摘要：The emergence of crowdsourced geographic information (CGI) has markedly accelerated the evolution of land-use and land-cover (LULC

本次给大家整理的是《International Journal of Geographical Information Science》杂志2024年第38卷第11期的论文的题目和摘要，一共包括9篇SCI论文！

论文1

A review of crowdsourced geographic information for land-use and land-cover mapping: current progress and challenges

通过众包地理信息进行土地利用和土地覆盖制图的综述：当前进展与挑战

【摘要】The emergence of crowdsourced geographic information (CGI) has markedly accelerated the evolution of land-use and land-cover (LULC) mapping. This approach taps into the collective power of the public to share spatial information, providing a relevant data source for producing LULC maps. Through the analysis of 262 papers published from 2012 to 2023, this work provides a comprehensive overview of the field, including prominent researchers, key areas of study, major CGI data sources, mapping methods, and the scope of LULC research. Additionally, it evaluates the pros and cons of various data sources and mapping methods. The findings reveal that while applying CGI with LULC labels is a common way by using spatial analysis, it is limited by incomplete CGI coverage and other data quality issues. In contrast, extracting semantic features from CGI for LULC interpretation often requires integrating multiple CGI datasets and remote sensing imagery, alongside advanced methods such as ensemble and deep learning. The paper also delves into the challenges posed by the quality of CGI data in LULC mapping and explores the promising potential of introducing large language models to overcome these hurdles.

【摘要翻译】众包地理信息（CGI）的出现显著加速了土地利用和土地覆盖（LULC）制图的发展。这种方法利用公众分享空间信息的集体力量，为生产LULC地图提供了相关的数据来源。通过分析2012年至2023年间发表的262篇论文，本研究全面概述了该领域，包括杰出研究者、主要研究领域、主要CGI数据来源、制图方法以及LULC研究的范围。此外，还评估了各种数据来源和制图方法的优缺点。研究结果表明，虽然使用LULC标签应用CGI是通过空间分析的常见方法，但它受到CGI覆盖不完整和其他数据质量问题的限制。相比之下，从CGI中提取语义特征以进行LULC解释通常需要整合多个CGI数据集和遥感影像，并结合集成学习和深度学习等先进方法。论文还探讨了CGI数据在LULC制图中的质量挑战，并探讨引入大型语言模型以克服这些障碍的潜在前景。

【doi】https://doi.org/10.1080/13658816.2024.2353695

【作者信息】Hao Wu, 湖北省地理过程分析与模拟重点实验室，华中师范大学，中国武汉；城市与环境科学学院，华中师范大学，中国武汉Yan Li, 湖北省地理过程分析与模拟重点实验室，华中师范大学，中国武汉；城市与环境科学学院，华中师范大学，中国武汉Anqi Lin, 湖北省地理过程分析与模拟重点实验室，华中师范大学，中国武汉；城市与环境科学学院，华中师范大学，中国武汉，linanqi@mails.ccnu.edu.cnHongchao Fan,挪威科技大学，土木与环境工程系，挪威特隆赫姆，TorgardenKaixuan Fan, 湖北省地理过程分析与模拟重点实验室，华中师范大学，中国武汉；城市与环境科学学院，华中师范大学，中国武汉Junyang Xie，湖北省地理过程分析与模拟重点实验室，华中师范大学，中国武汉；城市与环境科学学院，华中师范大学，中国武汉Wenting Luo，湖北省地理过程分析与模拟重点实验室，华中师范大学，中国武汉；城市与环境科学学院，华中师范大学，中国武汉

论文2

Spatial ensemble learning for predicting the potential geographical distribution of invasive species

