Natural-language spatial relations between geographic entities (geoentities) reflect diverse perceptions influenced by factors like location, culture, and linguistic conventions. These relations play a crucial role in supporting geospatial tasks, such as question answering and cognitive reasoning. While prior studies focused on a limited set of human-selected spatial terms and geometric attributes, they often overlooked essential semantic attributes. To overcome this limitation, we developed a Spatial Relation-based Knowledge Graph Embedding framework, SR-KGE, with new KG fusion functions to predict spatial relation terms among distinct geoentities. This method not only considers graph structures and the diversity of natural language expressions in the embedding and learning process, but also incorporates geoentity types as a constraint to capture spatial and semantic relations more accurately. Our experiments on two knowledge graph datasets, one small-scale and one large-scale, have both shown its superior performance in spatial relation inference compared to popular KGE models, including TransE, RotatE, and HAKE. We hope our research will advance the classic study of natural language described spatial relations in a more automated and intelligent way.

Geographic knowledge graph (GeoKG) alignment is important for the integration and knowledge discovery of multisource geographic information and the generation of large-scale and high-quality knowledge graphs (KGs). However, the existing models/technologies face many challenges when dealing with large-scale multisource complex GeoKG alignment tasks, including the inconsistency of attribute and relationship values caused by domain differences, the inability to perceive relationships and entities, and missing geographic domain training data. To address these issues, we propose a GeoKG alignment model based on depth relationships and neighborhood awareness (named DRNA-GCNE). The DRNA-GCNE model adopts a graph neural network as the infrastructure and uses the graph attention technique to evaluate and weight the entity’s relationship attributes dynamically, thus enhancing the ability to perceive structural and semantic information in the GeoKG; concurrently, the relationship information and the multihop neighbor characteristics of the entity are effectively integrated, and the representation of the entity is further enriched. Finally, the training technique of normalized loss mining for multiple negative samples is shown. This approach increases the model’s capacity for generalization. The DRNA-GCNE model, as evaluated on two public datasets and our GeoEA2024 Chinese dataset, significantly outperforms current GeoKG entity alignment methods across key metrics.

As the domain of artificial intelligence has advanced, the integration of deep learning techniques into terrain and landform analysis has become more prevalent. Nevertheless, many existing methods are fully supervised and designed for specific tasks; thus, their transferability is limited and massive annotated samples are required. This study introduces a geomorphic pretrained model (GeomorPM) capable of performing multiple tasks. First, an architecture was designed that combined a convolution-based Vector Quantised-Variational Autoencoder (VQVAE) with a Transformer-based masked autoencoder (MAE) framework, allowing it to autonomously learn local details and global patterns from large-scale digital elevation model (DEM) data. Subsequently, GeomorPM, based on the VQMAE architecture, was pretrained on massive DEM data and fine-tuned for three specific tasks: DEM void filling, DEM superresolution, and landform classification. GeomorPM outperformed the traditional and other deep learning methods in all three tasks, demonstrating the superior learning ability and transferability of the model. This study provides a practical framework for developing pretrained models based on DEMs that can be expanded to other continuous geoscientific data.

Geographic knowledge graphs (GKG), central to GeoAI, represent the culmination of knowledge engineering in the era of geographic big data. Knowledge Graph Embedding (KGE) transforms entities and relationships within a knowledge graph into a low-dimensional vector space, effectively capturing their semantic and structural properties. Geographic object knowledge encompasses both intrinsic features and spatiotemporal characteristics, with temporal features indicating the existence or state changes of objects. However, neglecting temporal aspects—such as order, continuity, granularity, and periodicity—during vector calculations can distort the embedding space, reducing the effectiveness of time-sensitive geographic queries, link prediction, and recommendations. This study introduced a temporal feature encoder and designed a fusion mechanism that integrated geographic objects and temporal features. Grounded in logical query tasks, this approach aims to enhance the temporal expressiveness by refining temporal embeddings, thereby improving query accuracy for time-sensitive tasks. A comparative analysis was conducted to evaluate the effects of different baseline models, temporal encoders, and temporal feature weights on the performance of geographic queries.

