Cellular Automata (CA) provides an important tool for land use/land cover change simulation. However, previous CA models based on pure cells ignore the mixed land cover structure within cells, making it difficult to simulate the continuous evolution of mixed land systems during rapid urbanization. The Mixed-Cell Cellular Automata (MCCA) can address this issue, but its widespread application is hindered by the difficulty in obtaining fine-scale mixed structure data. To solve these problems, this study proposes a simulation analysis framework that couples the mixed pixel decomposition method with the MCCA model. This framework uses the mixed pixel decomposition algorithm to directly obtain the sub-pixel scale mixed structure data required by the MCCA model from Landsat images. The SHAP method is utilized to explore the driving forces of sub-pixel scale land cover change. To verify the proposed framework, we conducts an experiment in Wuhan city. Results show that: 1) The decomposition accuracy of the land cover data is above 0.8, and the mcFoM index of the simulation results is 0.38, indicating that this coupled model has high accuracy in characterizing the spatial pattern of mixed land structures and simulating future changes; 2) The proposed coupling model can effectively simulate the fine-scale dynamic changes of land cover proportions and discover relevant patterns of regional land use changes. For example, future land cover structure changes will mainly concentrate in built-up areas, and land mixture will experience a process of increasing first and then decreasing. Socio-economic factors such as proximity to companies, the municipal government, and high population and GDP are important driving factors for the expansion of impervious surfaces, and impervious surfaces in urban centers relatively far from high-speed railway stations grow more rapidly; 3) The future land cover change trends simulated by the proposed model are consistent with the future planning layout of Wuhan. The comparison between multiple scenarios demonstrates the MCCA model’s ability to accurately capture the subtle differences in land cover proportion between pixels. This method couples the mixed pixel decomposition method from the field of remote sensing with the mixed Cellular Automata (CA) model from the field of GIS, solving the problem of lacking fine-scale data sources for simulating mixed land cover structures. It simulates future changes in mixed land cover structures at the sub-pixel scale, which can enrich existing research on mixed land structures and provide a certain theoretical basis for urban development decisions. Additionally, it opens up new avenues for the application of CA models in other areas.

Points of Interest(POI), which are rich in semantic information, reflect current situations, and indicate areas of interest, serve as the primary data source in studies related to urban functionalization studies. These studies aim to deepen the understanding of human activities and environmental features within geographical spaces. An important research issue for enhancing the understanding of the human-environment system is detecting outliers, namely elements considerably different from the rest in large-scale spatial data. The detection of POI outliers can be broadly discussed from three perspectives: (1) spatial distribution differences, (2) spatial contextual differences, and (3) variations in the usage frequency of some POI instances and their surrounding points in specific areas due to factors such as special events, changes in urban population behavior, cultural activities, etc., leading to outliers. This paper focuses on discussing the phenomenon of POI outliers caused by spatial distribution differences. However, current outlier detection methods face with challenges. They fall short of adequately expressing and quantifying POIs' local spatial distribution features. The effectiveness of these methods needs further investigation. Given these considerations, this study proposed a novel approach for detecting POI outliers based on determination of local aggregation scales. Initially, we constructed spatial adjacency relationships of the POIs using Delaunay triangulation. Subsequently, the local aggregation characteristic scales of these points were determined by combining cross K-nearest distances and multi-scale feature parameters. Thereafter, based on the scale constraint, the points and their adjacent edge sets that met the conditions were extracted. Finally, we employed the edge length constraint index to systematically remove local long edges that did not meet the prescribed criteria. This meticulous process ensured the integration of the refined point set, thus facilitating the comprehensive detection of outliers within the POI context. The comparative experimental results, drawn from real-world data, suggested that the proposed method possessed a strong generalization ability. Moreover, it effectively and robustly detected outliers without compromising the inherent distribution characteristics of POI. We also performed an interpretability analysis of outlier detection results. The analysis revealed a close correlation between the causes of outlier distribution and various factors including the proportion of POI types, spatial layout, footprint area, and public awareness level. This study provides novel methodologies and academic perspectives for a comprehensive understanding of urban development trends, optimal resource allocation, and the enhancement of urban sustainability and quality of life.

With the increasing improvement of the living standard of the residents in urban areas and their pursuit of quality of life, urban green spaces have become the main places of leisure and recreation for residents. Under this background, how to fairly evaluate the rationality of the layout of urban green spaces and put forward suggestions for improvement has become an important part of urban transportation and land use planning. Urban green space accessibility is a key indicator for evaluating the layout of urban green spaces. In response to the limitations of assessing attractiveness based solely on urban green space area in the past, this paper takes Suzhou urban area as an example. In addition to calculating accessibility using objective attributes in the traditional framework, the paper delves into social media data to incorporate urban residents' subjective sentiment towards urban green space quality indicators into the consideration scope of attractiveness. Through this innovative integration, the paper improves the Two-Step Floating Catchment Area (2SFCA) method, analyzing in-depth the accessibility of urban residents to urban green spaces and the dynamic changes in accessibility before and after public health emergencies. The improved 2SFCA method, combined with Sentiment Knowledge Enhanced Pre-training (SKEP) model, incorporates residents' emotional evaluations of urban green spaces to measure their subjective attractiveness. Meanwhile, considering the skewness characteristic of area indicators, the paper innovatively proposes the Scale Index (SI) as an objective attractiveness evaluation indicator for urban green spaces, providing more scientific and robust support for urban green space planning. The research findings reveal that during public health emergencies, individuals tend to prefer urban green spaces that offer convenient access, such as community parks. However, as daily life gradually resumes, there is a greater preference for urban green spaces equipped with high-quality facilities, such as specialized parks. Only considering objective area as the attractiveness of urban green space leads to overestimation of the accessibility of large-area and underestimation of small-area urban green space. Moreover, solely based on visitors' subjective quality perception of urban green space may underestimate the accessibility of communities around large urban green spaces. The improved 2SFCA method, considering both visitors' subjective perception and objective attributes of urban green space attractiveness, can more accurately assess urban green space accessibility, broadening the perspective of traditional urban green space accessibility assessment. This method can not only be applied to urban green space planning, but also provides a new idea and computational framework for the accessibility analysis of public service facilities.

[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.

