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.

Human-centric Emotional Video Captioning (H-EVC) aims to generate fine-grained, emotion-related sentences for human-based videos, enhancing the understanding of human emotions and facilitating human-computer emotional interaction. However, existing video captioning methods often overlook subtle emotional clues and interactions in videos. As a result, the generated captions frequently lack emotional information. To address this, we proposeEmotion-orientedCross-modalPrompting andAlignment (ECPA), which improves HEVC accuracy by modeling fine-grained visual-textual emotion clues. Using large foundation models, ECPA introduces two learnable prompting strategies: visual emotion prompting (VEP) and textual emotion prompting (TEP), along with an emotion-oriented cross-modal alignment (ECA) module. VEP uses two levels of visual prompts,i.e., emotion recognition (ER) and action unit (AU), to focus on both coarse and fine visual emotional features. TEP devise two-level learnable textual prompts,i.e., sentence-level emotional tokens and word-level masked tokens to capture global and local textual emotion representations. ECA introduces another two levels of emotion-oriented prompt alignment learning mechanisms: the ER-sentence level and the AU-word level alignment losses. Both enhance the model's ability to capture and integrate both global and local cross-modal emotion semantics, thereby enabling the generation of fine-grained emotional linguistic descriptions in video captioning. Experiments show ECPA significantly outperforms state-of-the-art methods on various H-EVC datasets (relative improvements of 9.98%, 5.72%, 4.46%, 24.52% on MAFW, and 12.82%, 20.27%, 4.22%, 5.01% on EmVidCap across four evaluation metrics) and supports zero-shot tasks on MSVD and MSRVTT, demonstrating strong applicability and generalization.

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.

Community roads are crucial for efficient navigation in residential areas. However, current navigation maps often lack comprehensive coverage of these roads. To address this issue, we present a Human-Flow Model (HFM) to identify roads within residential communities by utilizing abundant low-frequency trajectory data from human movements. First, the HFM leverages human movement density in residential zones to estimate the likelihood of road existence. Using the probability distribution of human movement and neighboring building footprints, we construct a Human-Flow Probability Field (HFPF), which serves as a distribution representation for modeling human movement within densely populated built-up areas. Then, the flow paths are extracted from the HFPF using hydrological analysis techniques, which facilitates the identification of main paths and smaller branches within the community road network. Finally, the road network is refined using morphological methods. Our model was tested using six residential communities located in Wuhan, China. It consistently outperformed other methods by detecting more roads with higher accuracy, especially intricate branches. By incorporating flow semantics, our model capitalizes on sparse trajectory data to enhance the fine-scale community road networks. This improvement enhances the last-mile navigation experience in sustainable cities, contributing to overall urban mobility and convenience for residents.

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.

Human interaction with indoor objects constantly changes indoor space and its navigability. Spatial subdivision models that delineate navigable space become a crucial prerequisite for indoor navigation. However, existing spatial subdivision models do not fully capture the dynamic changes of the indoor context and have limitations in identifying precise navigable space, thereby reducing the accuracy and efficiency of indoor navigation. This study proposes a novel interactive semantic model (ISM) that consists of an interactive semantic base (ISB) and empirical rules to accurately determine the navigability of the reshaped functional spaces (F-Spaces) of indoor objects. First, two-level F-Spaces (fine-grained resource F-Space and coarse-grained structure F-Space) are defined to express multi-granularity interactive semantics for delineating heterogeneous F-Spaces. Second, empirical rules are established through an extensible multi-dimensional semantics classification framework to determine each F-Space’s navigability. Lastly, a navigable F-Space generation scheme is designed by considering the adaptive navigability of the two-level F-Spaces. Simulated experiments show that the proposed model can generate precise and efficient dynamic navigable spaces. This study reduces the cognitive burden of human agents when facing indoor dynamic navigation, thereby improving the spatial experience of navigation.

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.