用于预测入侵物种潜在地理分布的空间集成学习

【摘要】Understanding the geographical distribution of invasive species is beneficial for preventing and controlling biological invasions. A global model is often constructed with existing species distribution models (SDMs) to describe the relationships between environmental characteristics and species distributions. Because of the spatial variations in environmental characteristics, it may be difficult for a single SDM to obtain an accurate result in any given location or area. Therefore, a spatial ensemble learning method for predicting the potential geographical distribution of invasive species is presented in this study. The method mainly includes two types of learners: one learner is a base learner used to predict the geographical distribution of invasive species, and the other learner is a spatial ensemble learner for combining predictions from different base learners. In this research, spatial ensemble learning is used to predict the geographical distribution of Erigeron annuus in the Yangtze River Economic Belt, China. The kappa coefficient and AUC (area under the receiver operating characteristic curve) obtained with the spatial ensemble learner are 0.88 and 0.94, respectively, and these values are greater than those obtained using three base learners and other ensemble strategies. This demonstrates the feasibility and effectiveness of spatial ensemble learning.

【摘要翻译】理解入侵物种的地理分布有助于防止和控制生物入侵。通常构建一个全球模型，利用现有的物种分布模型（SDMs）来描述环境特征与物种分布之间的关系。由于环境特征的空间变化，单一的物种分布模型在任何给定地点或区域内可能难以获得准确的结果。因此，本研究提出了一种用于预测入侵物种潜在地理分布的空间集成学习方法。该方法主要包括两种类型的学习器：一种是基础学习器，用于预测入侵物种的地理分布，另一种是空间集成学习器，用于结合不同基础学习器的预测。在本研究中，空间集成学习用于预测中国长江经济带中千里光（Erigeron annuus）的地理分布。空间集成学习器获得的卡帕系数和AUC（受试者工作特征曲线下面积）分别为0.88和0.94，这些值均高于使用三种基础学习器和其他集成策略所获得的值。这证明了空间集成学习的可行性和有效性。

【doi】https://doi.org/10.1080/13658816.2024.2376325

【作者信息】Wentao Yang,中国湘潭，湖南科技大学国家地方联合工程实验室，地理空间信息技术Xiafan Wan，中国湘潭，湖南科技大学国家地方联合工程实验室Min Deng，中国长沙，中南大学地球科学与信息物理学院

论文3

Learning spatial interaction representation with heterogeneous graph convolutional networks for urban land-use inference

利用异构图卷积网络学习空间交互表示进行城市土地利用推断

【摘要】Urban land use is central to urban planning. With the emergence of urban big data and advances in deep learning methods, several studies have leveraged graph convolutional networks (GCNs) with local functional characteristics from points of interest data and spatial features from flow data to infer urban land use. However, these studies cannot distinguish spatial interaction and spatial dependence in terms of conceptualization and modeling mechanisms and overlook the inadequacy of GCNs in modeling spatial interaction. This study proposes a novel framework—a heterogeneous graph convolutional network (HGCN)—to explicitly account for the spatial demand and supply components embedded in spatial interaction data. Several experiments, including 19 different models and datasets from Shenzhen and London, were conducted to validate the proposed framework and its generalizability within the same and different spatial contexts. The HGCN can distinguish heterogeneous mechanisms in supply- and demand-related modalities of spatial interactions, incorporating both spatial interaction and spatial dependence for urban land-use inference. Empowered by HGCN, we found that spatial interaction features play a distinctively crucial role in urban land-use inference compared to local attributes and spatial dependence features. In addition, our findings highlight the superiority of HGCN-based models in boosting performance and enhancing model transferability.

【摘要翻译】城市土地利用是城市规划的核心。随着城市大数据的出现和深度学习方法的发展，一些研究利用图卷积网络（GCNs）结合兴趣点数据中的局部功能特征和流量数据中的空间特征来推断城市土地利用。然而，这些研究在概念化和建模机制方面无法区分空间交互和空间依赖，且忽视了GCNs在建模空间交互方面的不足。本研究提出了一种新颖的框架——异构图卷积网络（HGCN），以明确考虑嵌入在空间交互数据中的空间需求和供给组件。我们进行了多项实验，包括来自深圳和伦敦的19种不同模型和数据集，以验证所提出框架的有效性及其在相同和不同空间背景下的通用性。HGCN能够区分与供给和需求相关的空间交互的异构机制，同时结合空间交互和空间依赖进行城市土地利用推断。通过HGCN的应用，我们发现，空间交互特征在城市土地利用推断中扮演了比局部属性和空间依赖特征更为重要的角色。此外，我们的研究结果强调了基于HGCN的模型在提升性能和增强模型可转移性方面的优势。

