial equity, and social equity, an evaluation framework and method are constructed to consider the fairness of medical resource allocation for vulnerable groups. Using Shangluo City in the Qinba Mountain Area as the experimental area, this study comprehensively evaluates the supply of medical resources at different levels, the relationship between supply and demand, and the difference between supply and demand among vulnerable groups. The results show: (1) The evaluation framework and method proposed in this paper, by taking into account the needs and preferences of vulnerable groups, promote the equitable distribution of medical resources to benefit these groups, while maximizing satisfaction of individual medical needs. This provides a theoretical framework for scientifically evaluating and implementing medical resource allocation for vulnerable groups. (2) Based on this theoretical framework, the evaluation method accurately measures the match between medical resource supply and the needs of various types of users, particularly vulnerable groups, from a supply and demand perspective. This makes the evaluation results more aligned with real-world allocation needs, ensuring efficient and rational resource distribution. (3) The experimental results in Shangluo City show that, in terms of regional equity, the coverage ratio of village- and township-level medical resources within a 30-minute rural health radius is over 95 %. In terms of spatial equity, there is significant imbalance between urban and rural medical resources, with the supply-demand relationship mostly in a 'double low' state. In terms of social equity, the agricultural population faces disadvantages in accessing medical resources at all levels, and the Gini coefficient for the four groups is higher than 0.5. Overall, compared to fairness issues among different groups, the general balance and equity of medical resources in Shangluo City requires more attention. The findings suggest that the evaluation framework and method developed in this paper can accurately access the fairness of medical resource allocation and help identify critical issues, providing a reference for rational medical resource distribution in cities.

The identification of urban black and odorous water bodies plays an important role in water environment regulation and urban water ecological protection. Small black and odorous water bodies are difficult to identify using traditional remote sensing technology due to their small scale, high dispersion, poor mobility, and complex pollution sources. To improve the recognition accuracy of these water bodies, this paper presents an integrated remote sensing method based on high-resolution imagery, combining a "recognition algorithm" with “recognition marks”. Using GF2 remote sensing image data from spring, summer, and autumn of 2023, a remote sensing model for identifying small urban black and odorous water bodies was developed through the band ratio method, alongside an analysis of the formation mechanisms and causes of these water bodies. Remote sensing markers such as water color, shape, texture, secondary environment, ditch blockage, and shoreline garbage were established on the GF2 images. The final identification result was achieved by integrating the recognition algorithm and identification markers, with accuracy verified through "visual inspection + UAV aerial photography + water quality testing". The results show that: (1) Through precision verification analysis reveals that the accuracy rate (P1), sensitivity (P2), accuracy (P3), and area identification accuracy (P4) of black and odorous water bodies are 85.29%, 90.63%, 94.74%, and 91.19%, respectively, demonstrating the method’s effectiveness in identifying small and slightly black and odorous water bodies in urban areas; (2) By comparing the weights of the band ratio method and remote sensing identification markers, it was found that the recognition algorithm and water color weight accounted for 25.38% and 21.11%, respectively, playing a significant role in identifying small, dark, and odorous urban water bodies; (3) The proportion of small black and odorous water bodies incorrectly identified by remote sensing is 17.1%, while the proportion of missed detections is 8.57%, indicating relatively low misclassification and omission rates; (4) A comparison of the same water bodies in spring, summer, and autumn shows that the integrated remote sensing identification method effectively captures the spatiotemporal changes of black and odorous water bodies. In terms of accuracy, compared to the red-green band ratio method, the difference method, and the WCI index method, the "algorithm-mark" method in this study improves point location identification accuracy by at least 1.88% and area identification accuracy by at least 1.95%, indicating superior performance and providing technical support for the long-term management and remediation of black and odorous water bodies in other cities.

[Objectives] To investigate the use of temporal deformation fractal features for identifying landslides in alpine glaciated areas and analyze their applicability. [Methods] The deformation time series of the Chamoli landslide and its neighboring glacier were characterized using slope (average deformation rate) and fractal features (fractal dimension and fractal goodness of fit). Cluster analysis was used to distinguish landslide areas from glaciers and analyze influencing factors. [Results] The deformation time series of landslides exhibited higher fractal dimensions and lower fractal goodness of fit compared to glaciers. While significant differences in the slope of deformation time series (average deformation rate) were observed between landslides and glaciers, clustering analysis based solely on deformation rate achieved an accuracy of only 61.70%. In contrast, using fractal indexes of the deformation time series (fractal dimension and fractal goodness of fit) significantly improved clustering accuracy to nearly 84.00%. The applicability of this method is attributed to intrinsic differences in material composition, influencing factors, and developmental evolution between landslides and glaciers. Compared to glaciers, landslides are more complex in material composition, influenced by multiple factors, and exhibit greater variability in their deformation time series. [Conclusions] The study demonstrates the feasibility of identifying landslides in alpine glaciated areas using fractal features of deformation time series. In the context of global warming, this method has the potential to support landslide identification and contribute to disaster prevention and mitigation efforts in alpine glacier regions.

