Linking geographic vocabularies through WordNet

The linked open data (LOD) paradigm has emerged as a promising approach to structuring and sharing geospatial information. One of the major obstacles to this vision lies in the difficulties found in the automatic integration between heterogeneous vocabularies and ontologies that provides the semantic backbone of the growing constellation of open geo-knowledge bases. In this article, we show how to utilize WordNet as a semantic hub to increase the integration of LOD. With this purpose in mind, we devise Voc2WordNet, an unsupervised mapping technique between a given vocabulary and WordNet, combining intensional and extensional aspects of the geographic terms. Voc2WordNet is evaluated against a sample of human-generated alignments with the OpenStreetMap (OSM) Semantic Network, a crowdsourced geospatial resource, and the GeoNames ontology, the vocabulary of a large digital gazetteer. These empirical results indicate that the approach can obtain high precision and recall

Web data extraction systems versus research collaboration in sustainable planning for housing: Smart governance takes it all

To date, there are no clear insights in the spatial patterns and micro-dynamics of the housing market. The objective of this study is to collect real estate micro-data for the development of policy-support indicators on housing market dynamics at the local scale. These indicators can provide the requested insights in spatial patterns and micro-dynamics of the housing market. Because the required real estate data are not systematicly published as statistical data or open data, innovative forms of data collection are needed. This paper is based on a case study approach of the greater Leuven area (Belgium). The research question is what are suitable methods or strategies to collect data on micro-dynamics of the housing market. The methodology includes a technical approach for data collection, being Web data extraction, and a governance approach, being explorative interviews. A Web data extraction system collects and extracts unstructured or semi-structured data that are stored or published on Web sources. Most of the required data are publicly and readily available as Web data on real estate portal websites. Web data extraction at the scale of the case study succeeded in collecting the required micro-data, but a trial run at the regional scale encountered a number of practical and legal issues. Simultaneously with the Web data extraction, the dialogue with two real estate portal websites was initiated, using purposive sampling and explorative semi-structured interviews. The interviews were considered as the start of a transdisciplinary research collaboration process. Both companies indicated that the development of indicators about housing market dynamics was a good and relevant idea, yet a challenging task. The companies were familiar with Web data extraction systems, but considered it a suboptimal technique to collect real estate data for the development of housing dynamics indicators. They preferred an active collaboration instead of passive Web scraping. In the frame of a users’ agreement, we received one company’s dataset and calculated the indicators for the case study based on this dataset. The unique micro-data provided by the company proved to be the start of a collaborative planning approach between private partners, the academic world and the Flemish government. All three win from this collaboration on the long run. Smart governance can gain from smart technologies, but should not loose sight of active collaborations

GeoAI-enhanced Techniques to Support Geographical Knowledge Discovery from Big Geospatial Data

abstract: Big data that contain geo-referenced attributes have significantly reformed the way that I process and analyze geospatial data. Compared with the expected benefits received in the data-rich environment, more data have not always contributed to more accurate analysis. “Big but valueless” has becoming a critical concern to the community of GIScience and data-driven geography. As a highly-utilized function of GeoAI technique, deep learning models designed for processing geospatial data integrate powerful computing hardware and deep neural networks into various dimensions of geography to effectively discover the representation of data. However, limitations of these deep learning models have also been reported when People may have to spend much time on preparing training data for implementing a deep learning model. The objective of this dissertation research is to promote state-of-the-art deep learning models in discovering the representation, value and hidden knowledge of GIS and remote sensing data, through three research approaches. The first methodological framework aims to unify varied shadow into limited number of patterns, with the convolutional neural network (CNNs)-powered shape classification, multifarious shadow shapes with a limited number of representative shadow patterns for efficient shadow-based building height estimation. The second research focus integrates semantic analysis into a framework of various state-of-the-art CNNs to support human-level understanding of map content. The final research approach of this dissertation focuses on normalizing geospatial domain knowledge to promote the transferability of a CNN’s model to land-use/land-cover classification. This research reports a method designed to discover detailed land-use/land-cover types that might be challenging for a state-of-the-art CNN’s model that previously performed well on land-cover classification only.Dissertation/ThesisDoctoral Dissertation Geography 201

An Intelligent Multi-Agent System Approach to Automating Safety Features for On-Line Real Time Communications: Agent Mediated Information Exchange

Child safety online is a growing problem, governmental attempts to highlight and combat this issue have not been as successful as it was hoped, and still there are highly publicised cases of children, young people and vulnerable adults coming to harm as a result of unsafe online practices. This thesis presents the research, design and development of a prototype system called SafeChat, which will provide a safer environment for children interacting in online environments. In order to combat such a complex problem, it is necessary to integrate various artificial intelligent technologies and autonomous systems. The SafeChat prototype system discussed within this research has been implemented in Java Agent Development Environment (JADE) and utilises Protégé Ontology development, reasoning and natural language processing techniques. To evaluate our system performance, comprehensive testing to measure its effectiveness in detecting potential risk to the user (e.g. child) is in constant development. Initial results of system testing are encouraging and demonstrate its effectiveness in identifying different levels of threat during online conversation. The potential impact of this work is immense, when used as a plug-in to popular communications software, such as Facebook Messenger, Skype and WhatsApp. SafeChat provides a safer environment for children to communicate, identifying potential and actual threats, whilst maintaining the privacy of their discourse. The SafeChat system could be easily adapted to provide autonomous solutions in other areas of online threat, such as cyberbullying and radicalisation