【doi】https://doi.org/10.1080/13658816.2024.2379473

【作者信息】Zhaoya Gong,北京大学深圳研究生院，城市规划与设计学院，中国深圳；中国自然资源部地表系统与人地关系重点实验室，北京大学深圳研究生院，中国深圳Chenglong Wang,北京大学深圳研究生院，城市规划与设计学院，中国深圳；中国自然资源部地表系统与人地关系重点实验室，北京大学深圳研究生院，中国深圳Yuting Chen,北京大学深圳研究生院，城市规划与设计学院，中国深圳；中国自然资源部地表系统与人地关系重点实验室，北京大学深圳研究生院，中国深圳Bin Liu, 北京大学深圳研究生院，城市规划与设计学院，中国深圳；中国自然资源部地表系统与人地关系重点实验室，北京大学深圳研究生院，中国深圳Pengjun Zhao，北京大学深圳研究生院，城市规划与设计学院，中国深圳；中国自然资源部地表系统与人地关系重点实验室，北京大学深圳研究生院，中国深圳Zhengzi Zhou，北京大学深圳研究生院，城市规划与设计学院，中国深圳；中国自然资源部地表系统与人地关系重点实验室，北京大学深圳研究生院，中国深圳

论文4

A local encryption method for large-scale vector maps based on spatial hierarchical index and 4D hyperchaotic system

基于空间层次索引和四维超混沌系统的大规模矢量地图局部加密方法

【摘要】With the development of geographic information services, applications based on map data are increasing. Internet and mobile communication technologies have brought great convenience to the distribution, sharing, and acquisition of map data. Unfortunately, it also brings great challenges to protect the security and privacy of confidential map data. Although several encryption algorithms have been developed for map data, most of the existing methods are simple extensions of classical encryption algorithms for text data in cryptography, ignoring special structures and characteristics of geospatial data. Existing methods mainly take the entire map or layers as encryption units, which makes the encryption efficiency for large-scale map data low and limits the ability to perform local and incremental encryption for local areas, making it still challenging to meet the needs of map data in applications, such as autonomous driving. To solve the limitations, a local encryption approach for large-scale vector map data is proposed in this paper considering the structure characteristics of map data. Experiments on map data of different types (including points, lines, and polygons) demonstrate the effectiveness of the proposed method. With local and parallel encryption strategies, the proposed method has higher efficiency compared to available algorithms for large-scale map data.

【摘要翻译】随着地理信息服务的发展，基于地图数据的应用日益增多。互联网和移动通信技术为地图数据的分发、共享和获取带来了极大便利。然而，这也给保护机密地图数据的安全性和隐私性带来了巨大挑战。尽管已有若干加密算法针对地图数据进行开发，但现有大多数方法仅仅是对文本数据的经典加密算法的简单扩展，忽略了地理空间数据的特殊结构和特性。现有方法主要将整个地图或图层作为加密单元，这使得大规模地图数据的加密效率较低，并限制了对局部区域进行本地和增量加密的能力，这仍然难以满足自动驾驶等应用对地图数据的需求。为了解决这些局限性，本文提出了一种考虑地图数据结构特征的大规模矢量地图数据本地加密方法。对不同类型的地图数据（包括点、线和多边形）的实验验证了所提方法的有效性。通过本地和并行加密策略，所提出的方法在大规模地图数据上的效率高于现有算法。