Image super-resolution technology enhances image clarity and enriches image detail by improving image spatial resolution, enabling quality enhancement without changing hardware conditions. Given the large size, complex target features, and abundant details of remote sensing images, along with the need for efficient information acquisition, we propose a Diffusion Super-Resolution (DSR) algorithm based on a conditional diffusion model. This approach uses low-resolution remote sensing images from the same region as conditioning inputs to the diffusion model, while high-resolution images with added noise are concatenated as inputs. A deep noise training network was constructed with U-Net as the backbone, incorporating residual connections and self-attention mechanisms. The loss function was also improved for better super-resolution results. The DSR method was tested using high-resolution remote sensing images from multiple periods of the domestic Gaofen and SuperView satellite series. The super-resolution results demonstrated pixel dimension expansion from 32 to 128. Comparative experiments with Bicubic, SRGAN, Real-ESRGAN, and SwinIR super-resolution algorithms showed that the DSR method outperforms these algorithms in both PSNR and SSIM metrics. Additionally, the DSR method significantly improves the quality of multispectral remote sensing images. By leveraging the conditional diffusion model, it successfully preserves rich detail and enhances spatial resolution without compromising image clarity. This method offers an efficient solution for super-resolution reconstruction, ensuring effective information acquisition in remote sensing applications and fulfilling the requirements of various domains such as land use classification, environmental monitoring, and urban planning. Moreover, the DSR method also opens new avenues for future research by demonstrating the potential of diffusion models in remote sensing image processing. It overcomes the limitations of simple convolutional networks, which extract only shallow features, and avoids the convergence issues commonly seen in adversarial neural networks during training, ultimately improving the restoration of rich details in remote sensing images.

The We-Map, a novel cartographic phenomenon emerging in the era of social media, is distinctively characterized by mass participation, personalization, and swift dissemination. However, existing research on We-Map falls short in addressing the intricate challenges posed by point symbol design, thereby hampering the fulfillment of the public's desire for personalized cartographic representations. To bridge this gap, this paper starts from the perspective of We-Map mapping production, taking common map symbols in hand-drawn maps as the research object, and constructs an open hand-drawn map dataset. To this end, we have constructed a comprehensive dataset encompassing a diverse array of hand-drawn map symbols, encompassing various types and styles. This dataset serves as a valuable resource for exploring and enhancing the automated extraction of common map symbols. Drawing inspiration from existing research, we have embarked on a journey to identify and evaluate the most suitable model for our task. Among the numerous models for object detection, the performance of the YOLOv5 series models is well-known, and therefore this article will not delve into it excessively. Specifically, through comparison, we ultimately chose the YOLOv5-X model, which boasts advanced capabilities in object detection and classification. By leveraging the YOLOv5-X model, we have achieved remarkable results in the automatic extraction of common map symbols from hand-drawn maps. Our experiments reveal that the model achieves high levels of accuracy, recall, and F1 score in identifying and extracting point categories from the hand-drawn map dataset. These scores stand testament to the model's effectiveness in capturing the intricate details and unique characteristics of hand-drawn map symbols. Moreover, to further validate the generalizability of our model, we have conducted additional experiments on the Quick Draw doodle dataset. The results obtained from these experiments confirm that our model performs equally well in extracting common map symbols from diverse and varying datasets. The significance of this study lies not only in enhancing the dataset available for personalized point symbol research in We-Map but also in advancing the techniques for extracting common map symbols. By introducing more diversified elements into We-Map cartography, we have opened up new avenues for more flexible and personalized mapmaking in the age of self-media. This study represents a significant step forward in the evolution of cartography in the rea of self-media, catering to the evolving needs and preferences of the modern audience. The finally extracted point symbols can provide a data foundation for downstream tasks related to We-Map.

[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.

Accurate matching of line features is of paramount importance in the reconstruction and optimization of three-dimensional models. However, traditional dual-view line matching encounters challenges due to a limited number of views, resulting in suboptimal robustness in line matching. For line extraction results with breaks, the number of lines extracted for the same line on different images is different, resulting in poor integrity of straight line matching results. To address these issues, this paper proposes a multi-view line matching algorithm that combines Multiple-View Stereo (MVS) and Leiden graph clustering. The algorithm commences by employing the line extraction algorithm and the MVS three-dimensional reconstruction algorithm on input multi-view images for line information extraction and multi-view three-dimensional information extraction, respectively. This process yields lines on each view, dense three-dimensional points encapsulating the image scene, and the correspondence between object-side three-dimensional points and their corresponding image-side two-dimensional points. Building upon this foundation, the algorithm constructs line descriptors in the image domain by considering lines and their matching point sets within their neighborhoods. Subsequently, leveraging the three-dimensional line projection angle constraints, point-line position relationship constraints, and corresponding point constraints, the algorithm filters matching candidates based on these three geometric constraints. Harnessing the similarity relationships between lines on each view, an undirected graph is constructed. Here, lines on each view serve as nodes, and the similarity scores between lines act as edge weights. Simultaneously, connected components composed of single nodes are removed from the undirected graph, resulting in the set of connected components that represent the initial matching results. In the final stage of this process, nodes of each connected component are reconnected based on same-view collinear constraints, forming many sub-undirected graphs. The Leiden algorithm is then applied to cluster the nodes of these sub-undirected graphs. The clusters composed of a single node in the clustering results represent unsuccessfully matched lines, while clusters composed of two or more nodes signify the presence of corresponding lines across multiple views. Ultimately, the algorithm achieves accurate line matching on multi-view images. The experimental results show that the line matching results using the proposed algorithm are improved in terms of the number of line matches and the matching accuracy relative to other comparison algorithms.

