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.

Texts have become an important spatial data resource. Interpretation of unstructured geoscience texts using natural language processing methods can effectively facilitate the discovery and retrieval of geographic information. Yet studies on the extraction of spatial information from textual geoscience data are limited compared to digital geoscience data. In this work, a machine learning approach is proposed for mining spatial relations in Chinese geological texts. The approach views spatial relation extraction as a sequence labeling problem, avoids the division of relation categories, and enables mining fine-grained spatial relations. The extracted geological texts commonly describe three-dimensional spatial relations among regions, strata, and lithologies. The extracted spatial relations are classified into three major categories (topological relations, absolute directional relations and relative directional relations) and 14 subcategories. We validated the proposed model with a test dataset, constructed visual displays of the extracted spatial relations on different topics, and quantified the uncertainty in the process from spatial entity recognition to spatial relation extraction. With the detailed portrayal of these spatial relations, this study provides support for solving theoretical and practical problems of cognition, prediction, decision-making, and evaluation in geoscience.