【doi】https://doi.org/10.1080/13658816.2024.2381225

【作者信息】Chen Ding,中南大学地理信息系，中国长沙Jianbo Tang,中南大学地理信息系，中国长沙；湖南省地理空间信息工程技术研究中心，中国长沙Min Deng,中南大学地理信息系，中国长沙；湖南省地理空间信息工程技术研究中心，中国长沙；江西师范大学地理与环境学院，中国南昌Huimin Liu，中南大学地理信息系，中国长沙；湖南省地理空间信息工程技术研究中心，中国长沙Xiaoming Mei，中南大学地理信息系，中国长沙

论文5

Exploring geospatial digital twins: a novel panorama-based method with enhanced representation of virtual geographic scenes in Virtual Reality (VR)

探索地理空间数字双胞胎：一种基于全景的新方法，增强虚拟现实（VR）中虚拟地理场景的表现

【摘要】An important step in implementing geospatial digital twins is to enhance the expressiveness of virtual geographical scenes for the physical world. However, the existing virtual geographical scenes cannot quickly express the dynamically changing geographic environment for remote users due to the inefficient handling of modeling processes, user perception, and remote sharing. The research analysed the concept and characteristics of geospatial digital twins, and constructed the virtual geographical scene ontology, based on which we developed geographical spatiotemporal semantic rules and designed a dynamic annotation algorithm to enhance the representation of virtual geographical scenes. Finally, we investigated a real-time transmission method of panoramic video based on 5 G and used immersive virtual reality (IVR) to realize the user experience of remote immersion in geographical scenes. We selected a specific geographic environment containing multiple typical geographic entities to develop three prototype systems for experimental analyses. The results showed that the proposed method enabled users to view the virtual geographical scene on a VR device. The average latency for this process was 14.72 seconds. Compared with the virtual geographical scenes constructed by traditional methods, the experiments showed the proposed method advantageous in comprehensiveness, timeliness, and photorealism and abilities to enhance the user’s geographical scene perception.

【摘要翻译】在实施地理空间数字孪生技术时，增强虚拟地理场景对物理世界的表达能力是一个重要步骤。然而，现有的虚拟地理场景由于建模过程、用户感知和远程共享的处理效率较低，无法快速表达动态变化的地理环境。该研究分析了地理空间数字孪生的概念和特点，并构建了虚拟地理场景本体，在此基础上开发了地理时空语义规则，并设计了动态标注算法以增强虚拟地理场景的表示能力。最后，我们研究了一种基于5G的全景视频实时传输方法，并利用沉浸式虚拟现实（IVR）技术实现了用户远程沉浸式体验地理场景。我们选择了包含多个典型地理实体的特定地理环境，开发了三个原型系统用于实验分析。结果表明，所提出的方法使用户能够在VR设备上查看虚拟地理场景，平均延迟时间为14.72秒。与传统方法构建的虚拟地理场景相比，实验表明该方法在全面性、时效性、真实感以及增强用户对地理场景的感知能力方面具有优势。

【doi】https://doi.org/10.1080/13658816.2024.2386064

【作者信息】Jinbin Zhang,地球科学与工程学院，西南交通大学，中国成都Jun Zhu,地球科学与工程学院，西南交通大学，中国成都Qing Zhu,地球科学与工程学院，西南交通大学，中国成都Jianlin Wu,地球科学与工程学院，西南交通大学，中国成都Yukun Guo,地球科学与工程学院，西南交通大学，中国成都Pei Dang,地球科学与工程学院，西南交通大学，中国成都Weilian Li,地球科学与工程学院，西南交通大学，中国成都Heng Zhang，中国铁路设计集团有限公司，中国天津

论文6

NLA-GCL-Net: semantic segmentation of large-scale surveying point clouds based on neighborhood label aggregation (NLA) and global context learning (GCL)

