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16th International Symposium on Spatial and Temporal databases (SSTD)

作者： Patroumpas, Kostas Skoutas, Dimitrios Mandilaras, Georgios Giannopoulos, Giorgos Athanasiou, Spiros Athena Res Ctr IMSI Athena Greece Univ Athens Athens Greece

ISBN: (纸本)9781450362801

Point of Interest (POI) data is widely used in many modern applications and services related to navigation, tourism, social networking, logistics, and many more. In this paper, we propose a comprehensive and vendor-agnostic data model to represent multi-faceted and enriched POI profiles. Harnessing the versatility of linked data technologies, this semantically rich ontology accommodates and extends existing POI formats for assembling and managing POI data from heterogeneous sources. Furthermore, we have developed the open-source software TripleGeo, which can effectively transform POI data from diverse sources and formats (geographical files, databases, and semi-structured data) to their RDF representations and vice versa. Thus, it is possible to import POI data from various existing systems and products, transfer and address the data integration challenges in the linked data domain, and export back the results. Our empirical study confirms the validity and efficiency of this framework for a variety of real-world POI assets and formats, underscoring its robustness to cope with scalable data volumes.

关键词： POI RDF ontology linked geospatial data transformation

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Template-Based Question Answering over linked geospatial data 18

Template-Based Question Answering over Linked Geospatial Dat...

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12th Workshop on Geographic Information Retrieval (GIR)

作者： Punjani, D. Singh, K. Both, A. Koubarakis, M. Angelidis, I. Bereta, K. Beris, T. Bilidas, D. Ioannidis, T. Karalis, N. Lange, C. Pantazi, D. Papaloukas, C. Stamoulis, G. Univ Athens Dept Informat & Telecommun Athens Greece Fraunhofer IAIS St Augustin Germany Univ Bonn Bonn Germany DATEV eG Nurnberg Germany

ISBN: (纸本)9781450360340

Large amounts of geospatial data have been made available recently on the linked open data cloud and on the portals of many national cartographic agencies (e.g., OpenStreetMap data, administrative geographies of various countries, or land cover/land use data sets). These datasets use various geospatial vocabularies and can be queried using SPARQL or its OGC-standardized extension GeoSPARQL. In this paper we go beyond these approaches to offer a question answering service on top of linked geospatial data sources. Our system has been implemented as re-usable components of the Qanary question answering architecture to provide benefits for future research tasks. We give a detailed description of the architecture of the system, its underlying algorithms and its evaluation using a set of 201 natural language questions.

关键词： linked geospatial data DBpedia General Administrative Divisions dataset (GADM) OpenStreetMap

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Sextant: Visualizing time-evolving linked geospatial data

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JOURNAL OF WEB SEMANTICS 2015年 35卷 35-52页

作者： Nikolaou, Charalampos Dogani, Kallirroi Bereta, Konstantina Garbis, George Karpathiotakis, Manos Kyzirakos, Kostis Koubarakis, Manolis Univ Athens Dept Informat & Telecommun Univ Campus Athens 15784 Greece

The linked open data cloud is constantly evolving as datasets get continuously updated with newer versions. As a result, representing, querying, and visualizing the temporal dimension of linked data is crucial. This is especially important for geospatial datasets that form the backbone of large scale open data publication efforts in many sectors of the economy (e.g., the public sector, the Earth Observation sector). Although there has been some work on the representation and querying of linked geospatial data that change over time, to the best of our knowledge, there is currently no tool that offers spatio-temporal visualization of such data. This is in contrast with the existence of many tools for the visualization of the temporal evolution of geospatial data in the GIS area. In this article, we present Sextant, a Web-based system for the visualization and exploration of time-evolving linked geospatial data and the creation, sharing, and collaborative editing of "temporally-enriched" thematic maps which are produced by combining different sources of such data. We present the architecture of Sextant, give examples of its use and present applications in which we have deployed it. (C) 2015 Elsevier B.V. All rights reserved.

关键词： linked geospatial data linked spatiotemporal data Visualization Browsing Exploration

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Wildfire monitoring using satellite images, ontologies and linked geospatial data

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JOURNAL OF WEB SEMANTICS 2014年 24卷 18-26页

作者： Kyzirakos, K. Karpathiotakis, M. Garbis, G. Nikolaou, C. Bereta, K. Papoutsis, I. Herekakis, T. Michail, D. Koubarakis, M. Kontoes, C. Univ Athens Athens Greece Natl Observ Athens Athens Greece Harokopio Univ Athens Athens Greece

Advances in remote sensing technologies have allowed us to send an ever-increasing number of satellites in orbit around Earth. As a result, Earth Observation data archives have been constantly increasing in size in the last few years, and have become a valuable source of data for many scientific and application domains. When Earth Observation data is coupled with other data sources many pioneering applications can be developed. In this paper we show how Earth Observation data, ontologies, and linked geospatial data can be combined for the development of a wildfire monitoring service that goes beyond applications currently deployed in various Earth Observation data centers. The service has been developed in the context of European project TELEIOS that faces the challenges of extracting knowledge from Earth Observation data head-on, capturing this knowledge by semantic annotation encoded using Earth Observation ontologies, and combining these annotations with linked geospatial data to allow the development of interesting applications. (C) 2014 Elsevier B.V. All rights reserved.

