[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] 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] 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] 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.

[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.

[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.

[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] 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] 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.

[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.

[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.

[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.

[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.

[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.

[Objectives] As remote sensing classification and interpretation technologies continue to advance, the intelligent interpretation of natural resources in complex environments has become a critical research focus. The accuracy and reliability of remote sensing data interpretation depend fundamentally on the quality and representativeness of the samples used in the analysis. In China, diverse terrain, complex meteorological conditions, and fragmented land surface structures introduce significant spatiotemporal variability, making the selection and quality of remote sensing samples particularly challenging. Traditional sampling methods often fail to adequately represent the full spectrum of characteristics inherent in these diverse landscapes, leading to substantial biases and inaccuracies in interpretation outcomes. [Methods] To address these challenges, this study offers a comprehensive review of key elements in remote sensing classification, encompassing methods for sampling labeled data, techniques for multi-scale morphological transformations to augment samples, and strategies for evaluating the quality of labeled samples. The research emphasizes the critical importance of optimizing sample selection to reduce bias and improve interpretation accuracy. It explores the theoretical foundations for sample optimization, highlighting the necessity of obtaining representative samples that accurately capture the complexity and variability of the land surface. [Results] One of the primary contributions of this study is the development of a novel sampling optimization method that integrates terrain complexity into the sampling process. By considering the diverse and intricate nature of the landscape, our approach enhances the representativeness of the samples, thereby reducing errors introduced by sampling bias and significantly improving the accuracy of remote sensing interpretation. In particular, we emphasize the role of multi-scale morphological transformations, which allow for the expansion of sample diversity and the generation of more robust and generalizable remote sensing models. This process is crucial for creating high-quality labeled samples that can better support complex interpretation tasks. The effectiveness of this complexity-based sample optimization approach is demonstrated through a series of experiments. These experiments reveal significant improvements in interpretation accuracy when compared to traditional sampling methods. This substantial enhancement underscores the value of incorporating terrain complexity and multi-scale transformations in the sampling and interpretation process. [Conclusions] By following the principles and methodologies outlined in this research, practitioners and researchers can obtain high-quality, representative labeled samples that significantly improve the precision and efficiency of remote sensing classification models. The findings of this study provide a solid theoretical and technical foundation for advancing remote sensing intelligent interpretation technology. Furthermore, the research offers practical insights and guidelines for applying these optimized sampling strategies to the classification of natural resources in complex scenarios, ultimately contributing to more accurate and reliable interpretation outcomes in the field of remote sensing.

[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.

[Objectives] Remote Sensing Intelligent Interpretation (RSII) often encounters challenges when applied for practical resource and environmental management, especially for complex scenes. To address this, we start from the explanation of why remote sensing interpretation is needed, and clarify that the mission of RSII is to achieve more rapid interpretation to build the digital twin earth with lower cost compared to manual interpretation. However, most RSII systems operate as a unidirectional process from remote sensing data to geoscience knowledge, lacking the feedback from knowledge to data. As a result, remote sensing information extracted from data often mismatch the knowledge of existing geoscience, creating a trust crisis between RSII researchers and geoscience researchers. And the crisis becomes more severe with the uncertainty of remote sensing information. [Analysis] We believe that an agreed upon representation model of geoscience knowledge between RSII researchers and geoscience researchers is necessary to alleviate the crisis. Based on this analysis, we propose a framework using geo-science zoning as the bridge to connect RSII researchers and geoscience researchers. In this framework, knowledge from geoscience could be transferred into the RSII system through geo-science zoning so that the interpretation results could be more coincided with geoscience knowledge. The framework mainly relies on (a) the scene complexity measurement, (b) the knowledge coupling of geographic regions to form the geological zoning method for remote sensing intelligent interpretation, and (c) the sampling specification of regional samples. The scene complexity measurement provides quantitative features for geoscience zoning and sampling weights assignment. Existing zoning data, such as ecological zoning data, geographic elements, and multisource remote sensing images are the main data inputs for geoscience zoning. The main principles for constructing zoning methods include (a) the geoscience elements type, (b) the scale of geoscience zoning, and (c) the process of information flow from data to knowledge. [Prospects] With these models, we can realize regional RSII guided by the knowledge. Preliminary experiments on complexity and optimization sampling, image segmentation scale optimization, cultivated land type fine classification, etc., reveal that this framework has great potential in improving the geoscience knowledge acquisition by RSII, enhancing the accuracy of the state-of-the-art RSII by 6%~10%, especially for the high-complexity nature scenes. However, the superiority of the framework may disappear if the scene for interpretation is simple, like the first level land use/cover classification, which is mainly caused by the inefficient samples after geoscience zoning. Therefore, more attention is needed in sampling when developing geoscience zoning framework.

