Spatial Information Extraction from Short Messages

Models of spatial relations are a key component of geographical information science (GIS). Efforts have been made to formally define spatial relations. The foundation model for such a formal presentation is the 4-intersection model proposed by Egenhofer and Franzosa (1991). In this model, the topological relations between two simple spatial entities A and B are transformed into pointset topology problems in terms of the intersections of A's interior and boundary with B's interior and boundary. Later, Egenhofer and Herring (1991) extended this model to 9-intersection by addition of another element, i.e. the exterior of an entity, which is then defined as its complement. However, the use of its complement as the exterior of an entity causes the linear dependency between its interior, boundary and exterior. Thus such an extension from 4- to 9-intersection should be of no help in terms of the number of relations. This can be confirmed by the discovery of Egenhofer et al. (1993). The distinction of additional relations in the case where the co-dimension is not zero is purely due to the adoption of definitions of the interior, boundary and exterior of entities in a lower dimensional to a higher dimension of space, e.g. lines in 1-dimensional space to 2-dimensional space. With such adoption, the topological convention that the boundary of a spatial entity separates its interior from its exterior is violated. It is such a change of conventional topological properties that causes the linear dependency between these three elements of a spatial entity (i.e. the interior, boundary and exterior) to disappear, thus making the distinction of additional relations possible in such a case (i.e. the co-dimension is not zero). It has been discussed that the use of Voronoi-regions of an entity to replace its complement as its exterior in the 9-intersection model would solve the problem (i.e. violation of topological convention) or would make this model become more comprehensive. Therefore, a Voronoi-based 9-intersection model is proposed. In addition to the improvement in the theoretical aspect, the Voronoi-based 9-intersection model (V9I) can also distinguish additional relations which are beyond topological relations, such as high-resolution disjoint relations and relations of complex spatial entities. However, high-resolution disjoint relations defined by this model are not purely topological. In fact, it is a mixture of topology and metric.

Adopting the framework of Cognitive Grammar (CG), developed by Langacker (1987), this paper investigated the semantics of the preposition /ʕalaa/ in order to obtain a cognitive semantic analysis of this preposition as it is used in the Taizzi dialect, (TD from now onwards). Typically, /ʕalaa/ is used to encode verticality. That is, it is used to describe a spatial relation between two physical entities where one physical entity is located on/supported by another one. The crucial conundrum of /ʕalaa/ is that it is very appealing to be used in the locative sense in which one physical entity is LOCATED ON another one. However, the analysis of /ʕalaa/ provided justifications of its use in many instances of the TD where this preposition was seemingly exploited to encode non-support-related spatial relations. The data presented in this paper showed that speakers of the TD extend the use of /ʕalaa/ to depict spatial relations other than the ones where the Trajector (TR) is actually located on the Landmark (LM), and spatial relations in which the TR and the LM are horizontally or vertically related to each other. Pedagogically speaking, developing semantic awareness of the use of prepositions is crucial for teaching purposes. This study helps students and learners of a language to reinforce their awareness of concepts like polysemy, idiomaticity, cultural diversity, universality, and language specificity…etc. Furthermore, this study supports Mohammed, Turki (2012) and Mohammed, Turki et al (2012) proposal that the use of prepositions by speakers of the TD to encode spatial or temporal relations depends on how they cognitively characterize the relation between the TRs and the LMs.

Descriptions of the spatial locations of disappeared objects are often recorded in eyewitness records, travel notes, and historical documents. However, in geographic information system (GIS), the observer-centered and vague nature of the descriptions causes difficulties in representing the spatial characters of these objects. To address this problem, this paper proposes a Fuzzy Spatial Region Extraction Model for Object’s Vague Location Description from Observer Perspective (FSREM-OP). In this model, the spatial relationship between the observer and the object are represented in spatial knowledge. It is composed of “phrase” and “region”. Based on the spatial knowledge, three components of spatial inference are constructed: Spatial Entities (SEs), Fuzzy Spatial Regions (FSRs), and Spatial Actions (SAs). Through spatial knowledge and the components of FSREM-OP, an object’s location can be inferred from an observer’s describing text, transforming the vagueness and subjectivity of location description into fuzzy spatial regions in the GIS. The FSREM-OP was tested by constructing a group of observers, object position relationships and vague descriptions. The results show that it is capable of extracting the spatial information and presenting location descriptions in the GIS, despite the vagueness and subjective spatial relation expressions in the descriptions.