Visualization of uncertainty is a research focus and difficulty in the field of cyberspace map visualization. Reasonable design of uncertainty symbols is crucial for the quick reading, mining, accurate analysis, and decision making of cyberspace map information. In this paper, a symbolic representation of double variables uncertainty in cyberspace based on data model of multi-granularity spatiotemporal objects is proposed. This method can solve the problem that the variable symbols in the cyberspace node link graph cannot reflect the expression uncertainty of nodes and connected edges in a timely and efficient manner. Taking geographic social networks as an example, we first adopt the modeling method of multi-granularity spatiotemporal objects and divide the cyberspace into carrier class, subject class, and data class based on the four classifications of cyberspace proposed by Academician Fang Binxing. The entity is divided specifically from the virtual and real perspectives. Then we analyze the content and process of cyberspace object modeling, design cyberspace entity object class, and create spatiotemporal objects. Based on this, combining the problems of traditional symbols in the expression of cyberspace node link graph, the uncertainty expression theory and uncertainty expression model of cyberspace are analyzed. The expression theory reveals that the uncertainty in cyberspace generated from Single Variables expression is larger than that from Double Variables expression over time. The uncertainty expression model divides the uncertainty expression of object data in cyberspace into node uncertainty, edge uncertainty, local uncertainty, and global uncertainty. Then the vizent symbols of nodes and edges of 1-8 levels are made respectively. Finally, a case study is carried out. Firstly, the presentation of application results is conducted. The experiment first constructs the cyberspace object, instantiates it, and then visualizes it with symbols. Secondly, a symbolic control experiment is carried out. The control experiments are carried out from four categories: primary and secondary values, width brightness, saturation transparency, and vizent symbols of the experimental group. The results of the symbol experiment are tested by statistical methods. The results show that the method based on objectified modeling is conducive to the expression of multi-granularity, all-type, and multi-dimensional dynamics of cyberspace, and the development and change of cyberspace can be vividly, intuitively, and comprehensively expressed through visualization and interaction technology. The newly designed vizent symbol has a good effect on the expression of double variables uncertainty difference in cyberspace, which is helpful to obtain the uncertainty information in cyberspace timely, efficiently, and accurately. This study provides references for the development of the field of map visualization in cyberspace.

The use of Unmanned Aerial Vehicles (UAVs) for road image collection is advantageous owing to their large scope and cost-effectiveness. However, the size and shape of road damages vary significantly, making them challenging to predict. Furthermore, due to the limitations of computational resources, generalized target detection algorithms are only applicable to small-size images (512 pixels× 512 pixels or 640 pixels× 640 pixels). This makes them unsuitable for direct application to large-size UAV images (5 472 pixels× 3 648 pixels or 7 952 pixels × 5 304 pixels). The utilization of traditional methods for the detection of multi-scale targets in large-size images is associated with a number of issues, including the slicing of large-size targets and the failure to detect small-size targets. To address these challenges, this paper presents an innovative solution that combines the global-local multiscale fusion strategy with YOLOv5-RDD. First, a YOLOv5-RDD model is constructed, and based on the existing YOLOv5 model, a multiscale C3 (MSC3) module and a Contextual Feature Pyramid Network (CFPN) are designed to improve the detection capability of multiscale targets. Additionally, we introduce an extra detection head for larger-size targets. Then, a global-local multiscale fusion strategy is proposed, which uses resizing and slicing means to obtain global and local information of large UAV images, and then superimposes the global and local multiscale information to obtain the multi-scale information of the whole large image. The detection results are optimized using the center non-maximum value suppression algorithm. Specifically, the global-local multiscale fusion strategy first trains the YOLOv5-RDD using multiscale training strategy to learn complete multiscale features. Then, YOLOv5-RDD predicts multiscale road damages in large-size images using a multiscale prediction strategy to avoid directly applying it to these images. Finally, we use center non-maximum suppression to eliminate redundant object detection boxes. To verify the effectiveness of the proposed method and meet real-world requirements, a UAV-RDD dataset specialized for UAV road disease detection is created. The experimental results show that compared with the original YOLOv5 model, the new model YOLOv5-RDD improves the mAP by 5.8%, while the global-local multiscale fusion strategy improves the mAP by 9.73% compared with the traditional method. The MSC3 achieves the maximum enhancement of mAP@0.5, with an improvement of 2.6%, contributing only 0.8 M parameters. The CFPN yields an improvement of 0.2% in mAP@0.5 while reducing the number of parameters by 8 M. These results fully prove the effectiveness and superiority of the method in this paper.

In the context of the rapid development of urbanization, the reasonable selection of locations for public service facilities is critical for delivering efficient services and enhancing the quality of urban residents' lives. However, prevailing approaches for allocation of public service facilities often fall short of meeting the demands on their performance and efficiency in complex and large-scale real-world scenarios. To address these issues, this article proposed a novel Graph-Deep-Reinforcement-Learning Facility Location Allocation Model (GDRL-FLAM), coupling a Facility Location Allocation Graph Attention Network (FLA-GAT) with a Deep Reinforcement Learning (DRL) algorithm. This proposed model tackled the location allocation problem for public service facilities based on graph representation and the REINFORCE algorithm. To assess the performance and efficiency of the proposed model, this study conducted experiments based on randomly generated datasets with 20, 50, and 100 points. The experimental results indicated that: (1) For the tests with 20, 50, and 100 points, the GDRL-FLAM model exhibited a significant improvement ranging from 11.79% to 14.49% compared to the Genetic Algorithm (GA) which is one of the commonly used heuristic algorithms for addressing location allocation problems. For the tests with 150 and 200 points, the improvement ranged from 1.52% to 9.35%. Moreover, with the increase in the size of the training set, the model also demonstrated enhanced generalizability on large-scale datasets; (2) The GDRL-FLAM model showed strong transfer learning ability to obtain the location allocation strategies in simple scenarios and adapt them to more complex scenarios; (3) In the case study of Singapore, the GDRL-FLAM model outperformed GA significantly, achieving obvious improvements ranging from 1.01% to 10.75%; (4) In all these abovementioned tests and experiments, the GDRL-FLAM model showed substantial improvement in efficiency compared to GA. In short, this study demonstrated the potential of the proposed GDRL-FLAM model in addressing the location allocation issues for public service facilities, due to its generalization and transfer learning abilities. The proposed GDRL-FLAM could also be adapted to solve other spatial optimization problems. Finally, the article discussed the limitations of the model and outlined potential directions for future research.