[Significance] The spatial patterns of geographic features have a profound impact on the natural environment and human activities. Mining and discovering typical feature patterns from spatial-temporal data is a prerequisite for morphological analysis and planning, which can provide basic support for urban planning and watershed planning. Spatial clustering pattern is a significant and repeated orderly arrangement or combination of relationships between geographic features, which shows a significant distribution pattern and spatial morphology. The discovery of spatial clustering pattern of features is facilitated by spatial analysis, data mining, pattern recognition, and other related technical methods. This process helps to build a perception of the laws of the arrangement and combination of features within a complex and irregular collection of feature sets. Through analytical reasoning, it uncovers the spatial clustering and morphological structure of features with specific semantics. This discovery is of great significance in revealing the spatial distribution law of features, explaining the formation mechanism of geographic phenomena, and understanding the interaction process between humans and space. [Progress] On the basis of elaborating the connotation of spatial clustering patterns of features, this paper summarizes two types of methods for spatial clustering pattern discovery, including rule-oriented pattern extraction and data-driven pattern recognition. The rule-oriented pattern extraction methods rely on expert knowledge to summarize pattern characteristics. They express, constrain and guide the pattern discovery process with formal explicit rules, and extract the features of the specified spatial clustering patterns from the spatial data set. The data-driven pattern recognition methods draw knowledge from both 'experts' and 'data'. They learn the pattern characteristics of features from multiple scales and perspectives through a large number of samples automatically under the guidance of expert knowledge, and perform category prediction on a set of features in order to identify the spatial clustering patterns of the features. Subsequently, the spatial clustering pattern discovery of three types of typical features, namely buildings, roads and water systems, is reviewed. The data-driven approach represented by graph deep learning is usually superior to the rule-oriented pattern extraction approach in terms of pattern discovery accuracy due to its powerful pattern learning capability. In terms of the overall trend, spatial clustering pattern discovery of features is shifting from traditional methods to close integration with deep learning methods. [Prospect] In the future, knowledge aggregation of the rule base and sample set for feature spatial clustering pattern discovery, active discovery techniques for clustering patterns, graph deep learning models for efficient clustering pattern discovery, and pattern discovery based on generative AI will become the main research directions.

The accurate extraction of tunnel sections is a pivotal step in tunnel deformation analysis. However, due to inadequate illumination, the reflection and occlusion caused by dust and structural elements generate noise and erroneous points in the point cloud data, complicating data processing. Additionally, the intricate geometry of tunnel features, such as curved surfaces, corners, and cracks, renders traditional section extraction algorithms ineffective for point cloud data. Consequently, there is an urgent need for more efficient and robust algorithms. To address this issue, this paper proposes a method for continuous tunnel section extraction based on laser point cloud data. First, a combination filtering method is introduced, integrating Random Sample Consensus (RANSAC) cylindrical fitting and radius filtering to effectively remove scattered outliers and noise points adhering to the tunnel walls with sparse density. Next, the tunnel central axis is obtained via bidirectional projection, and a mathematical function model is established in line with the principle that ‘a straight line intersecting the tunnel central axis intersects the tunnel wall’, enabling the continuous extraction of tunnel section point clouds. Finally, the fitting center coordinates of the section points and the tunnel design radius are used as parameters to calculate the radial deviation of the tunnel points, representing the shape variables. The tunnel point cloud is visually rendered using these shape variables to display the overall deformation of the tunnel. In this paper, laser point cloud data from three sections of a subway tunnel in Chengdu are used as the experimental data. The results show that the mean values of Class I error, Class II error, and total error are 1.48%, 1.03%, and 1.21%, respectively, with the Kappa coefficient reaching 97.45% when using this method for noise filtering. Compared to traditional methods such as least squares, density clustering, and normal deviation algorithms, this method reduces cumulative errors by 9.34%, 10.61%, and 4.41%, respectively, while increasing the Kappa coefficient by 5.36%, 6.38%, and 3.65%. This demonstrates the enhanced robustness and accuracy of the proposed method. Moreover, the mean deviation between the tunnel section fitting radius obtained through this method and the design radius is merely 1.36 mm, compared to deviations of 1.60 mm and 6.00 mm with existing methods, achieving reductions of 2.5 mm and 2.7 mm, respectively. The range of the tunnel shape variable is between 0 and 18 mm, and the overall deformation of the tunnel is visually displayed through point cloud rendering. The method provides a reliable foundation and essential support for tunnel safety monitoring.

[Objectives] Traditional settlements contain rich geographical, cultural, and historical information, making them an essential component of cultural heritage. The urgent need to protect these resources highlights the importance of their preservation. Research in traditional settlements has generated vast, multimodal, and heterogeneous data resources. However, much of the textual information remains unstructured, limiting its potential for in-depth analysis and the exploration of embedded landscape gene information. There is currently a lack of principles and methods that combine data mining and natural language processing to extract cultural landscape genes information from extensive textual data on traditional settlements. This study introduces the concept of Traditional Settlement Landscape Genes Named Entity (TSLGNE) and applies it in recognition experiments using 48 traditional villages in Shaoyang, supported by the BERT-BiLSTM-CRF deep learning model. [Methods] First, the study explores the connotation, classification system, and knowledge representation of TSLGN by combining geographical entity characteristics with cultural landscape gene theory. Second, based on the TSLGNE classification system and an extended BIOES annotation method, the source text data from the study area is annotated to construct a corresponding corpus. Subsequently, the BERT-BiLSTM-CRF model is utilized for TSLGNE identification and extraction. Finally, the obtained TSLGNE knowledge is organized and stored using a Neo4j graph database, enabling spatial feature analysis of traditional settlements and their associated TSLGNEs. [Results] The model achieves an overall F1-score of 64% for TSLGNE recognition, outperforming the BiLSTM-CRF and BERT-CRF models by 11% and 1%, respectively. Notably, the model significantly enhances recognition performance for entities with low-quality and semantically complex data, with the F1-score for cultural gene category C3 increasing by 31% and 5%, respectively, compared to the baseline models. [Conclusions] The proposed model efficiently extracts TSLGNE information such as architecture, environment, and culture from large-scale text. Additionally, it effectively analyzes the spatial characteristics and relationships of cultural genes within traditional settlements in complex regions. This study offers valuable insights into traditional Chinese settlements, combining GIS and spatial data mining methods to advance research on their key cultural characteristics.