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.

The automatic measurement of socioeconomic status (SES), such as household income, provides fundamental data for policymakers and business applications. Although several urban data sources were employed in previous studies, the spatial extent of the measured objects was ignored, which left room for further improvement in the accuracy of measuring SES. This study develops a multimodal semantic segmentation framework to fuse the spatial extent of ground objects, remote sensing, and near-sensing images for predicting SES levels. The framework first constructs ground feature layer (GFL) tiles by projecting ground-level features from sparse ground images. Then, the ground feature tiles aggregate regional ground-level features with the help of the spatial extents of ranges. Lastly, an improved deep semantic segmentation network is employed to fuse GFL and remote sensing images to predict SES levels. Experimental results on the London dataset show that the proposed method outperforms SOTA models and is robust. The framework provides a rewarding exploration in fusing image data and spatial vector data for image-based intelligent applications and can be applied to a series of socioeconomic applications.

Automatic building instance extraction from high-resolution (HR) remote sensing imagery (RSI) is crucial for urban planning and mapping. The dominant approaches are based on the full-supervised learning paradigm that requires a large number of labeled samples to train their models, which is very time-consuming and labor-intensive. To alleviate this problem, this study proposes a semi-supervised building instance extraction method that integrates teacher-student learning and pseudo-labeling to improve the building instance extraction from HR RSI. Specifically, the proposed method consists of three modules, the hybrid data augmentation (HDA) module, the pseudo label generation (PLG) module and the contour refinement (CR) module. The HDA module is designed to enrich the diversity of labeled samples to optimize the teacher model. The PLG module generates pseudo labels from unlabeled data, and to train student model with pseudo-labels. Finally, the CR module is designed to refine the contours of buildings. Experimental results on three challenging public datasets demonstrate that the proposed method achieves superior performance and exhibits great robustness at different proportions of labeled data and different building scenarios. This study provides a new approach for extracting building instances from HR RSI in scenarios with insufficient labeled samples, and a methodological reference for various applications of semi-supervised on RSIs.

Image-level weakly supervised semantic segmentation (WSSS) methods have greatly facilitated the extraction of buildings from remote sensing (RS) images. However, the lack of the locations and extents of individual buildings in image-level labels results in some limitations of the methods, especially in the cases of cluttered backgrounds, diverse building shapes and sizes. By utilizing bounding box annotations, a novel WSSS model is developed to improve building extraction from RS images in this paper. Specifically, during the training phase, a multiscale feature retrieval (MFR) module is designed to learn multiscale building features and suppress the background noise inside the bounding box. In the inference phase, multiscale class activation maps (CAM) are generated from multiscale features to achieve accurate building localization. Finally, a pseudo mask generation and correction (PGC) module refines the CAMs to generate and correct the building pseudo masks. Experiments are conducted to examine the proposed model in three datasets, namely, the WHU aerial building dataset, the CrowdAI building dataset, and a self-annotated building dataset. Experimental results demonstrate that the proposed method outperforms baselines, achieving 76.99%, 75.51% and 67.35% in terms of IoU scores on the three challenging datasets, respectively. This paper provides a methodological reference for the application of weakly supervised learning on RS images.

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.

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.

Urban function zoning is of great importance for urban structure optimization, urban resource allocation, and urban development planning. Since citizens usually act as a network of motion sensors of the city, their activities could reflect the environment around them. We considered taking advantage of VGI data to classify urban function zones. In this paper, we proposed a framework for automated urban function zoning which is based on VGI geo-tagged photos and OpenStreetMap (OSM) data. Through combining the high-level image features of geo-tagged photos with the road network data, we obtained the functional zoning map of the study area. The experiment result shows the effectiveness of the framework we proposed.