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

Construction waste is an inevitable byproduct of urban renewal processes, causing serious environmental pollution and ecological pressure. Precisely quantifying the annual production of urban construction waste and the resource conversion rate is crucial for assessing the cost of urban renewal. Traditional manual methods of estimating construction waste production rely heavily on statistical data and historical experience, which are inflexible, time-consuming, and labor-intensive in practical application, and need improvement in terms of accuracy and timeliness. Existing deep learning models have relatively poor capabilities in extracting and integrating small targets and multi-scale features, making it difficult to handle irregular shapes and fragmented detection areas. This paper proposes a Multi-Scale Feature Fusion and Attention-Enhanced Network (MS-FF-AENet) based on High-resolution Remote Sensing Images (HRSIs) to dynamically track and detect changes in buildings and construction waste disposal sites. This paper introduces a novel encoder-decoder structure, utilizing ResNet-101 to extract deeper features to enhance classification accuracy and effectively mitigate the gradient vanishing problem caused by increasing the depth of convolutional neural networks. The Depthwise Separable-Atrous Spatial Pyramid Pooling (DS-ASPP) with different dilation rates is constructed to address insufficient receptive fields, resolving the issue of discontinuous holes when extracting large targets. The Dual Attention Mechanism Module (DAMM) is employed to better preserve spatial details, enriching feature extraction. In the decoder, Multi-Scale Feature Fusion (MS-FF) is utilized to capture contextual information, integrating shallow and intermediate features of the backbone network, thereby enhancing extraction capabilities in complex scenes. MS-FF-AENet is employed to extract and analyze changes in building areas at different time periods, calculating the engineering waste from new constructions and demolition waste from demolished buildings, thereby obtaining the annual production of urban construction waste. Furthermore, MS-FF-AENet is utilized to extract construction waste disposal sites at different time periods, estimating the amount of construction waste landfill based on changes in landfill waste, indirectly assessing the resource conversion rate of urban construction waste. Based on HRSIs of Changping District, Beijing from 2019 to 2020, experimental results demonstrate: (1) Among a series of baseline models including UNet, SegNet, PSPNet, DeepLabV3+, DSAT-Net、ConvLSR-Net and SDSC-UNet, MS-FF-AENet exhibits advantages in terms of precision and efficiency in extracting buildings and construction waste; (2) During the period from 2019 to 2020, the annual production of construction waste in the study area due to urban renewal is approximately 4 101 156.5 tons, with approximately 2 251 855.872 tons being landfill waste and approximately 1 849, 300.628 tons being resource conversion waste, resulting in a construction waste resource conversion rate of 45.09%, further corroborating government statistical reports. This paper provides a convenient and effective analysis approach for accurate measurement of the cost of urban renewal.

Technological knowledge has become the key element of regional innovation and development in the new era. Exploring the inherent mechanism of the growth and development of technology transfer network is of great significance to improve the vitality of regional innovation. However, the endogenous mechanisms and spatial differences of technology transfer network evolution is rarely studied. This study aims to gain a better understanding of the growth and development process of urban technology transfer networks in China and their spatial differences. First, this paper takes 282 cities of China as research units. Second, information on patent transferred data is subjected to ownership linkage mode to construct the urban technology transfer network, resulting in a panel dataset of 282 cities in China in 2001—2020. Finally, stochastic actor-oriented models for the evolution of networks are constructed to study the evolution of technology transfer networks and spatial heterogeneity. Results show that: (1) The evolution pattern of urban technology transfer network in China presents a "core-periphery" structure. The network exhibits strong polarization characteristics, but it is decreasing gradually. The increasingly complex tripartite relationship between cities is an important feature of network evolution. These tripartite relations not only affect the formation of link relations but also promote the differentiation of local levels of the network; (2) Endogenous structural factors are the key factors for the growth and development of urban technology transfer network in China. Reciprocity and network closure constitute the micro basis of the evolution of urban technology transfer network. Path dependence is a key force in strengthening the link relationship between urban technology transfer networks; (3) The endogenous mechanism of the evolution of urban technology transfer network in China has obvious spatial heterogeneity. In the southern region of eastern China, the urban technology transfer network has strong dynamics. Reciprocity, network closure, and path dependence have become the endogenous driving forces for the growth and development of technology transfer networks. In the northern region of eastern China, the evolution rate of the network shows a downward trend. Reciprocity and path dependence contribute to the formation of network link pattern. In the northwest inland and the Qinghai-Tibet Plateau, the network evolution rate tends to increase, but the network density is small, and only the reciprocity effect is significant. This paper will deepen the understanding of the evolution law of urban networks and provide a scientific reference for China's urban innovation and development policy.

The rapid development of information and communication technologies and mobile computing has generated a variety of mobility big data, providing new opportunities for understanding and exploring the spatiotemporal distribution and mobility characteristics of resident travel, and further contributing to the construction of smart cities. However, the emerging mobile data have experienced significant growth in both scale and complexity compared to traditional data, posing challenges for its structural characteristic analysis. To address these issues, this paper proposes an analytical framework to deal with the spatiotemporal distribution characteristics of high-dimensional ride-hailing travel pattern. Compared to traditional square partitions, a regular hexagon is closer to a circle, and the six adjacent hexagons connected to its edges are symmetrically equivalent, which can be more advantageous in aggregating demands with similar travel characteristics into the same partition. Therefore, hexagonal partition is selected as the basic clustering unit, and different spatiotemporal patterns are identified by clustering homogeneous travel distribution groups. Firstly, the spatiotemporal characteristics of travel distribution aggregated in the hexagonal partition are summarized into three main components: the departure demand distribution at the origin partition, the spatial distribution at the destination partition, and the arrival demand distribution at the destination partition. The spatiotemporal similarity between two partitions can be expressed as the product of these three types of distribution similarity. Furthermore, a Clustering Algorithm with Fast Search and Find of Spatiotemporal Density Peaks (CFSFSTDP) is proposed to identify the spatiotemporal patterns of ride-hailing travel distribution in each partition. The spatiotemporal distances between different partitions are obtained through the calculation of spatiotemporal similarity. Finally, affinity propagation clustering algorithm is used to perform clustering analysis on the time series variation pattern of spatiotemporal pattern of travel distribution in each partition. The time series similarity of spatiotemporal patterns between different partitions is represented by the sum of Euclidean distances between time series of each interval, and the model converges through continuous updates of attractiveness and affiliation indices. Through the empirical analysis of Didi Chuxing order data in Chengdu for one month, the validity of the method is verified. Based on the identified seven spatiotemporal distribution patterns, the differences of spatiotemporal patterns in the size, location, and time of demand are analyzed, and the functional types of ride-hailing travel in different partitions are discussed. The identified six time series patterns better grasp the time continuity of spatiotemporal patterns of ride-hailing travel distribution and help to better build the corresponding spatiotemporal evolution digital.