NLA-GCL-Net：基于邻域标签聚合（NLA）和全局上下文学习（GCL）的大规模测量点云语义分割

【摘要】For large-scale 3D point clouds from surveying, the RandLA-Net semantic segmentation network model is unable to learn global context features efficiently during training, leading to suboptimal feature acquisition of globally related features. Furthermore, inconsistent predictions are made in the vicinity of data boundaries due to the oversimplified usage of multi-layer perceptrons (MLPs) and non-linear activation functions following decoding, which has a detrimental impact on both model training and performance. This study provides two solutions to address these issues: a neighborhood label aggregation (NLA) classifier that aggregates neighborhood information for segmentation tasks, and a global context learning (GCL) module that learns global volume-relative information to improve the semantic segmentation network. Overall accuracy (OA) obtained via experimental validation on large-scale SensatUrban and S3DIS datasets is 91.80% and 88.8%, respectively. The proposed model outperforms RandLA-Net by 3.8% and 3.0% in overall mean Intersection over Union (mIOU), respectively, significantly improving model performance and generalization abilities. This work offers new perspectives on how to effectively segment point cloud data.

【摘要翻译】对于大规模三维点云数据的语义分割任务，RandLA-Net 网络模型在训练过程中无法有效学习全局上下文特征，导致对全局相关特征的获取不足。此外，由于在解码阶段对多层感知器（MLP）和非线性激活函数的过度简化使用，模型在数据边界附近的预测结果不一致，影响了模型的训练和性能表现。为了解决这些问题，本文提出了两种解决方案：一种是邻域标签聚合（NLA）分类器，用于在分割任务中聚合邻域信息；另一种是全局上下文学习（GCL）模块，用于学习全局体积相关信息，从而提升语义分割网络的性能。在对大规模 SensatUrban 和 S3DIS 数据集进行实验验证后，整体精度（OA）分别达到91.80%和88.8%。与 RandLA-Net 相比，所提出的模型在总体平均交并比（mIOU）上分别提升了3.8%和3.0%，显著提高了模型的性能和泛化能力。本研究为有效分割点云数据提供了新的视角和方法。

【doi】https://doi.org/10.1080/13658816.2024.2382273

【作者信息】Jianhua Wang，山东建筑大学测绘与地理信息学院，中国济南Wenping Fan,山东建筑大学测绘与地理信息学院，中国济南Xueyan Song,山东建筑大学测绘与地理信息学院，中国济南Guobiao Yao,山东建筑大学测绘与地理信息学院，中国济南Mengmeng Bo，山东建筑大学测绘与地理信息学院，中国济南Ze Liu，山东建筑大学测绘与地理信息学院，中国济南

论文7

A new two-step estimation approach for retrieving surface urban heat island intensity and footprint based on urban-rural temperature gradients

基于城乡温度梯度的新双步估计方法，用于获取城市热岛强度及其影响范围

【摘要】Past decades have seen substantial efforts devoted to observing, assessing, and documenting the urban heat island (UHI) phenomenon. However, the discrepant criteria of non-urban references and ambiguous distinctions between urban and rural landscapes pose great challenges in measuring UHI magnitudes and spatial extents. This study goes beyond the conventional urban-rural dichotomy and introduces a new two-step approach based on the continuous transition of thermal environments along urban-rural gradients. The approach is applied to quantify Surface UHI (SUHI) intensities and footprints across 283 Chinese cities from 2005 to 2018 using multiple satellite-derived data sources. The results include: 1) The two-step approach avoids the limitations in subjective rural reference selections and provides reliable quantification of SUHI characteristics in various cities over time. 2) The SUHI footprints extracted by our approach are more reasonable than those obtained by two existing methods, with footprint ratios generally ranging within 0 − 6 times the urban area. 3) The two-step approach provides more concentrated estimates of SUHI intensity. Typically, ignoring heat sources in non-built-up areas can cause an overestimation of SUHI effect and misidentification of remote rural areas with high temperatures. Overall, the two-step approach enables more accurate estimates of SUHI effect, thereby facilitating policy-making for SUHI mitigation.