关键词： Earth Observation Scientific databases linked geospatial data GIS data

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ONTOLOGICAL MODELING OF CLIMATE data TO IMPROVE CLIMATE ANALYTICS

ONTOLOGICAL MODELING OF CLIMATE DATA TO IMPROVE CLIMATE ANAL...

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IEEE International Geoscience and Remote Sensing Symposium (IGARSS)

作者： Wu, Jiantao Orlandi, Fabrizio O'Sullivan, Declan Dev, Soumyabrata ADAPT SFI Res Ctr Dublin Ireland Univ Coll Dublin Sch Comp Sci Dublin Ireland Trinity Coll Dublin Sch Comp Sci & Stat Dublin Ireland

ISBN: (纸本)9798350320107

Climate data is a valuable resource for understanding past weather patterns, assessing long-term climate trends, and conducting climate-related research. However, most existing knowledge graphs for climate data rely heavily on the standardized (per W3C recommendations) SOSA/SSN ontology, which can help improve general data accessibility, but typically overlooks the analytical applications of multisource climate data. To further enhance the accessibility of heterogeneous data for climate data analytics, this paper extends the CA ontology and implements a virtual knowledge graph for analytical applications. We emphasize the importance of incorporating observation metadata and geospatial representation into analytical applications. Through our study, we demonstrate the applicability of the proposed ontological model in deriving the ETCCDI indices. An example of the formation of the annual maximum daily temperature is given. Furthermore, we showcase the potential of linkedGeodata in providing a more comprehensive geographical context for accessing climate data within the knowledge graph, leveraging the proposed ontological modeling and linked data principles.

关键词： Climate data Analytics Virtual Knowledge Graphs linked geospatial data GeoSPARQL

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Converting and Enriching Geo-annotated Event data: Integrating Information for Ukraine Resilience 23

Converting and Enriching Geo-annotated Event Data: Integrati...

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31st ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems (ACM SIGSPATIAL GIS)

作者： Attar, Manar Wang, Shuai Siebes, Ronald Kultorp, Eirik Vrije Univ Amsterdam Amsterdam Netherlands

ISBN: (纸本)9798400701689

The mission of resilience of Ukrainian cities calls for international collaboration with the scientific community to increase the quality of information by identifying and integrating information from various news and social media sources. linked data technology can be used to unify, enrich, and integrate data from multiple sources. In our work, we focus on datasets about damaging events in Ukraine due to Russia's invasion since February 2022. We convert two selected datasets to linked data and enrich them with additional geospatial information. Following that, we present an algorithm for the detection of identical events from different datasets. Our pipeline makes it easy to convert and enrich datasets to integrated linked data. The resulting dataset consists of 10K reported events covering damage to hospitals, schools, roads, residential buildings, etc. Finally, we demonstrate in use cases how our dataset can be applied to different scenarios for resilience purposes.

关键词： linked open data data integration linked geospatial data Ukraine resilience

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ExtremeEarth Meets Satellite data From Space

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IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING 2021年 14卷 9038-9063页

作者： Hagos, Desta Haileselassie Kakantousis, Theofilos Vlassov, Vladimir Sheikholeslami, Sina Wang, Tianze Dowling, Jim Paris, Claudia Marinelli, Daniele Weikmann, Giulio Bruzzone, Lorenzo Khaleghian, Salman Kraemer, Thomas Eltoft, Torbjorn Marinoni, Andrea Pantazi, Despina-Athanasia Stamoulis, George Bilidas, Dimitris Papadakis, George Mandilaras, George Koubarakis, Manolis Troumpoukis, Antonis Konstantopoulos, Stasinos Muerth, Markus Appel, Florian Fleming, Andrew Cziferszky, Andreas KTH Royal Inst Technol S-11428 Stockholm Sweden Log Clocks S-16440 Stockholm Sweden Univ Trento I-38122 Trento Italy UiT Arctic Univ Norway N-9019 Tromso Norway Natl & Kapodistrian Univ Athens Athens 15772 Greece Natl Ctr Sci Res Demokritos Paraskevi 15341 Greece VISTA Remote Sensing Geosci GmbH D-80333 Munich Germany British Antarctic Survey Cambridge CB3 0ET England