Spatial-temporal data missingness and sparsity are prevalent phenomena, for which spatial-temporal interpolation serves as a critical methodology to address these issues. Spatial-temporal interpolation constitutes a significant research domain within the field of Geographical Information Science. This technique enables the capture of dependencies in spatial-temporal data and the estimation of the geometric and attribute variations of geographical phenomena over time. With the advancement of geospatial technologies, particularly Geographic Information Systems, contemporary spatial-temporal interpolation methods predominantly rely on statistical, machine learning, and deep learning approaches that account for both temporal and spatial dimensions. These methods aim to reveal the evolutionary processes and spatial-temporal distribution patterns inherent in the data. However, a majority of such techniques often overlook long-term dependencies and contextual spatial information when interpolating. This study proposes an innovative model that intertwines Long Short-Term Memory (LSTM) networks with spatial attributes to address these limitations effectively. The proposed model operates through several key stages: (1) It employs a dedicated spatial layer to systematically eliminate weakly correlated information, focusing on extracting and feeding more significantly correlated spatial data into the LSTM network. (2) Given that conventional Artificial Neural Network (ANN) models are unable to consider the impact of the temporal dimension on interpolation, and unidirectional LSTM models can only factor in past moments' influence without utilizing future moment information, this research adopts a Bidirectional LSTM (BiLSTM) architecture. The BiLSTM inherently captures both spatial and temporal dependencies, thereby overcoming previous limitations. (3) To further enhance its performance by efficiently extracting comprehensive global spatial features while maintaining the advantages of bidirectional modeling offered by BiLSTM, we integrate a self-attention mechanism into the BiLSTM framework. This results in a novel, fused Bidirectional LSTM Interpolation Model with Spatial Layer-Self Attention (SL-BiLSTM-SA). In the experimental phase, the SL-BiLSTM-SA model is rigorously applied to a PM_2.5 concentration dataset from Shandong Province to conduct a meticulous investigation into its interpolation capabilities. Upon comparative analysis against other models, it is evident that the SL-BiLSTM-SA model outperforms with notably lower error metrics, demonstrating substantial improvements in accuracy—by 39.83% and 36.63% when compared to Spatio-Temporal Ordinary Kriging (STOK) and Genetic Algorithm-optimized Spatio-Temporal Kriging (GA-STK) methods, respectively. Moreover, our model exhibits commendable precision in forecasting high and low concentration levels. By seamlessly integrating spatial information and coupling the strengths of BiLSTM with self-attention mechanisms, this research not only extends the suite of interpolation methods for spatiotemporal data analysis but also furnishes robust theoretical underpinnings and methodological support to facilitate sophisticated spatiotemporal data analyses.

[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] 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.

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

[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.

Scientific knowledge of the spatio-temporal evolution processes and formation mechanisms of territorial space in countries along the Central Asia-West Asia Economic Corridor holds significant scientific value and practical importance for supporting the current "Going Global" strategy and the "Belt and Road" initiative. Based on the dominant functions of the territories, the Central Asia-West Asia Economic Corridor is divided into three major types of territorial space: urban and rural construction, agricultural production, and ecological protection. A long-term analysis base map of territorial space from 2002 to 2022 was constructed by integrating multi-source spatio-temporal data. The spatio-temporal cube model was employed to depict the spatio-temporal evolution processes and typical patterns, while the integrated spatial transformation intensity model analyzed the characteristics of spatial structural transformation across three dimensions: scale, location, and intensity. The VIVI-SHAP framework of an interpretable machine learning model was used to analyze the evolution mechanisms, focusing on the importance of driving factors, interaction intensity, and non-linear dependencies. The results show that: (1) Approximately 6.14% of the territorial space in countries along the corridor underwent structural transformation over the past 20 years. The proportion of urban and rural construction space, though small, increased steadily by 0.17%, while agricultural production space decreased by 19.04% overall, with significant structural changes within the ecological protection space. (2) The dynamic interchange between green and other ecological spaces within the ecological protection space is predominant, with a systematic tendency for green ecological space to convert into agricultural production space, while the main source of urban and rural construction space expansion was green ecological space, accounting for 56.36% of the total converted area. 3) The territorial spatial pattern of the corridor is shaped by multiple processes of territorial space transformation, each with different magnitudes, intensities, and driving mechanisms. Natural geographic factors and transportation location factors played decisive roles, while the global influence of population growth and socio-economic development on territorial space structural transformation was less pronounced. This study provides new perspectives and methods to reveal the patterns and mechanisms of changes in land spatial types in the Central Asia-West Asia region. It further provides data support for decision-making departments to formulate reasonable land spatial planning, and demonstrates its application value in achieving greater spatial comprehensive benefits and promoting coordinated regional economic development.

In the transition of urban taxi fleets toward electrification in large cities, the charging demand of taxis exhibits characteristics of high charging load and strong spatiotemporal randomness, with a noticeable mismatch between charging supply and demand in time and space. To accurately estimate the potential charging demand post- the full electrification of the taxi fleet, this study introduces a bottom-up conceptual framework and a binary tree algorithm based on trajectory map matching, leveraging fuel taxi trajectory data exclusively. This approach provides a new paradigm for estimating charging demands in regions lagging behind in the electrification transition. The model and algorithm are validated using trajectory data from 890 electric taxis with battery status fields (State of Charge, SOC). Results show that the estimation errors of indicators such as the number of charging segments and charging amount are less than 6.5%. Moreover, the model also exhibits high spatiotemporal distribution estimation accuracy under different parameter settings (battery depletion threshold θ) and spatial scales (with grid sizes of 500 m, 1 000 m, and 10 000 m), ensuring their applicability in real-world scenarios. Specifically, the temporal distribution error of charging amount is less than 8.5% in the best-case scenario, and over half of the charging amount within 500 m grids has a spatial distribution error less than 0.3, with 59% of the 500 m grids having an estimated error of charging segment count less than 0.3. Building upon this, the Singular Value Decomposition (SVD) algorithm is used to decompose and reduce the dimensionality of the spatiotemporal matrix of charging demands, identifying spatiotemporal patterns of potential charging demands within the real road network of Beijing's Sixth Ring Road at road level. Finally, a case study is conducted using trajectory data from 1 913 taxis in Beijing over three consecutive days from March 9th (Monday) to March 11th (Wednesday) in 2019, and the results indicate that the spatial distribution of potential charging demands for taxis in Beijing exhibits prominent clustering features in key areas and critical corridors, corresponding to high-density charging demands associated with residents' high activity levels and long-distance travel. The decomposed charging demands reveal a spatiotemporal structural pattern dominated by regular charging demands, with supplementary heterogeneity in charging demands between morning and afternoon, as well as during working and non-working hours. This analysis method assists in uncovering the spatial distribution structural characteristics of potential charging demand and spatiotemporal coupling relationships, providing decision-making references for long-term planning of charging infrastructure, grid load scheduling, and charging demand management in the electrification transformation of taxi fleets.