【摘要翻译】在过去的几十年里，研究人员为观察、评估和记录城市热岛（UHI）现象付出了大量努力。然而，非城市参照标准的差异性以及城乡景观之间模糊的区分，使得在测量UHI的强度和空间范围时面临重大挑战。本研究突破了传统的城市与农村二分法，提出了一种基于城乡梯度间热环境连续过渡的两步法新方法。该方法用于量化2005年至2018年间中国283个城市的地表城市热岛（SUHI）强度和足迹，使用了多种卫星遥感数据。研究结果如下：1）两步法避免了主观选择农村参照标准的局限性，为不同城市在不同时期的SUHI特征提供了可靠的量化手段。2）与现有的两种方法相比，该方法提取的SUHI足迹更为合理，足迹比率通常在城市面积的0-6倍范围内。3）两步法提供了更为集中的SUHI强度估计，忽略非建成区热源往往会导致SUHI效应的高估，并错误识别高温的偏远农村地区。总体而言，两步法能够更加准确地估算SUHI效应，从而为制定减缓SUHI的政策提供支持。

【doi】https://doi.org/10.1080/13658816.2024.2385435

【作者信息】Anqi Zhang，中国长沙中南大学建筑与艺术学院Chang Xia，中国长沙湖南大学公共管理学院

论文8

DCAI-CLUD: a data-centric framework for the construction of land-use datasets

DCAI-CLUD：构建土地利用数据集的数据驱动框架

【摘要】A high-quality land-use dataset is crucial for constructing a high-performance land-use classification model. Due to the complexity and spatial heterogeneity of land-use, the dataset construction process is inefficient and costly. This challenge affects the quality of datasets, consequently impacting the model’s performance. The emerging field of Data-Centric Artificial Intelligence (DCAI) is expected to deliver techniques for dataset optimization, offering a promising solution to the problem. Therefore, this study proposes a data-centric framework named DCAI-CLUD for the construction of land-use datasets. Based on this framework, the accuracy and rate of data labeling are improved by 5.93 and 28.97%. The Gini index of the dataset and the proportion of samples with non-mixed land-use categories are enhanced by 3.27 and 8.52%. The overall accuracy (OA) and Kappa of the land-use classification model improved significantly by 27.87 and 58.08%. This study is the first to introduce DCAI into the field of geographic information and remote sensing and verify its effectiveness. The proposed framework can effectively improve the construction efficiency and quality of the dataset and synchronously optimize the model performance. Based on the proposed framework, we constructed a multi-source land-use dataset of major cities in China named CN-MSLU-100K.

【摘要翻译】高质量的土地利用数据集对于构建高性能的土地利用分类模型至关重要。由于土地利用的复杂性和空间异质性，数据集的构建过程效率低下且成本高昂。这一挑战影响了数据集的质量，从而影响模型的表现。新兴的数据中心人工智能（DCAI）领域预计将提供数据集优化技术，提供了一个有前景的解决方案。因此，本研究提出了一种名为DCAI-CLUD的数据中心框架用于土地利用数据集的构建。基于此框架，数据标注的准确率和速率分别提高了5.93%和28.97%。数据集的基尼指数和非混合土地利用类别样本的比例分别提高了3.27%和8.52%。土地利用分类模型的总体准确率（OA）和Kappa显著提高了27.87%和58.08%。本研究首次将DCAI引入地理信息与遥感领域，并验证了其有效性。该框架能够有效提高数据集的构建效率和质量，并同步优化模型性能。基于该框架，我们构建了名为CN-MSLU-100K的中国主要城市多源土地利用数据集。

