Wildfire monitoring using satellite images, ontologies and linked geospatial data

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

Estimating Fire Weather Indices via Semantic Reasoning over Wireless Sensor Network Data Streams

Wildfires are frequent, devastating events in Australia that regularly cause significant loss of life and widespread property damage. Fire weather indices are a widely-adopted method for measuring fire danger and they play a significant role in issuing bushfire warnings and in anticipating demand for bushfire management resources. Existing systems that calculate fire weather indices are limited due to low spatial and temporal resolution. Localized wireless sensor networks, on the other hand, gather continuous sensor data measuring variables such as air temperature, relative humidity, rainfall and wind speed at high resolutions. However, using wireless sensor networks to estimate fire weather indices is a challenge due to data quality issues, lack of standard data formats and lack of agreement on thresholds and methods for calculating fire weather indices. Within the scope of this paper, we propose a standardized approach to calculating Fire Weather Indices (a.k.a. fire danger ratings) and overcome a number of the challenges by applying Semantic Web Technologies to the processing of data streams from a wireless sensor network deployed in the Springbrook region of South East Queensland. This paper describes the underlying ontologies, the semantic reasoning and the Semantic Fire Weather Index (SFWI) system that we have developed to enable domain experts to specify and adapt rules for calculating Fire Weather Indices. We also describe the Web-based mapping interface that we have developed, that enables users to improve their understanding of how fire weather indices vary over time within a particular region.Finally, we discuss our evaluation results that indicate that the proposed system outperforms state-of-the-art techniques in terms of accuracy, precision and query performance.Comment: 20pages, 12 figure

Managing big, linked, and open earth-observation data: Using the TELEIOS/LEO software stack

Big Earth-observation (EO) data that are made freely available by space agencies come from various archives. Therefore, users trying to develop an application need to search within these archives, discover the needed data, and integrate them into their application. In this article, we argue that if EO data are published using the linked data paradigm, then the data discovery, data integration, and development of applications becomes easier. We present the life cycle of big, linked, and open EO data and show how to support their various stages using the software stack developed by the European Union (EU) research projects TELEIOS and the Linked Open EO Data for Precision Farming (LEO). We also show how this stack of tools can be used to implement an operational wildfire-monitoring service

Real-Time Wildfire Monitoring Using Scientific Database and Linked Data Technologies

We present a real-time wildfire monitoring service that exploits satellite images and linked geospatial data to detect hotspots and monitor the evolution of fire fronts. The service makes heavy use of scientific database technologies (array databases, SciQL, data vaults) and linked data technologies (ontologies, linked geospatial data, stSPARQL) and is implemented on top of MonetDB and Strabon. The service is now operational at the National Observatory of Athens and has been used during the previous summer by emergency managers monitoring wildfires in Greece

Improving knowledge discovery from synthetic aperture radar images using the linked open data cloud and Sextant

In the last few years, thanks to projects like TELEIOS, the linked open data cloud has been rapidly populated with geospatial data some of it describing Earth Observation products (e.g., CORINE Land Cover, Urban Atlas). The abundance of this data can prove very useful to the new missions (e.g., Sentinels) as a means to increase the usability of the millions of images and EO products that are expected to be produced by these missions. In this paper, we explain the relevant opportunities by demonstrating how the process of knowledge discovery from TerraSAR-X images can be improved using linked open data and Sextant, a tool for browsing and exploration of linked geospatial data, as well as the creation of thematic maps

Real Time Fire Monitoring Using Semantic Web and Linked Open Data Technologies

Στα πλαίσια αυτής της διπλωματικής εργασίας ασχολούμαστε με την εφαρμογή των τεχνολογιών του Σημασιολογικού Ιστού και των Ανοιχτών Συνδεδεμένων Δεδομένων σε εφαρμογές Τηλεπισκόπησης. Παρουσιάζουμε μια εφαρμογή παρακολούθησης πυρκαγιών του Εθνικού Αστεροσκοπείου Αθηνών και εξηγούμε πως βελτιώσαμε αυτή την εφαρμογή με την χρήση των παραπάνω τεχνολογιών. Παρουσιάζουμε το σύστημα σημασιολογικών γεωχωρικών βάσεων δεδομένων Strabon και εξηγούμε πως επεκτείναμε την λειτουργικότητα του για να ικανοποιήσουμε τις ανάγκες που προκύπτουν από μία εφαρμογή Τηλεπισκόπησης όπως η παρακολούθηση πυρκαγιών. Παρουσιάζουμε κάποια γεωχωρικά δεδομένα που μετατρέψαμε σε RDF και δημοσιοποιήσαμε σύμφωνα με τις αρχές των Ανοιχτών Συνδεδεμένων Δεδομένων. Επίσης εξηγούμε πως τα δεδομένα αυτά με την χρήση τους συστήματος Strabon χρησιμοποιήθηκαν για την βελτίωση της ακρίβειας των δεδομένων παρακολούθησης πυρκαγιών και για την ταχεία παραγωγή σχετικών χαρτών. Τέλος παρουσιάζουμε μία αξιολόγηση της βελτιωμένης εφαρμογής.In the context of this thesis we discuss about applying Semantic Web and Linked Open Data technologies to Earth Observations applications. We present a fire monitoring application of the National Observatory of Athens and we explain how we improved this application by using the above technologies. We present the semantic geospatial database system Strabon and we explain how we extended its functionality in order to satisfy the requirements of earth observation applications such as fire monitoring. We present some geospatial datasets which we tranlsated into RDF and we published according to the Linked Open Data principles. Further, we explain how these datasets and the system Strabon were used for improving the accuracy of fire monitoring data and for rapid generation of relevant maps. Finally we present an evaluation of the improved application