Many studies have demonsrated that nighttime light intensity has a strong correlation with various social parameters. Some social parameters are only correlated with the nighttime light intensity within a specific range of radiation intensity. However, traditional nighttime light index is established based on the total range of radiation intensity from nighttime light remote sensing images, which limits the application potential of the nighttime light remote sensing data. In this study, we proposed a method for constructing nighttime light index based on specific light radiation intensity ranges. The nighttime light index was calculated based on different light radiation sub-intervals. For each social parameter, an optimal sub-interval was determined when the nighttime light index on the sub-interval showed the strongest correlation with the social parameter. Based on the NPP-VIIRS nighttime light data from 2012 to 2020, the total light radiation values of 5050 light radiation intensity intervals in all provinces and 36 main cities of China were calculated. We conducted correlation analysis between these light radiation values and 39 and 24 social parameters at provincial and municipal spatial scales, respectively. The optimal light radiation intensity intervals for all social parameter were determined, except for one social parameter at provincial and two social parameters at municipal scales because of the failure to pass the significance test. Compared with the traditional total light radiation values from the total radiation interval, total light radiation values from the optimal light radiation intensity intervals showed stronger correlation with various social parameters at provincial and municipal scales. The average correlation coefficient was increased by 0.06 at provincial scale and 0.08 at municipal scale. Some social parameters that were not significantly correlated with the traditional total light radiation showed strong correlation with the total light radiation from optimal light radiation intensity intervals. The optimal light radiation intensity intervals for most social parameters were relatively stable with little fluctuation over time. The optimal light radiation intensity intervals for social parameters in different industries showed obvious differences, which provided insights for a more in-depth analysis of the relationship between social parameters and nighttime light remote sensing data and a better evaluation of the impact of background noise of nighttime light remote sensing data on social parameter analysis. The construction of nighttime light index considering light radiation intensity range can improve the application potential of nighttime light remote sensing in the field of social parameter research.

The continuous and frequent urban flooding presents a formidable impediment to social and economic progress, imperiling community welfare. A critical aspect of urban flood emergency management involves enhancing the efficiency of drainage in rescue and relief operations. In this study, we combined diverse datasets encompassing the spatial distribution of urban rescue units, urban flooding occurrences, traffic dynamic flows, and traffic signal placements. We devised a multi-scenario simulation model for urban flood emergency response employing multi-agent based model guided by a "information integration - simulation modeling - emergency scenarios - efficiency comparison - optimization scheme". The model embedded Dijkstra algorithm to solve the optimal path for rescue and optimized the spatial layout of rescue points of rescue units with K-means algorithm. Through simulation experiments, we investigated the interaction between behavioral subjects and geographic environments, wherein rescue units navigate waterlogged areas, considering traffic constraints. The culmination of rescue and relief efforts is deemed achieved upon reducing water depth at all inundated points below the 15cm threshold. By formulating scenarios mirroring morning and evening peaks, diverse rescue levels, and rescue combination, we dissected the influence mechanisms of varied factors on rescue efficiency. Focused on the Liangshui River basin in Beijing, against the backdrop of a century-extreme rainfall event, our study scrutinized diverse emergency rescue scenarios, analyzed factors affecting rescue efficiency, and proffered optimization strategies for rescue unit layouts, to explore the path for the enhancement of rescue and relief efficiency. Our findings proposed optimized strategies for rescue unit deployment, advocating a spatial layout scheme emphasizing "global dispersion and local aggregation". Implementation of this scheme yielded substantial efficiency improvements by 18.27%, 18.24%, and 10.34% during morning and evening peak scenarios, varying rescue levels, and different rescue compositions, respectively. In addition, the efficiency of rescue during off-peak hours was significantly higher than that during peak hours. Moreover, we underscored the pivotal role of rescue personnel efficiency in dictating overall rescue efficacy, observing nonlinear, and accelerated efficiency declines with diminishing rescue personnel levels. Depending on the road conditions, there was uncertainty in the rescue efficiency of both joint rescues and individual stationary teams. Hence, within the realm of practical rescue operations, it is imperative to adeptly tailor rescue strategies to the nuanced dynamics of each scenario, encompassing variables such as rainfall intensity, traffic congestion, resource availability, and other pertinent factors. These adaptive measures ensure a nimble response that optimally addresses the evolving exigencies of urban flooding emergencies. Notably, our model attained a commendable stability rate of 93.85%. This study offers strategic recommendations for enhancing emergency rescue efficiency and assuaging the societal ramifications of urban flood risk, as well as scientific insights for urban flood emergency management.