A spatial model to aggregate point-source and nonpoint-source water-quality data for large areas

While both linguistic semantics and geographic information science have struggled to accurately and usefully define the meanings of natural language prepositions, there has been less dialogue between these disciplines than one might expect. In this paper we set out to describe the meaning of the English preposition at using well-established formal models of qualitative spatial relations within geographic information science (GIScience), ultimately proposing an account for its meaning which is novel for both discipline areas, and we relate this description to previous definitions of at from within linguistic semantics. The investigation in this paper illustrates that at cannot be handled using the mathematical formalizations of crisp spatial relations in GIScience. However, the model proposed in this paper based on contrast sets, Voronoi Diagrams and Galton & Hood's (2005) Anchoring theory, contributes to a better understanding of the meaning of at. The model also enables improved automatic interpretations of the preposition by partitioning space into contrast sets and representing at as indicating that a spatial entity is more closely related to a reference object than to any other in the set. We show how the model can be framed within a formal semantics account and how it can in principle be extended to account for spatial uses of other prepositions as well. The paper demonstrates potential areas of fruitful cross-fertilisation between GIScience and linguistic semantics. EARLY ACCESS

The integration of the raster data cube alongside another form of geospatial data (e.g., vector data) raises considerable challenges when it comes to managing and representing it using knowledge graphs. Such integration can play an invaluable role in handling the heterogeneity of geospatial data and linking the raster data cube to semantic technology standards. Many recent approaches have been attempted to address this issue, but they often lack robust formal elaboration or solely concentrate on integrating raster data cubes without considering the inclusion of semantic spatial entities along with their spatial relationships. This may constitute a major shortcoming when it comes to performing advanced geospatial queries and semantically enriching geospatial models. In this paper, we propose a framework that can enable such semantic integration and advanced querying of raster data cubes based on the virtual knowledge graph (VKG) paradigm. This framework defines a semantic representation model for raster data cubes that extends the GeoSPARQL ontology. With such a model, we can combine the semantics of raster data cubes with features-based models that involve geometries as well as spatial and topological relationships. This could allow us to formulate spatiotemporal queries using SPARQL in a natural way by using ontological concepts at an appropriate level of abstraction. We propose an implementation of the proposed framework based on a VKG system architecture. In addition, we perform an experimental evaluation to compare our framework with other existing systems in terms of performance and scalability. Finally, we show the potential and the limitations of our implementation and we discuss several possible future works.

In recent years, models of spatial relations, especially topological relations, have attracted much attention from the GIS community. In this paper, some basic topologic models for spatial entities in both vector and raster spaces are discussed. It has been suggested that, in vector space, an open set in 1-D space may not be an open set any more in 2-D and 3-D spaces. Similarly, an open set in 2-D vector space may also not be an open set any more in 3-D vector spaces. As a result, fundamental topological concepts such as boundary and interior are not valid any more when a lower dimensional spatial entity is embedded in higher dimensional space. For example, in 2-D, a line has no interior and the line itself (not its two end-points) forms a boundary. Failure to recognize this fundamental topological property will lead to topological paradox. It has also been stated that the topological models for raster entities are different in Z^{2} and R^{2}. There are different types of possible boundaries depending on the definition of adjacency or connectedness. If connectedness is not carefully defined, topological paradox may also occur. In raster space, the basic topological concept in vector space—connectedness—is implicitly inherited. This is why the topological properties of spatial entities can also be studied in raster space. Study of entities in raster (discrete) space could be a more efficient method than in vector space, as the expression of spatial entities in discrete space is more explicit than that in connected space.

Spatial relation extraction is the process of identifying geographic entities from text and determining their corresponding spatial relations. Traditional spatial relation extraction mainly uses rule-based pattern matching, supervised learning-based or unsupervised learning-based methods. However, these methods suffer from poor time-sensitive, high labor cost and high dependence on large-scale data. With the development of pre-trained language models greatly alleviating the shortcomings of traditional methods, supervised learning methods incorporating pre-trained language models have become the mainstream relation extraction methods. Pipeline extraction and joint extraction, as the two most dominant ideas of relation extraction, both have obtained good performance on different datasets, and whether to share the contextual information of entities and relations is the main differences between the two ideas. In this paper, we compare the performance of two ideas oriented to spatial relation extraction based on Chinese corpus data in the field of geography and verify which method based on pre-trained language models is more suitable for Chinese spatial relation extraction. We fine-tuned the hyperparameters of the two models to optimize the extraction accuracy before the comparison experiments. The results of the comparison experiments show that pipeline extraction performs better than joint extraction of spatial relation extraction for Chinese text data with sentence granularity, because different tasks have different focus on contextual information, and it is difficult to take account into the needs of both tasks by sharing contextual information. In addition, we further compare the performance of the two models with the rule-based template approach in extracting topological, directional and distance relations, summarize the shortcomings of this experiment and provide an outlook for future work.

spatial clustering method can be used for the analysis of satellite image to get the meaningful information with the help of fuzzy local information c-means algorithm using image segmentation technique. In previous clustering based image segmentation method may lose the important image details due to the noise present in the satellite image. The effects of noise are avoided by the spatial relationship among pixels, but it often generates boundary zones for the mix of pixel around the edges .To overcome these problem (FELICM) reduces the edge degradation by introducing the weights of pixel within the local neighboring windows. Canny edge detection used for the extraction of edges, during that detection multi-Otsu threshold can be used to obtain two adaptive thresholds. Local neighbors and window center are separated by edge with respect to different weights are set to the windows. Until the final clustering result may be obtained in a efficient manner the pixel of the different local neighbor window periodically iterated. Without any filter preprocessing steps FELICM method can directly applied to the satellite image to get the valuable information when compared to the remote sensing image of a experimental results and not only solves the problem of isolation of samples and random distribution of pixels inside the region but it also produce the high edge accuracies.

