GeoSPARQL 1.1: Motivations, Details and Applications of the Decadal Update to the Most Important Geospatial LOD Standard

Car, Nicholas J.; Homburg, Timo

doi:10.3390/ijgi11020117

Cited by 30 publications

(7 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The properties of the spatial ontology mainly define the topological spatial relations between geographic objects, as well as the geometry literal [26], which is the serialization standard used when generating geometry descriptions and the supported geometry types. In addition, the properties of the spatial ontology also include Metric [26], which are scalar spatial properties that describe the geographic object. The main rule of the spatial ontology includes basic ontology constraints for class and property, such as constraints on hierarchical relationships between classes and constraints on property values.…”

Section: • Spatial Ontologymentioning

confidence: 99%

An Improved Multi-Source Data-Driven Landslide Prediction Method Based on Spatio-Temporal Knowledge Graph

Chen

Yang

et al. 2023

Remote Sensing

View full text Add to dashboard Cite

Landslides pose a significant threat to human lives and property, making the development of accurate and reliable landslide prediction methods essential. With the rapid advancement of multi-source remote sensing techniques and machine learning, remote sensing data-driven landslide prediction methods have attracted increasing attention. However, the lack of an effective and efficient paradigm for organizing multi-source remote sensing data and a unified prediction workflow often results in the weak generalization ability of existing prediction models. In this paper, we propose an improved multi-source data-driven landslide prediction method based on a spatio-temporal knowledge graph and machine learning models. By combining a spatio-temporal knowledge graph and machine learning models, we establish a framework that can effectively organize multi-source remote sensing data and generate unified prediction workflows. Our approach considers the environmental similarity between different areas, enabling the selection of the most adaptive machine learning model for predicting landslides in areas with scarce samples. Experimental results show that our method outperforms machine learning methods, achieving an increase in F1 score by 29% and an improvement in processing efficiency by 93%. Furthermore, by comparing the susceptibility maps generated in real scenarios, we found that our workflow can alleviate the problem of poor prediction performance caused by limited data availability in county-level predictions. This method provides new insights into the development of data-driven landslide evaluation methods, particularly in addressing the challenges posed by limited data availability.

show abstract

Section: • Spatial Ontologymentioning

confidence: 99%

An Improved Multi-Source Data-Driven Landslide Prediction Method Based on Spatio-Temporal Knowledge Graph

Chen

Yang

et al. 2023

Remote Sensing

View full text Add to dashboard Cite

show abstract

“…Linked Data and Semantic Web technologies may assist in aligning and linking EO data with each other, but also other kind types of geo-information, using the Resource Description Framework (RDF) for interconnecting data. W3C has proposed standards for representing and querying geospatial data in RDF allowing for topological querying in RDF with GeoSPARQL 1,2 . Such vocabularies and querying languages have been adopted by OpenStreetMaps, DBpedia, and Ordinance Services across the world.…”

Section: Background 21 Geo-spatial Linked Datamentioning

confidence: 99%

Linking Earth observation data with ground truth from the open web

Koster,

Valk,

Athanasiadis

2023

Image and Signal Processing for Remote Sensing XXIX

View full text Add to dashboard Cite

This paper presents a system for linking Earth Observation with open Web data, into a Linked Open Data architecture. The architecture has two components, one for extracting signals from Earth Observation data, and another for harvesting web sources. Both are linked with spatial objects. Web scraped data, either from APIs or crowd-sourced websites are geo-referenced and thematically annotated with standard vocabularies. The architecture has been demonstrated in two case studies, one for building permits and another for crowd-sourced observations of invasive aquatic plants.

show abstract

“…In addition, some properties of classes and relationships can be stated as part of the ontology. Geo-ontologies usually base their foundation on the primary schema of GeoSPARQL ontology [7] which contains "spatial object" as the main class with two subclasses namely "feature" and "geometry". A spatial object is defined as anything spatial (being or having a shape, a position, or an extent).…”

Section: Ontology Design and Developmentmentioning

confidence: 99%

“…For example, a river is a feature and hence can be linked to a point object (subclass geometry) which can represent a geocoordinate [7]. An important design feature of the GeoSPARQL and almost all the other related ontologies [7,28,31,42] describing geographical aspects is that they define qualitative spatial relations as object properties e.g. {Entity: Place1, ObjectProperty: west, Entity: Place2}.…”

Section: Ontology Design and Developmentmentioning

confidence: 99%

Towards an Extensible Framework for Understanding Spatial Narratives

Ezeani,

Rayson,

Gregory

et al. 2023

Proceedings of the 7th ACM SIGSPATIAL International Workshop on Geospatial Humanities

View full text Add to dashboard Cite

Spatial narratives help us to organize experiences and give them meaning. Previous approaches to understanding geographies in textual sources focus on geoparsing to automatically identify place names and allocate them to coordinates. Those are highly quantitative, and are limited to named places with coordinates, and have little concept of time. Narratives of journeys indicate that human experiences of geography are often subjective and more suited to qualitative representation. Geography is not limited to named places but incorporates the vague, imprecise, and ambiguous, e.g "the camp", or "the hills in the distance", and relative locations such as "near to", "on the left", "north of" or "a few hours' journey from". Places are organized worlds of meaning, characterized by experience, emotion, and memory as well as by geography. In this paper, we discuss our approach to gaining more insight from textual data beyond the toponyms and introduce an extensible framework for extracting, analyzing, and visualizing spatial elements that define the 'locale' as well as the 'sense of place' referenced in text using two test corpora -the Corpus of the Lake District Writing and Holocaust Survivors' Testimonies. CCS CONCEPTS• Information systems → Geographic information systems; Presentation of retrieval results; • General and reference → Experimentation; • Human-centered computing → Collaborative and social computing systems and tools.

show abstract

GeoSPARQL 1.1: Motivations, Details and Applications of the Decadal Update to the Most Important Geospatial LOD Standard

Cited by 30 publications

References 21 publications

An Improved Multi-Source Data-Driven Landslide Prediction Method Based on Spatio-Temporal Knowledge Graph

An Improved Multi-Source Data-Driven Landslide Prediction Method Based on Spatio-Temporal Knowledge Graph

Linking Earth observation data with ground truth from the open web

Towards an Extensible Framework for Understanding Spatial Narratives

Contact Info

Product

Resources

About