Ontology driven multi-agent systems : an architecture for sensor web applications.

Thesis (Ph.D.)-University of KwaZulu-Natal, 2009.Advances in sensor technology and space science have resulted in the availability of vast quantities of high quality earth observation data. This data can be used for monitoring the earth and to enhance our understanding of natural processes. Sensor Web researchers are working on constructing a worldwide computing infrastructure that enables dynamic sharing and analysis of complex heterogeneous earth observation data sets. Key challenges that are currently being investigated include data integration; service discovery, reuse and composition; semantic interoperability; and system dynamism. Two emerging technologies that have shown promise in dealing with these challenges are ontologies and software agents. This research investigates how these technologies can be integrated into an Ontology Driven Multi-Agent System (ODMAS) for the Sensor Web. The research proposes an ODMAS framework and an implemented middleware platform, i.e. the Sensor Web Agent Platform (SWAP). SWAP deals with ontology construction, ontology use, and agent based design, implementation and deployment. It provides a semantic infrastructure, an abstract architecture, an internal agent architecture and a Multi-Agent System (MAS) middleware platform. Distinguishing features include: the incorporation of Bayesian Networks to represent and reason about uncertain knowledge; ontologies to describe system entities such as agent services, interaction protocols and agent workflows; and a flexible adapter based MAS platform that facilitates agent development, execution and deployment. SWAP aims to guide and ease the design, development and deployment of dynamic alerting and monitoring applications. The efficacy of SWAP is demonstrated by two satellite image processing applications, viz. wildfire detection and monitoring informal settlement. This approach can provide significant benefits to a wide range of Sensor Web users. These include: developers for deploying agents and agent based applications; end users for accessing, managing and visualising information provided by real time monitoring applications, and scientists who can use the Sensor Web as a scientific computing platform to facilitate knowledge sharing and discovery. An Ontology Driven Multi-Agent Sensor Web has the potential to forever change the way in which geospatial data and knowledge is accessed and used. This research describes this far reaching vision, identifies key challenges and provides a first step towards the vision

Geographica: A Benchmark for Geospatial RDF Stores

Geospatial extensions of SPARQL like GeoSPARQL and stSPARQL have recently been defined and corresponding geospatial RDF stores have been implemented. However, there is no widely used benchmark for evaluating geospatial RDF stores which takes into account recent advances to the state of the art in this area. In this paper, we develop a benchmark, called Geographica, which uses both real-world and synthetic data to test the offered functionality and the performance of some prominent geospatial RDF stores

Why Geospatial Linked Open Data for Smart Mobility?

While the concept of Smart Cities is gaining momentum around the world and government data are increasingly available and accessible on the World Wide Web, key issues remain about Open Data and data standards for smart cities. A better integration and interoperabilty of data through the World Wide Web is only possible when everyone agrees on the standards for data representation and sharing. Linked Open Data positions itself as a solution for such standardization, being a method of publishing structured data using standard Web technologies. This facilitates the interlinking between datasets, makes them readable by computers, and easily accesible on the World Wide Web. We illustrate this through the example of an evolution from a traditional Content Management System with a geoportal, to a semantic based aproach. The Traffic Safety Monitor was developed in the period of 2012-2015 to monitor the road safety and to support policy development on road safety in Flanders (the northern part of Belgium). The system is built as a Content Management System (CMS), with publication tools to present geospatial indicators on road safety (e.g. the number of accidents with cars and the number of positive alcohol tests) as Web maps using stardardized Open Geospatial Consortium Webservices. The Traffic Safety Monitor is currently further developed towards a Mobility Monitor. Here, the focus is on the development of a business process model for the semantic exchange and publication of spatial data using Linked Open Data principles targeting indicators of sustainable and smart mobility. In the future, the usability of cycling Infrastructure for vehicles such as mobility scooters, bicycle trailers etc. can be assessed using Linked Open Data. The data and metadata is published in Linked open data format, opening the door for their reuse by a wide range of (smart) applications