Enhancing and sustaining urban competitiveness is contingent upon the presence of urban vitality. Urban planners and managers face increasing pressure to find more accurate and logical ways to manage urban development due to the growing challenges associated with municipal government. This study focuses on the central region of Nanjing. A detailed framework for evaluating urban vitality is proposed from three perspectives, human activities, network interactions, and the physical environment. This framework uses foundational road networks and building footprints from the World Map, Baidu heatmap, Dianping restaurant data, social media check-in data, Baidu Map POI, and innovation data. To create a comprehensive vitality evaluation framework, nine urban vitality indicators were reduced in dimensionality using the real-coded accelerated genetic algorithm based on the Projection Pursuit Model (RAGA-PPM). An analysis was also conducted on the differences with EWM and the spatial distribution patterns of both unidimensional and comprehensive vitality in Nanjing. The conclusions can be divided into three parts. First, the spatial distribution pattern of vitality in Nanjing's central urban area is successfully reflected by the comprehensive evaluation technique based on multi-source big data. The validity of the proposed evaluation system was confirmed by analyzing vitality cluster sample locations. Second, similar spatial features may be seen in Nanjing's unidimensional vitality, revealing a monocentric urban structure, with high-value areas gradually decreasing outward from the Xinjiekou commercial district. Commercial districts and metro stations are the focal points of population activity vitality, with each district exhibiting strong central values and secondary vitality clusters. Urban vitality values decline, with the Xinjiekou commercial area and Nanjing South Station serving as hubs of network interaction vitality. Urban vitality ratings decrease concentrically, with the Xinjiekou commercial area and Nanjing South Station serving as the hubs of network interaction vitality. Physical building vitality is geographically scattered, with high and relatively high values spread across most areas. Third, unidimensional vitality is not unfamiliar to comprehensive vitality. Additional viable centers for vitality were identified, with each district having a vitality hub. Xuanwu, Gulou, Jianye, and Qinhuai districts, which comprise the old city, form the core of Nanjing's vibrancy and serve as significant hubs. Liuhe and Yuhuatai districts have the lowest vitality, while Xuanwu and Qinhuai districts show the highest vitality. Most districts with above-average comprehensive vitality scores are located near transportation hubs, university areas, industrial parks, pedestrian streets, and commercial centers. According to the study, urban designers may benefit from a more thorough and multifaceted understanding of urban vitality patterns.

As an important transportation infrastructure, the timely updating of road network data is of great significance in the fields of traffic management, emergency response, and urban planning. Road network matching that determines the correspondence between the features of road network data from different sources serves this purpose. It also provides technical support for tasks such as the quality assessment of crowdsourced road network data, which has attracted a lot of attention in the field of geographic information. However, traditional road network matching methods mainly measure the similarity of road network structure through the geometric and topological attributes of road network data to determine the matching relationship of road network elements. Such methods with manually designed features and thresholds are easily limited by experts' experience, which degrades their performance under complex road network structures. In recent years, road network data modeling based on graph neural networks has become a research hotspot and has achieved excellent performance in several road network modeling tasks. However, most of the existing methods use direct neighborhood aggregation on the graph topology to learn the embedded representation of the road network structure, without considering the spatial relationship of road network features in this key step, and failing to make full use of the representation learning capability of graph neural networks. For this reason, this study proposes an improved neighborhood aggregation that performs a spatially explicit graph-based embedding learning method for road network matching. First, a road graph model of the road network data is constructed, and geometric, semantic, and location features are extracted. Then, based on the GraphSAGE framework, three kinds of neighborhood aggregation operators (i.e., spatial, classified, and hybrid) are proposed, and the computation of spatial relationships and attribute types of road network features is introduced in the neighborhood aggregation operations. Finally, the similarity of graph node embedding is utilized to determine the matching relationship of road network features. To verify the effectiveness of the proposed method, extensive experiments are carried out using real-world road network data. The proposed method achieves the optimal performance in all metrics on the test data of the study region, which improves the matching correctness rate by more than 11% and the recall rate by more than 6.8% compared to the baseline graph neural network method. Furthermore, the road network graph embedding features are analyzed from the aspects of graph embedding structure and embedded road network structure, which helps explore the role of improved neighborhood aggregation on the graph embedding representation capability and provides a new perspective for further improving the graph neural network road network modeling.

Soil salinization is a significant issue that not only leads to ecological problems like land desertification but also poses a threat to food security by reducing the quantity and quality of arable land. Therefore, it is crucial to rapidly and accurately obtain information about soil salinization for timely and effective soil management. In recent years, the development of microwave remote sensing has provided new methods for large-scale and rapid monitoring of soil salinization, with Synthetic Aperture Radar (SAR) data-based soil salinization monitoring becoming a hotspot in remote sensing research. Recent advancements in SAR remote sensing for soil salinization monitoring can be summarized in four main aspects: (1) Microwave scattering mechanism of saline soil: Research has clarified the correlation between soil salinity and radar backscattering coefficients, providing a basis for estimating soil salinity using SAR data. Understanding the microwave scattering mechanism of saline soil is essential for accurately interpreting SAR data and extracting meaningful information about soil salinization. (2) Construction and extraction of characteristic parameters of saline soil: The construction and extraction of characteristic parameters of saline soil have shown a trend towards diversification and integration. Various parameters, such as backscattering coefficients, polarization ratios, and texture features, are used to characterize the properties of saline soil. By utilizing a combination of these parameters, researchers can obtain a more comprehensive understanding of soil salinization. (3) Classification of saline soil: The classification methods for saline soil have shifted from traditional machine learning to deep learning methods. Deep learning algorithms, such as convolutional neural networks, have shown promising results in accurately classifying saline soil areas. These advanced techniques enable the identification and mapping of different levels of soil salinity, aiding in effective soil management strategies. (4) Inversion of soil salinity: The inversion of soil salinity has transitioned from regression analysis to inversion methods that combine scattering mechanisms. By considering the microwave scattering mechanisms and using multiple data sources, more accurate estimations of soil salinity can be obtained. This approach allows for a better understanding of the spatial distribution and variability of soil salinity, facilitating targeted interventions and management practices. Despite these advancements, there are still challenges and issues in the current research on soil salinization monitoring using SAR remote sensing. Some of these challenges include the influence of multiple factors on the relationship between soil salinity and backscattering coefficients, the need for further analysis of mechanisms, the construction of characteristic parameters, and the fusion of multi-source data for achieving high-precision soil salinization monitoring using SAR remote sensing.