[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] The active discovery and scientific assessment of the ecological damage caused by mining disturbances in coal mining areas is a key focus in the research of "intelligent mining and green mines". However, traditional analysis methods, characterized by a reliance on monitoring, limited discovery capabilities, dependence on experts, and retrospective assessments, struggle to meet the regulatory authorities’ actual needs for intelligent identification and rapid early warning. This article aims to verify the adaptability of knowledge graph-based spatial reasoning methods for actively detecting and intelligently identifying ecological damage in coal mining areas, while exploring innovative approaches and technologies for ecological environment governance in the modern era. [Methods] By integrating multi-source monitoring data from "Space-Air-Ground-Human" systems and summarizing knowledge on the location, form, group distribution, distribution patterns, and spatiotemporal evolution of ecological units in coal mining areas, an indicator system for describing these units is designed. Additionally, intelligent identification and reasoning rules for ecological damage are constructed using knowledge graph technology. [Results] Coal mining subsidence is a typical land use/cover change phenomenon caused by coal resource extraction. Remote sensing technology is crucial for extracting subsidence and its variations, yet traditional methods often overestimate the affected area due to misclassification of natural water surfaces as mining-induced subsidence. To address this, new knowledge and spatial reasoning rules were introduced to accurately differentiate between mining subsidence areas and natural water surfaces. Using a coal mining area in Shanxi Province as a case study, spatial reasoning rules were applied to identify subsidence units accurately. Experimental results demonstrated that the proposed method improved the precision and intelligent recognition accuracy of mining disturbance units. Compared with traditional recognition approaches, the proposed method reduced false positives by 21.43%. [Conclusions] Knowledge graph technology proves highly adaptable for analyzing and evaluating ecological environments in coal mining areas. It offers technical support for the proactive discovery and accurate identification of ecological damage caused by mining disturbances. Furthermore, it provides new technological tools and ideas for building advanced ecological governance models.

[Objectives] All meaningful forms of human discourse are rhetorical, and the purpose of rhetoric is to enable communication and foster sympathy between parties with certain views. Narrative maps are essentially a discursive practice for communicating information and exchanging ideas, characterized by the strategic use of rhetoric to construct persuasive discourse and achieve the goal of "agreement" or "persuasion". In the current era, where visual dominance is increasingly prominent, rhetoric has garnered growing attention in cartography. This turn not only addresses core issues in narrative map research but also provides a realistic path for enriching and reconstructing the existing knowledge of modern cartography. However, the academic community has yet to establish a systematic framework, leaving three key issues unresolved: (1) How to conceptualize the rhetoric of narrative maps? (2) How to categorize the rhetoric of narrative maps? (3) What is the working mechanism of rhetoric in narrative maps? [Methods] To address these research gaps, this article, firstly, follows the research paradigm of rhetoric to clarify the essence of rhetoric in narrative maps, and defines it as: "During the design process of narrative maps, cartographers use certain visualization strategies to facilitate the representation of events, thereby weaving explicit narrative intentions into the mapping space in an implicit way to create persuasive discourse or emotional agreement for viewers." Secondly, a classification criterion is proposed based on the differences between content semantic representation and logical semantic representation. Two major categories, semantic rhetoric and structural rhetoric, along with 24 minor classes, are divided for rhetoric of narrative map. Semantic rhetoric mainly focuses on enhancing the understanding of content, expressing the connotation and imaginative tension of map "text". Structural rhetoric aims to emphasize the logic semantic relationships in narrative discourse, presenting the narrative logic of events. Semantic rhetoric often manifests as the design of visual symbols to describe events, serving as the "visual punctum" of narrative maps. Structural rhetoric typically involves adjusting the arrangement and structure of different event units, functioning as the "visual stadium" of narrative maps. Next, the mechanism of rhetoric in narrative maps is explored from four aspects: the dimensions of rhetoric, the hierarchy of rhetoric, the integrated use of rhetoric, and the applicability principles of rhetoric. Finally, this study demonstrates the applicability of the proposed theoretical framework through a case study of "Jiangnan Canal", illustrating how the framework can facilitate narrative map design. [Conclusions] This paper lays a theoretical foundation for narrative map research and contributes to the theoretical innovation of contemporary cartography.

