Extracting spatial information from text subserves data-driven geospatial semantic research. Traditional methods consider words, phrases, or triples to extract spatial entities but often overlook specific spatiotemporal conditions, leading to fragmented representations and potentially inaccurate spatial perceptions. In this study, we present Geo-Object-Reader, a template-based method for the joint spatial information extraction (SIE) of spatial objects and spatiotemporal attributes. The joint extraction highlights an integrated representation of spatial object attributes, relations and their associated spatiotemporal conditions. This study develops three SIE templates tailored to the spatiotemporal characteristics of geospatial objects: spatial attribute template, non-spatial attribute template and 3D spatial relation template. These templates integrate specific spatiotemporal fields to ensure that the extracted attributes and relations are accurate and contextually relevant. A subsequent graph neural network approach captures the contextual information associated with these template fields to apprehend the complex interactions within geoscience texts. The final stage involves the use of a directed acyclic graph (DAG)-based filling strategy to enhance the efficiency of template filling. A dataset constructed based on Chinese geological reports was used to demonstrate that the proposed method provides holistic perspectives, bridging semantic gaps and forming more reliable knowledge chains compared to triples.

Geophysical data are often integrated into geological data for 3D modeling of underground spaces. However, the existing single-view approach means it is difficult to adequately fuse the valid information between the two types of data, and the complexity of lithological decoding and classification is high. To address this issue, a multi-view ensemble machine learning (ML) framework is proposed. Initially, the original dataset of lithology prediction is constructed by aligning geological and geophysical data with different spatial scales. Next, the dataset is divided into three datasets of structural strength, density, and moisture content according to the lithology properties of the geophysical data. The proposed framework is then used to capture the lithologic characteristics under different views to achieve the prediction of lithologic labels. In this process, a self-attentive mechanism is used to adaptively fuse the valid information under each view. To validate the proposed framework, it is applied to a project in Jiaxing, Zhejiang Province, China. Compared with existing ML methods, the proposed multi-view ensemble ML framework improves modeling accuracy and constructs models with low uncertainty. The framework can be extended to other multi-source data fusion tasks across geoscience domains.

Urban area data are strategically important for public safety, urban management, and planning. Previous research has attempted to estimate the values of unsampled regular areas, while minimal attention has been paid to the values of irregular areas. To address this problem, this study proposes a hierarchical geospatial graph neural network model based on the spatial hierarchical constraints of areas. The model first characterizes spatial relationships between irregular areas at different spatial scales. Then, it aggregates information from neighboring areas with graph neural networks, and finally, it imputes missing values in fine-grained areas under hierarchical relationship constraints. To investigate the performance of the proposed model, we constructed a new dataset consisting of the urban statistical values of irregular areas in New York City. Experiments on the dataset show that the proposed model outperforms state-of-the-art baselines and exhibits robustness. The model is adaptable to numerous geographic applications, including traffic management, public safety, and public resource allocation.

Land-use maps are instrumental to inform urban planning and environmental research. Street view images (SVIs) have shown great potential for automated land-use classification for land-use mapping. However, previous studies overlooked SVI-derived location contextual information that may help improve land-use classification. This study proposes a novel land-use classification method that synthesizes location semantics from SVIs to account for contextual information from SVIs, land parcels and roads around the SVIs. The proposed method first generates land-use scene images (LUSIs) by using an SVI-derived straightforward algorithm. The LUSIs are then relocated to land parcels by using a displacement strategy and classified into land-use types by using a deep learning network. This study determines the land-use types of land parcels with classified LUSIs. Two case studies, consisting of LUSIs for five land-use types, show that introducing location semantics of SVIs can remarkably improve the classification accuracy of land-use types.

During navigation, a pedestrian needs to recognize a landmark at a certain decision point. If a potential landmark located at a decision point is complicated to recognize, the complexity of the decision point is significantly increased. Thus, it is important to compute routes that avoid complicated decision points (CDPs) but still achieve optimal navigation performance. In this paper, we propose an approach for computing routes that avoid CDPs while optimizing the performance of landmark-based pedestrian navigation. The approach includes (1) a model for identifying CDPs based on the structures of pedestrian networks and landmark data in real scenes, and (2) a modified genetic algorithm for computing routes that avoid the identified CDPs and find the shortest route possible. To demonstrate the advantages and effectiveness of the proposed approach, we conducted an empirical study on the pedestrian network in a real-world scenario. The experimental results show that our approach can effectively avoid CDPs while still minimizing travel distance. Furthermore, our approach can provide the routes with the shortest travel distance if the distances of the routes without CDPs exceed a certain threshold.