Low-altitude space is an important component of urban space. Requisite measures for precise and meticulous management of urban low-altitude are indispensable. The urban low-altitude space should be characterized by spatial coordinates and possess significant geographic attributes. With the increasing adoption of low-altitude applications in urban areas, the intricate utilization of space, represented by low-altitude traffic, has transcended conventional airspace boundaries and encroached upon near-ground urban space, exerting an impact on urban architecture and human settlement environment. Emphasizing the 3D land space's utilization and service, the issue of the utilization and management of 3D land space has become increasingly conspicuous. The land parcel serves as the fundamental unit for urban land management, recording information of ownership relationships, spatial rights, and interests information. The establishment of easements between land parcels ensures the lawful utilization of relevant spaces. Given the inclusion of low-altitude flight activities within the purview of urban land management, there is an urgent imperative to elucidate the spatial utilization and impact of low-altitude passage processes on land space while establishing the service rights of urban low-altitude passage to safeguard parcel interests. However, the concept of easement is limited by the cognitive constraints imposed by a 2D land plane, neglecting the modeling and representation of 3D space. Precisely articulating the easement relationship formed by low-altitude passage activities in urban low-altitude space poses a significant challenge. Utilizing GIS technology for modeling urban spaces and facilitating the characterization and mapping from the physical to digital realms has consistently served as a crucial information tool in urban space management. Drawing upon the core principles of GIS modeling methodology and conceptual modeling, this paper presents a conceptual approach for describing low-altitude passage easement in urban areas. By analyzing the movement conditions of aircraft in urban low-altitude traffic, considering the impact on human settlement rights caused by overflight, and examining the path utilization of aircraft in three-dimensional space, we develop a constrained spatial modeling for low-altitude passage easement as a geometric description of the conceptual model. By integrating the spatial characteristics of 3D land parcels, we integrate the supply and service conditions of these parcels in terms of their space utilization, aircraft takeoff and landing modes, and path utilization. As a result, we propose a comprehensive supply and service model to address the demand for traffic within 3D parcels. The semantic relationship among low-altitude planning, space value, and space easement is established by extracting the concept of low-altitude access from existing research and regulations, thereby forming a conceptual model. Finally, we conduct an experimental demonstration using a logistics transportation case study in Shenzhen to instantiate the model and achieve 3D visualization. The findings demonstrate that the integrated approach of "space utilization conditions-data modeling-visual expression", implemented around the conceptual model, effectively describes urban low-altitude passage easement and conveys the equitable relationship of urban land space in low-altitude application scenarios, thereby providing valuable support for urban low-altitude management.

The essence of geospatial knowledge lies in unveiling the spatiotemporal distribution, dynamics of change, and interaction patterns of geographical entities and phenomena. However, existing knowledge base management platforms often overlook the specific needs of geospatial knowledge representation and lack the capability to handle the unique attributes of geospatial data, making it challenging to meet the requirements for constructing and applying geospatial knowledge graphs. The Geospatial Knowledge Base Management System (GeoKGMS) is designed on the basis of an integrated geospatial knowledge base engine that efficiently aggregates geospatial knowledge resources across various modalities—'Image-Text-Number'—automates the construction of geospatial knowledge graphs, and facilitates a one-stop geospatial knowledge engineering process. This paper elucidates four key technologies for managing geospatial knowledge bases. First, the cloud-native geospatial knowledge base microservice unified scheduling technology decomposes the large geospatial knowledge base management system into fine-grained, independently operable, and deployable microservices. By comprehensively managing the lifecycle of the geospatial knowledge base, service classification and orchestration methods are determined to achieve unified scheduling of these microservices. Second, a human-computer collaborative geospatial knowledge graph construction method is proposed, supporting the sustainable, collaborative construction of geospatial knowledge graph engineering. Third, the spatiotemporal hybrid encoding technology of the geospatial knowledge graph achieves unified representation of geospatial knowledge by integrating multimodal geospatial data and spatiotemporal information. Fourth, a multimodal geospatial knowledge integrated storage and large-scale spatiotemporal graph partitioning technology is proposed to address the challenges of efficiently managing complex structured geospatial knowledge and retrieving large-scale spatiotemporal knowledge tuples. Based on these key technologies, an application service framework for GeoKGMS has been designed, featuring six functional modules: geospatial knowledge base management, multimodal geospatial knowledge extraction, human-computer collaborative construction of geospatial knowledge graphs, geospatial knowledge reasoning, geospatial knowledge graph quality assessment, and geospatial knowledge visualization. To demonstrate GeoKGMS's capabilities, the Karst landform knowledge graph is used as a case study. The Karst landform knowledge graph is an integrated 'Image-Text-Number' geospatial knowledge graph, constructed based on geospatial knowledge extracted from the texts, schematic diagrams, and related maps in geomorphology textbooks. Through a collaborative pipeline, geomorphology experts and computers jointly perform tasks such as mapping, alignment, supplementation, and conflict resolution of geospatial knowledge. This collaboration ultimately leads to the automated construction of the Karst landform knowledge graph by GeoKGMS. The resulting graph is highly consistent with expert knowledge models, ensuring the interpretability of knowledge-driven geocomputation and reasoning in practical applications.

Due to the imbalanced regional development, data scarcity exists in some regions, which to some extent restricts the progress of spatial prediction research. The introduction of cross-area knowledge transfer offers a valuable method for mitigating the impact of data scarcity in areas with limited samples and for conducting spatial prediction. With technological advancements, spatial prediction methods based on transfer learning and the Third Law of Geography have become mainstream in the fields of computer science and geography. Transfer learning techniques leverage knowledge from a source domain with abundant data to solve related tasks in a target domain with limited data. Meanwhile, the proposal and application of the Third Law of Geography show that by comparing the similarity of geographical environmental variables between sampled regions and unsampled regions (rather than relying solely on traditional spatial distance or quantitative relationships), it is possible to predict target information in unsampled regions using a small amount of sample data. This provides a theoretical basis and methodological reference for selecting the source domain and target domain in cross-regional knowledge transfer. This paper conducts a literature review of cross-regional spatial prediction research based on these two major methods since 2018, focusing on the following key tasks: (1) Comparing and analyzing the basic principles of spatial prediction based on geographical similarity and transfer learning, and identifying differences in their technical procedures; (2) Summarizing the differences in similarity representation indicators and measurement methods between the two approaches; (3) Examining differences in commonly used auxiliary data, spatial analysis units, modeling methods, and evaluation indicators between the two prediction methods; (4) Discussing the challenges and limitations faced by these cross-regional knowledge transfer methods. The study shows that while the technical principles of both methods are basically consistent, they have specific limitations regarding their scope of application, similarity representation and measurement, relevant auxiliary variables, and parameter selection. The research offers useful insights for optimizing and improving these methods, integrating them effectively, innovating cross-regional prediction approaches, and expanding their application fields.