[Objectives] The ultra-wideband ranging errors in underground narrow spaces show a significant heavy-tailed distribution. The Gaussian mixture model is more closer to the empirical distribution than a simple Gaussian probability envelope. The Protection Level (PL) obtained through traditional bounding distribution models are overconservative, which reduces system usability. [Methods] To enhance the system usability, an overbounding framework based on Gaussian Mixture Model (GMM) is employed to handle the Time of Arrival (TOA)-based distance measurements obtained from the ultra-wideband ranging system. Firstly, a possibility density function (PDF) of the ranging errors is determined in the form of a dual-component GMM using Expectation-Maximum (EM) algorithm, which provides an approximation of the practical noise distribution in underground space. The PDF plays a crucial role in computing the upcoming overbounding PL. To ensure the mathematical tractability of the overbounding model, the bilateral boundaries of the errors are examined through the use of Cumulative Distribution Function (CDF). Next, in correspondence with the GMM-based PDF model, an asymmetrical CDF is obtained, necessitating the separate overbounding operation on both sides of the CDF. A heuristic adjustment on previous GMM-based PDF is conducted to increase the possibility density of the bilateral tail parts, ensuring sufficient but not excessive space to guarantee the validity of the PL. Initially, on the left side of the CDF, the weight and variance of the Gaussian component with a lower mean value in the GMM-based PDF are increased. Subsequently, the updated PDF will be shifted to the left to create a new version of the GMM-based PDF, with higher values in the left part of the CDF compared to the traditional one. Similar adjustment work needs to be conducted for the right part of the original CDF. After the adjustment operations on both sides, two different GMM-based PDFs can be obtained for every single base station, one is termed as left-boundary PDF while the other is right-boundary PDF. Finally, the predefined PDFs from different UWB base stations will be used to infer the overall PDF in position domain using a convolution operation. Based on this, PL can be computed from the inverse operation of the corresponding CDFs. [Results] In order to verify the methodology, experiments under practical simulated underground scenarios are conducted using six UWB base stations. Error models are constructed using sample data collected within a range of 3 to 93 meters. Evaluation of practical performance shows that the GMM-based bilateral bounding box reduces PL by more than 20% compared to traditional Gaussian-based calculations. [Conclusions] The GMM-based PDF can tighten the PL with a relatively low computational cost, enhancing the system usability.

[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] Cross-view image matching and localization refers to the technique of determining the geographic location of a ground-view query image by matching it with a geotagged aerial reference image. However, significant differences in geometric appearance and spatial layout between different viewpoints often hinder traditional image matching algorithms. Existing methods for cross-view image matching and localization typically rely on Convolutional Neural Networks (CNNs) with fixed receptive fields or Transformers with global modeling capabilities for feature extraction. However, these approaches fail to fully address the scale differences among various features in the image. Additionally, due to their large number of network parameters and high computational complexity, these methods face significant challenges in lightweight deployment. [Methods] To address these issues, this paper proposes a lightweight cross-view image matching and localization method that employs multi-scale feature aggregation for ground panoramic and satellite images. The method first extracts image features using LskNet, then designs and introduces a multi-scale feature aggregation module to combine image features into a global descriptor. The module decomposes a single large convolution kernel into two sequential smaller depth-wise convolutions, enabling multiple scale feature aggregation. Meanwhile, spatial layout information is encoded into the global feature, producing a more discriminative global descriptor. By integrating LskNet and the multi-scale feature aggregation module, the proposed method significantly reduces parameters and computational cost while achieving superior accuracy on publicly available datasets. [Results] Experimental results on the CVUSA, CVACT, and VIGOR datasets demonstrate that the proposed method achieves Top-1 recall rates of 79.00% and 91.43% on the VIGOR and CVACT datasets, respectively, surpassing the current highest-accuracy method, Sample4Geo, by 1.14% and 0.62%. On the CVUSA dataset, the Top-1 recall rate reaches 98.64%, comparable to Sample4Geo, but with parameters and computational costs reduced to 30.09 M and 16.05 GFLOPs, representing only 34.36% and 23.70% of Sample4Geo's values, respectively. Additionally, ablation experiments on public datasets show that the multi-scale feature aggregation module improves the Top-1 recall rate of the baseline network by 1.60% on the CVUSA dataset and by 13.48% on the VIGOR dataset, further validating the effectiveness of the proposed method. [Conclusions] Compared to existing methods, the proposed algorithm significantly reduces both parameters and computational costs while maintaining high accuracy, thereby lowering hardware requirements for model deployment.

[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.

[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.