A Survey of Volunteered Open Geo-Knowledge Bases in the Semantic Web

Over the past decade, rapid advances in web technologies, coupled with innovative models of spatial data collection and consumption, have generated a robust growth in geo-referenced information, resulting in spatial information overload. Increasing 'geographic intelligence' in traditional text-based information retrieval has become a prominent approach to respond to this issue and to fulfill users' spatial information needs. Numerous efforts in the Semantic Geospatial Web, Volunteered Geographic Information (VGI), and the Linking Open Data initiative have converged in a constellation of open knowledge bases, freely available online. In this article, we survey these open knowledge bases, focusing on their geospatial dimension. Particular attention is devoted to the crucial issue of the quality of geo-knowledge bases, as well as of crowdsourced data. A new knowledge base, the OpenStreetMap Semantic Network, is outlined as our contribution to this area. Research directions in information integration and Geographic Information Retrieval (GIR) are then reviewed, with a critical discussion of their current limitations and future prospects

Internet of things

Manual of Digital Earth / Editors: Huadong Guo, Michael F. Goodchild, Alessandro Annoni .- Springer, 2020 .- ISBN: 978-981-32-9915-3Digital Earth was born with the aim of replicating the real world within the digital world. Many efforts have been made to observe and sense the Earth, both from space (remote sensing) and by using in situ sensors. Focusing on the latter, advances in Digital Earth have established vital bridges to exploit these sensors and their networks by taking location as a key element. The current era of connectivity envisions that everything is connected to everything. The concept of the Internet of Things(IoT)emergedasaholisticproposaltoenableanecosystemofvaried,heterogeneous networked objects and devices to speak to and interact with each other. To make the IoT ecosystem a reality, it is necessary to understand the electronic components, communication protocols, real-time analysis techniques, and the location of the objects and devices. The IoT ecosystem and the Digital Earth (DE) jointly form interrelated infrastructures for addressing today’s pressing issues and complex challenges. In this chapter, we explore the synergies and frictions in establishing an efficient and permanent collaboration between the two infrastructures, in order to adequately address multidisciplinary and increasingly complex real-world problems. Although there are still some pending issues, the identified synergies generate optimism for a true collaboration between the Internet of Things and the Digital Earth

OpenKnowledge at work: exploring centralized and decentralized information gathering in emergency contexts

Real-world experience teaches us that to manage emergencies, efficient crisis response coordination is crucial; ICT infrastructures are effective in supporting the people involved in such contexts, by supporting effective ways of interaction. They also should provide innovative means of communication and information management. At present, centralized architectures are mostly used for this purpose; however, alternative infrastructures based on the use of distributed information sources, are currently being explored, studied and analyzed. This paper aims at investigating the capability of a novel approach (developed within the European project OpenKnowledge1) to support centralized as well as decentralized architectures for information gathering. For this purpose we developed an agent-based e-Response simulation environment fully integrated with the OpenKnowledge infrastructure and through which existing emergency plans are modelled and simulated. Preliminary results show the OpenKnowledge capability of supporting the two afore-mentioned architectures and, under ideal assumptions, a comparable performance in both cases

Approaches for the clustering of geographic metadata and the automatic detection of quasi-spatial dataset series

The discrete representation of resources in geospatial catalogues affects their information retrieval performance. The performance could be improved by using automatically generated clusters of related resources, which we name quasi-spatial dataset series. This work evaluates whether a clustering process can create quasi-spatial dataset series using only textual information from metadata elements. We assess the combination of different kinds of text cleaning approaches, word and sentence-embeddings representations (Word2Vec, GloVe, FastText, ELMo, Sentence BERT, and Universal Sentence Encoder), and clustering techniques (K-Means, DBSCAN, OPTICS, and agglomerative clustering) for the task. The results demonstrate that combining word-embeddings representations with an agglomerative-based clustering creates better quasi-spatial dataset series than the other approaches. In addition, we have found that the ELMo representation with agglomerative clustering produces good results without any preprocessing step for text cleaning