Bringing together a number of cutting-edge technologies that range from storing extremely large volumes of data all the way to developing scalable machine learning and deep learning algorithms in a distributed manner and having them operate over the same infrastructure poses unprecedented challenges. One of these challenges is the integration of European Space Agency (ESA)'s Thematic Exploitation Platforms (TEPs) and data information access service platforms with a data platform, namely Hopsworks, which enables scalable data processing, machine learning, and deep learning on Copernicus data, and development of very large training datasets for deep learning architectures targeting the classification of Sentinel images. In this article, we present the software architecture of ExtremeEarth that aims at the development of scalable deep learning and geospatial analytics techniques for processing and analyzing petabytes of Copernicus data. The ExtremeEarth software infrastructure seamlessly integrates existing and novel software platforms and tools for storing, accessing, processing, analyzing, and visualizing large amounts of Copernicus data. New techniques in the areas of remote sensing and artificial intelligence with an emphasis on deep learning are developed. These techniques and corresponding software presented in this article are to be integrated with and used in two ESA TEPs, namely Polar and Food Security TEPs. Furthermore, we present the integration of Hopsworks with the Polar and Food Security use cases and the flow of events for the products offered through the TEPs.

关键词： Deep learning Satellites Monitoring Sea ice geospatial analysis Computer architecture data models Artificial intelligence (AI) copernicus deep learning earth observation (EO) extremeearth food security hopsworks linked geospatial data polar regions remote sensing

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CARTOGRAPHY AND GEOGRAPHIC INFORMATION SCIENCE 2020年第2期47卷 125-140页

作者： Chen, Zugang Yang, Yaping Chinese Acad Sci Inst Remote Sensing & Digital Earth Beijing Peoples R China Chinese Acad Sci Inst Geog Sci & Nat Resources Res State Key Lab Resources & Environm Informat Syst Beijing Peoples R China

Advances in linked geospatial data, recommender systems, and geographic information retrieval have led to urgent necessity to assess the overall semantic relatedness between keyword sets of geographic metadata. In this study, a new model is proposed for computing the semantic relatedness between arbitrary two keyword sets of geographic metadata stored in current global spatial data infrastructures. In this model, the overall semantic relatedness is derived by pairing these keywords that are found to be most relevant to each other and averaging their relatedness. To find the most relevant keywords across two keyword sets precisely, the keywords in the keyword set of geographic metadata are divided into three kinds: the thesaurus elements, the WordNet elements, and the statistical elements. The thesaurus-lexical relatedness measure (TLRM), the extended thesaurus-lexical relatedness measure (ETLRM), and the Longest Common Substring method are proposed to compute the semantic relatedness between two thesaurus elements, two WordNet elements, a thesaurus element, and a WordNet element and two statistical elements, respectively. A human data set - the geographic-metadata's keyword set relatedness dataset, which was used to evaluate the precision of the semantic relatedness measures of keyword sets of geographic metadata, was created. The proposed method was evaluated against the human-generated relatedness judgments and was compared with the Jaccard method and Vector Space Model. The results demonstrated that the proposed method achieved a high correlation with human judgments and outperformed the existing methods. Finally, the proposed method was applied to quantitatively linked geospatial data.

关键词： Geographic metadata keyword sets semantic relatedness knowledge-based method linked geospatial data

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From Big data to Big Information and Big Knowledge: the Case of Earth Observation data 18

From Big Data to Big Information and Big Knowledge: the Case...

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27th ACM International Conference on Information and Knowledge Management (CIKM)

作者： Bereta, Konstantina Koubarakis, Manolis Manegold, Stefan Stamoulis, George Demir, Beguem Univ Athens Dept Informat & Telecommun Athens Greece CWI Database Architectures Grp Amsterdam Netherlands Tech Univ Berlin Fac Elect Engn & Comp Sci Berlin Germany

ISBN: (纸本)9781450360142

Some particularly important rich sources of open and free big geospatial data are the Earth observation (EO) programs of various countries such as the Landsat program of the US and the Copernicus programme of the European Union. EO data is a paradigmatic case of big data and the same is true for the big information and big knowledge extracted from it. EO data (satellite images and in-situ data), and the information and knowledge extracted from it, can be utilized in many applications with financial and environmental impact in areas such as emergency management, climate change, agriculture and security.

关键词： Semantic web linked geospatial data Earth observation data Copernicus program

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