As we all know, remote sensing (RS) images contain multi-scale and numerous RS objects, along with massive and complex spatial topological relationships, such as the adjacency, proximity relations of same-scale objects, and inclusion relations of cross-scale objects. However, the existing semantic segmentation methods have never explored the cross-scale relations, which are especially important when comes to the situation that the RS objects cannot be accurately identified, they could be supplemented by the surrounding contents. To address the above concern, we propose a scale-relation joint decoupling network (SRJDN) for the semantic segmentation of RS images by simultaneously considering decoupling scales and decoupling relations to excavate more complete relationships of multi-scale RS objects. The SRJDN is performed by following three steps, namely scale decoupling (SD), relation decoupling (RD), and fine-granularity guided fusion (FGF). The SD module uses dilated convolution with different rates to decouple RS objects into different scale feature groups, from small to large scales. Afterward, the RD considers all the spatial topological relationships and decouples these relationships according to the scale, which is divided into two parts, including same-scale relation extraction (SSRE) and cross-scale relation extraction (CSRE). The SSRE establishes the graph structures at each scale independently to mine the relationships of same-scale RS objects and the CSRE constructs the graph in a unified pattern between cross-scales to explore cross-scale target relationships. Third, the FGF module regards small-scale features as fine-granularity representation and applies its attention map to guide the learning of other scale features, which could mine more reliable and comprehensive saliency information and improve the feature consistency. Numerical experiments conducted on two large-scale fine-resolution RS image datasets empirically demonstrate the robustness of the proposed joint decoupling strategy and the effectiveness of the fine-granularity guided fusion in RS image semantic segmentation tasks.

Late Cenozoic transpression in southwestern Mongolia and the Gobi Altai-Tien Shan connection

Interpretable tropical cyclone intensity estimation using Dvorak-inspired machine learning techniques

The quantity of water that can be extracted from a borehole is directly related to the groundwater potential of an area among other things. Boreholes drilled in areas of low groundwater potential have every tendency to fail. There have been reported cases of borehole failures in Okene Local Government Area of Kogi state. This study therefore aimed at analyzing the potentiality of groundwater and the spatial relationship between borehole yield and groundwater potential zones in the area. Various data which include Sentinel 2 satellite image, Digital Elevation Model, geological map, rainfall data and borehole yield data were analyzed to produce various zones of groundwater potential, and validated using existing borehole yields and the relationship was tested using Pearson correlation. The result of this study reveals that the low potential zone covers 33.2%; moderate potential zone occupies 44.9% which cuts across the whole of Okene LGA and the high potential zone covers 21.9% mainly found around the western part of the area. The Pearson moment correlation analysis result revealed a strong positive correlation (R) value of 0.919. The correlation value shows that there is a strong positive relationship between the existing borehole yields in the study area and the groundwater potentiality map produced from the analysis. The study concluded that there is a strong positive relationship between the existing borehole yields and the groundwater potential zones in Okene Local Government Area.

Classification and analysis of high-resolution satellite images using conventional techniques have been limited. This is due to the complex characteristics of the imagery. These images are characterized by features such as spectral signatures, complex texture and shape, spatial relationships and temporal changes. In this research, we present the performance evaluation and analysis of deep learning approaches based on Convolutional Neural Networks and vision transformer towards achieving efficient classification of remote sensing satellite images. The CNN-based models explored include ResNet, DenseNet, EfficientNet, VGG and InceptionV3. The models were evaluated on three publicly available EuroSAT, UCMerced-LandUse and NWPU-RESISC45 datasets containing categories of images. The models achieve promising results in accuracy, recall, precision and F1-score. This performance demonstrates the feasibility of Deep Learning approaches in learning the complex and in-homogeneous features of the high-resolution remote sensing images.

Line segment registration (LSR) for image pairs is a challenging task but plays an important role in remote sensing and photogrammetry. This paper proposes a line segment registration method using Gaussian Mixture Models (GMMs) and Expectation-Maximization (EM) algorithm. Comparing to the conventional registration methods which consider the local appearance of points or line segments, the proposed method of LSR uses only the spatial relations between the line segments detected from an image pair, and it does not require the corresponding line segments sharing the same start points and end points. Although the proposed method is not confined to the transformation model between the image pair, the affine model, which is a simple and fast registration model and widely used in remote sensing, is taken to verify the proposed method. Various images including aerial images, satellite images and GIS data are used to test the algorithm, and test results show that the method is robust to different conditions, including rotation, noise and illumination. The results of the proposed method are compared with those of other line segment matching methods, and it is shown that the proposed method is superior in matching precision and performs better in less-texture or no-texture case.