Use of Semantic Technology to Create Curated Data Albums

One of the continuing challenges in any Earth science investigation is the discovery and access of useful science content from the increasingly large volumes of Earth science data and related information available online. Current Earth science data systems are designed with the assumption that researchers access data primarily by instrument or geophysical parameter. Those who know exactly the data sets they need can obtain the specific files using these systems. However, in cases where researchers are interested in studying an event of research interest, they must manually assemble a variety of relevant data sets by searching the different distributed data systems. Consequently, there is a need to design and build specialized search and discovery tools in Earth science that can filter through large volumes of distributed online data and information and only aggregate the relevant resources needed to support climatology and case studies. This paper presents a specialized search and discovery tool that automatically creates curated Data Albums. The tool was designed to enable key elements of the search process such as dynamic interaction and sense-making. The tool supports dynamic interaction via different modes of interactivity and visual presentation of information. The compilation of information and data into a Data Album is analogous to a shoebox within the sense-making framework. This tool automates most of the tedious information/data gathering tasks for researchers. Data curation by the tool is achieved via an ontology-based, relevancy ranking algorithm that filters out non-relevant information and data. The curation enables better search results as compared to the simple keyword searches provided by existing data systems in Earth science

Training of Crisis Mappers and Map Production from Multi-sensor Data: Vernazza Case Study (Cinque Terre National Park, Italy)

This aim of paper is to presents the development of a multidisciplinary project carried out by the cooperation between Politecnico di Torino and ITHACA (Information Technology for Humanitarian Assistance, Cooperation and Action). The goal of the project was the training in geospatial data acquiring and processing for students attending Architecture and Engineering Courses, in order to start up a team of "volunteer mappers". Indeed, the project is aimed to document the environmental and built heritage subject to disaster; the purpose is to improve the capabilities of the actors involved in the activities connected in geospatial data collection, integration and sharing. The proposed area for testing the training activities is the Cinque Terre National Park, registered in the World Heritage List since 1997. The area was affected by flood on the 25th of October 2011. According to other international experiences, the group is expected to be active after emergencies in order to upgrade maps, using data acquired by typical geomatic methods and techniques such as terrestrial and aerial Lidar, close-range and aerial photogrammetry, topographic and GNSS instruments etc.; or by non conventional systems and instruments such us UAV, mobile mapping etc. The ultimate goal is to implement a WebGIS platform to share all the data collected with local authorities and the Civil Protectio

Dynamic data placement and discovery in wide-area networks

The workloads of online services and applications such as social networks, sensor data platforms and web search engines have become increasingly global and dynamic, setting new challenges to providing users with low latency access to data. To achieve this, these services typically leverage a multi-site wide-area networked infrastructure. Data access latency in such an infrastructure depends on the network paths between users and data, which is determined by the data placement and discovery strategies. Current strategies are static, which offer low latencies upon deployment but worse performance under a dynamic workload. We propose dynamic data placement and discovery strategies for wide-area networked infrastructures, which adapt to the data access workload. We achieve this with data activity correlation (DAC), an application-agnostic approach for determining the correlations between data items based on access pattern similarities. By dynamically clustering data according to DAC, network traffic in clusters is kept local. We utilise DAC as a key component in reducing access latencies for two application scenarios, emphasising different aspects of the problem: The first scenario assumes the fixed placement of data at sites, and thus focusses on data discovery. This is the case for a global sensor discovery platform, which aims to provide low latency discovery of sensor metadata. We present a self-organising hierarchical infrastructure consisting of multiple DAC clusters, maintained with an online and distributed split-and-merge algorithm. This reduces the number of sites visited, and thus latency, during discovery for a variety of workloads. The second scenario focusses on data placement. This is the case for global online services that leverage a multi-data centre deployment to provide users with low latency access to data. We present a geo-dynamic partitioning middleware, which maintains DAC clusters with an online elastic partition algorithm. It supports the geo-aware placement of partitions across data centres according to the workload. This provides globally distributed users with low latency access to data for static and dynamic workloads.Open Acces