[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] 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] 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 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] 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.

[Significance] The social-cultural transformation of geography and its integration with GIS technology are of great significance for gaining a deeper understanding of the complexity of human-land relationships. However, in practice, there are often insurmountable disciplinary barriers between the two fields, such as divergences between qualitative and quantitative methods and subjective versus objective perspectives. To address these challenges, the Western geography community has developed an interdisciplinary approach, known as qualitative GIS, that effectively bridges these disciplinary conflicts. This paper reviews and summarizes the research progress of qualitative GIS, aiming to provide new methodological insights for socio-cultural research in Chinese geographical studies and to promote deeper integration between the disciplines. [Progress] Qualitative GIS originates from a critical reflection on traditional geographic information technology and incorporates the methodological concepts of participatory research and mixed-methods research. On one hand, qualitative GIS emphasizes the democratization of geographic information, encouraging public participation in its production and use. On the other hand, it seeks methodological breakthroughs to enhance GIS's ability to process qualitative data, fostering a deeper understanding of phenomena from a spatial perspective. [Prospect] Based on the current development status and trends of qualitative GIS, this paper makes the following predictions for its future: (i) a shift from absolute space to relational space at the level of spatial epistemology; (ii) improved accommodation of diverse data types; (iii) exploration of the capacity for batch processing of qualitative data using AI technology; (iv) achieving a balance between research efficiency and participants' engagement through the integration of internet technology with in-depth fieldwork; and (v) the development of a standardized technological framework for qualitative GIS.

[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] 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 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] The accurate identification of critical road segments is crucial for effective traffic management across entire road networks. While significant progress has been made in identifying critical road segments, existing methods often fail to identify relatively critical road segments in local areas with lower traffic flow, particularly in large-scale networks such as city-level road systems. [Methods] To address this limitation, this study proposes a two-stage feature learning method based on the dynamic and static embeddings of road segments to identify critical road segments in large-scale networks. The proposed method consists of several key steps. First, travel routes are extracted from mobile positioning data to construct a comprehensive traffic corpus, which serves as the foundation for further analysis. Next, a two-stage feature learning process is conducted: (1) Static embeddings are extracted for each road segment to capture their inherent, unchanging characteristics. These embeddings are clustered to identify initial cluster centers, which serve as preliminary indicators of critical road segments. (2) Dynamic embeddings are then extracted for each road segment and processed using attention pooling, which emphasizes the most relevant aspects of the traffic data. These pooled feature vectors are subjected to differentiable clustering, a technique that optimize the clustering process through a loss function. The model iteratively adjusts until the loss value converges, signaling optimal clustering. Upon convergence, the static and dynamic features are fused to generate comprehensive feature representations for each road segment. These fused features are clustered again to identify the final cluster centers, which represent the critical road segments within the network. To validate the proposed method, a traffic corpus is constructed by using mobile positioning data from the Third Ring Road area of Fuzhou City. [Results] An identification experiment and comparative analysis of critical road segments are conducted using this road network as a case study. The results show that the proposed method effectively identifies critical road segments in large-scale road networks and relatively critical segments in local areas. [Conclusions] Furthermore, compared to existing methods, this method achieves superior performance across various evaluation metrics, indicating that the identified set of critical road segments is more reasonable and practical.

[Background] Physical model methods such as peridynamics, which offer the advantages of not requiring prior knowledge or continuity assumptions, are highly applicable to land subsidence modeling. However, the simulation accuracy of these modeling methods is limited by factors such as underground spatial structure and boundary conditions. Changes in groundwater levels, static building loads, and dynamic road loads can significantly impact the simulation results. Physical models and deep learning have naturally complementary strengths, and their integration represents a promising development direction, expected to enhance simulation accuracy. [Methods] This paper proposes a new approach to land subsidence modeling that combines peridynamics with deep learning methods. Based on using peridynamics to describe the physical processes of regional land subsidence, deep learning methods, including neural networks and Gaussian Process Regression, are employed to construct various boundary conditions that adapt to temporal developments and changes. This approach enables the optimization of boundary conditions within the peridynamics-based land subsidence model. [Results] In a case study of land subsidence in the Tongzhou District of Beijing from September 2021 to May 2023, the Root Mean Square Errors (RMSE) for the training and test sets of the peridynamics model combined with deep learning were 6.25 mm and 7.71 mm, respectively. Compared with the RMSE of 22.62 mm without Gaussian Process Regression, these results reflect a reduction of 72.37% and 65.92%, respectively. [Conclusions] Experimental results show that land subsidence modeling and simulation methods combining peridynamics and deep learning can significantly improve subsidence prediction accuracy. This approach to integrating physical models and artificial intelligence is highly applicable for regional land subsidence modeling and evolutionary studies.

[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] 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] In recent years, due to the rapid development of industrialization and urbanization, near-surface ozone pollution in many cities in China has become increasingly severe, disrupting people's daily lives and production activities. This study aims to deconstruct the influence of meteorological and socioeconomic conditions, as well as their interactions, on the distribution of near-surface ozone in China from the perspective of multi-spatiotemporal patterns. Moreover, the spatial-temporal variations of ozone exhibit multiple patterns, each affected by different dominant factors. However, most previous studies have explored the spatial-temporal characteristics of ozone and its influencing factors from a single perspective, with insufficient consideration given to the spatial-temporal heterogeneity of O₃ pollution. The interactions among influencing factors were rarely quantified from various spatial-temporal perspectives. [Methods] Using ozone pollution, meteorological, and socioeconomic data from 331 cities in China from 2014 to 2023, this study used the empirical orthogonal function and GeoDetector model to identify the typical spatiotemporal patterns of near-surface ozone pollution from multiple perspectives. At the same time, it quantifies the determinant power of each influencing factor and their interactions on different spatial-temporal patterns of ozone pollution. The Geotree model was further used to analyze the dynamic evolution of the dominant spatial patterns. [Results] The results show that the first spatiotemporal pattern accounts for 71.1% of the total variance, revealing that high-risk areas are mainly located in North China and the Yangtze River Delta region, with obvious seasonal changes, peaking mainly in the summer. The dominant socioeconomic and meteorological factors affecting this pattern are population density (Geo_q = 0.20) and average temperature (Geo_q = 0.86), respectively. The second spatiotemporal pattern accounts for 6.8% of the total variance, indicating that high-risk areas are mainly distributed in South China, with an increasing trend. The dominant socioeconomic and meteorological factors influencing this pattern are population density (Geo_q = 0.26) and average temperature (Geo_q = 0.37). Notably, the interactions between various factors have a greater influence on ozone pollution than the individual factors themselves. [Conclusions] The spatiotemporal patterns of ozone pollution in China are influenced by multiple factors, and the severity of ozone pollution dynamically varies across different city types and development stages.

[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.

[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.