【doi】https://doi.org/10.1080/13658816.2024.2387200

【作者信息】Hao Wu，地理与信息工程学院，中国地质大学（武汉），中国湖北省武汉市Zhangwei Jiang，阿里巴巴集团，中国浙江省杭州市Anning Dong，地理与信息工程学院，中国地质大学（武汉），中国湖北省武汉市Ronghui Gao，地理与信息工程学院，中国地质大学（武汉），中国湖北省武汉市Xiaoqin Yan，遥感与地理信息系统研究所，地球与空间科学学院，北京大学，中国北京市Zhihui Hu，地理与信息工程学院，中国地质大学（武汉），中国湖北省武汉市Fengling Mao，阿里巴巴集团，中国浙江省杭州市Hong Liu，阿里巴巴集团，中国浙江省杭州市Pengxuan Li，阿里巴巴集团，中国浙江省杭州市Peng Luo，遥感与地理信息系统研究所，地球与空间科学学院，北京大学，中国北京市；德国慕尼黑工业大学，制图与视觉分析教席，德国慕尼黑市Zijin Guo，地理与信息工程学院，中国地质大学（武汉），中国湖北省武汉市Qingfeng Guan，地理与信息工程学院，中国地质大学（武汉），中国湖北省武汉市Yao Yao，地理与信息工程学院，中国地质大学（武汉），中国湖北省武汉市；东京大学，空间信息科学中心，日本千叶市；粤港澳智慧城市联合实验室，中国深圳市

论文9

Automatic road network selection method considering functional semantic features of roads with graph convolutional networks

考虑道路功能语义特征的自动道路网络选择方法，使用图卷积网络

【摘要】Road network selection plays a key role in map generalization for creating multi-scale road network maps. Existing methods usually determine road importance based on road geometric and topological features, few evaluate road importance from the perspective of road utilization based on human travel data, ignoring the functional values of roads, which leads to a mismatch between the generated results and people’s needs. This paper develops two functional semantic features (i.e. travel path selection probability and regional attractiveness) to measure the functional importance of roads and proposes an automatic road network selection method based on graph convolutional networks (GCN), which models road network selection as a binary classification. Firstly, we create a dual graph representing the source road network and extract road features including six graphical and two functional semantic features. Then, we develop an extended GCN model with connectivity loss for generating multi-scale road networks and propose a refinement strategy based on the road continuity principle to ensure road topology. Experiments demonstrate the proposed model with functional features improves the quality of selection results, particularly for large and medium scale maps. The proposed method outperforms state-of-the-art methods and provides a meaningful attempt for artificial intelligence models empowering cartography.

【摘要翻译】道路网络选择在地图泛化中起着关键作用，以创建多尺度道路网络地图。现有的方法通常基于道路的几何和拓扑特征来确定道路的重要性，很少从人类旅行数据的角度评估道路的重要性，忽视了道路的功能价值，这导致生成的结果与人们的需求不匹配。本文开发了两个功能语义特征（即旅行路径选择概率和区域吸引力）来衡量道路的功能重要性，并提出了一种基于图卷积网络（GCN）的自动道路网络选择方法，该方法将道路网络选择建模为二元分类。首先，我们创建一个双图，表示源道路网络，并提取包括六个图形特征和两个功能语义特征的道路特征。然后，我们开发了一个具有连通性损失的扩展GCN模型，以生成多尺度道路网络，并提出了一种基于道路连续性原则的精细化策略，以确保道路拓扑结构。实验表明，具有功能特征的提议模型提高了选择结果的质量，特别是在大规模和中等规模地图上。所提出的方法优于现有的最先进方法，为人工智能模型赋能制图提供了有意义的尝试。

【doi】

【作者信息】Jianbo Tang，中南大学，地理信息工程系，长沙，湖南，中国；湖南省地理空间信息工程与技术研究中心，长沙，湖南，中国Min Deng，中南大学，地理信息工程系，长沙，湖南，中国；湖南省地理空间信息工程与技术研究中心，长沙，湖南，中国Ju Peng，中南大学，地理信息工程系，长沙，湖南，中国Huimin Liu，中南大学，地理信息工程系，长沙，湖南，中国Xuexi Yang，中南大学，地理信息工程系，长沙，湖南，中国；湖南省地理空间信息工程与技术研究中心，长沙，湖南，中国Xueying Chen，中南大学，地理信息工程系，长沙，湖南，中国