Cellular Automata (CA) models have become the most commonly used tool for simulating urban expansion. To improve the accuracy of CA models, various driving factors like spatial proximity and neighbourhood effects have been explored in previous studies, but the inclusion of these factors does not address the directional differences in urban expansion. To address this issue, this study develops a method to measure urban spatial anisotropy (SA) with respect to 18 variables at both the global and local scales, and integrates all these SA variables into a logistic regression-based CA model. The revised CA model is evaluated with a case study for Huizhou, China. The case study shows that the simulation results for the CA model with SA exhibit 89% overall accuracy; compared to CA models that do not consider SA, the revised CA model can improve precision by 5% on newly developed cells. The consideration of SA in CA models proves promising in improving the accuracy of urban expansion simulations.

Kai Sunabc , Yingjie Huc , Jia Songad & Yunqiang Zhuad* a State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, Chinab University of Chinese Academy of Sciences, Beijing, Chinac GeoAI Lab, Department of Geography, University at Buffalo, Buffalo, NY, USAd Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing, ChinaKai Sun is a PhD student at the Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences. He is also a visiting PhD student in the Department of Geography, University at Buffalo. His research interest is in geospatial semantics. He extracts information from data and builds knowledge base to make the information easier to be accessed. He also develops methods to align geospatial information to deal with the issues of data duplication and inconsistency among different geospatial knowledge bases.Dr. Yingjie Hu is an Assistant Professor in the Department of Geography at the University at Buffalo (UB) and the National Center for Geographic Information and Analysis (NCGIA). His major research area is in geographic information science (GIScience), and more specifically in geospatial artificial intelligence (GeoAI), spatial data mining, and geographic information retrieval. He develops and applies spatial analysis, data mining, machine learning, and deep learning methods to address various geospatial problems in disaster response, public health, urban planning, and digital humanities.Dr. Jia Song is an Associate Professor at the Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences. His main research interests include geospatial computing and big data processing.Dr. Yunqiang Zhu is a Professor at the Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences. He is interested in studying the spatial information technology research and application, scientific data sharing, and e-Geoscience.CONTACT Yunqiang Zhu [email protected] maps contain rich geographic information about the past of a region. They are sometimes the only source of information before the availability of digital maps. Despite their valuable content, it is often challenging to access and use the information in historical maps, due to their forms of paper-based maps or scanned images. It is even more time-consuming and labor-intensive to conduct an analysis that requires a synthesis of the information from multiple historical maps. To facilitate the use of the geographic information contained in historical maps, one way is to build a geographic knowledge graph (GKG) from them. This paper proposes a general workflow for completing one important step of building such a GKG, namely aligning the same geographic entities from different maps. We present this workflow and the related methods for implementation, and systematically evaluate their performances using two different datasets of historical maps. The evaluation results show that machine learning and deep learning models for matching place names are sensitive to the thresholds learned from the training data, and a combination of measures based on string similarity, spatial distance, and approximate topological relation achieves the best performance with an average F-score of 0.89.

The complexity of geographic modelling is increasing; hence, preparing data to drive geographic models is becoming a time-consuming and difficult task that may significantly hinder the application of such models. Meanwhile, a huge number of data sets have been shared and have become publicly accessible through the Internet. This study presents a data similarity-based approach to automatically recommend available data sets to fulfil the data requirements of geographic models. Unified description factors are adopted to provide a consistent description of public data sets and input data requirements of geographic models. Five elementary data similarities between them, specifically content, spatial coverage, temporal coverage, spatial precision, and temporal granularity similarities, are calculated. An overall similarity is estimated from aggregating the elementary data similarities. Thereafter, the candidate data for running the models are recommended in the order of overall data similarity. As a case study, the approach has been applied to recommend data from the China National Data Sharing Platform of Earth System Science to drive the population spatialization model (PSM). The approach has successfully recommended the most related data sets to run PSM. The result also suggests that the data recommendation approach can facilitate the intelligent identification of geographic data and the building of links between the open data sets.