Spatio-temporal modeling of the topology of swarm behavior with persistence landscapes

We propose a method for modeling the topology of swarm behavior in a manner which facilitates the application of machine learning techniques such as clustering. This is achieved by modeling the persistence of topological features, such as connected components and holes, of the swarm with respect to time using zig-zag persistent homology. The output of this model is subsequently transformed into a representation known as a persistence landscape. This representation forms a vector space and therefore facilitates the application of machine learning techniques. The proposed model is validated using a real data set corresponding to a swarm of 300 fish. We demonstrate that it may be used to perform clustering of swarm behavior with respect to topological features

Episodes in space: qualitative representation and reasoning over spatio-temporal objects

There is growing interest in many application domains for the temporal treatment and manipulation of spatially referenced objects. Handling the time dimension in spatial databases can greatly enhance and extend their functionality and usability by offering means of understanding the spatial behaviour in time. Few works, to date, have been directed towards the development of formalisms for representation and reasoning in this domain. In this paper, a new approach is presented for the representation and reasoning over spatio-temporal relationships. The approach is simple and aims to satisfy the requirements of coherency, expressiveness and reasoning power. Consistent behaviours of spatial objects in time are denoted episodes. The topology of the domain is defined by decomposing episodes into representative components and relationships are defined between those components. Spatio-temporal reasoning is achieved by composing the relationships between the object components using constraint networks. New composition tables between simple spatio-temporal regions and between regions and volumes are also derived and used in the reasoning process

Automatic semantic and geometric enrichment of CityGML building models using HoG-based template matching

Semantically rich 3D building models give the potential for a wealth of rich geo-spatially-enabled applications such as cultural heritage augmented reality, urban planning, radio network planning and personal navigation. However, the majority of existing building models lack much if any semantic detail. This work demonstrates a novel method for automatically locating subclasses of windows and doors, using computer vision techniques including the histogram of oriented gradient (HoG) template matching, and automatically creating enriched CityGML content for the matched windows and doors. Good results were achieved for class identification with potential for further refinement of subclasses of windows and doors and other architectural features. It is part of a wider project to bring even richer semantic content to 3D geo-spatial building models

An evaluation of geo-ontology representation languages for supporting web retrieval of geographical information

The internet is the single largest information resource in the world. It is, however, not being used to is full potential. Currently most the information is written using syntactical machine readable languages such as HTML. These languages are limited in that they are only intended for human consumption. To fully unlock the potential of such a vast resource of information, we need to make the information not only machine readable but machine-understandable. In order to gain machine understanding we need semantic languages which are able to define meaning to the information being stored. Agents (human or machine) could then use this information in variety of different ways. A large amount of geographical information is currently being stored and delivered over the internet. Internet providers such as the Ordnance Survey are realizing the potential and are currently offering their data in GML format. Geographic digital libraries, such as the ADL, are being established. There is, however, the need to realize the potential of semantically enriching the geographic information to provide more automated and intelligent ways of managing and retrieving the data over the web

What can I do there? Towards the automatic discovery of place-related services and activities

The current web is rich in geographically referenced data. Mining, retrieving and sharing these data raises the need for rich geographical place name resources that record spatial and thematic elements of geographical places. Here, possible services offered at a place and human activities that can be practised there are considered useful concepts to discover and encode in place name resources. Recognising this dimension of place description can enhance information retrieval tasks by extending the range of possible queries and search criteria that relate to different place instances. This work proposes an automatic approach for the identification and extraction of service and activity-related concepts from multiple resources of textual descriptions of geographical place types. Frequent affordance patterns are identified and then applied to a corpus of resources to extract service and activity types associated with specific geographical place types. The evaluation experiments undertaken demonstrate the potential value of the approach

An ontology of place and service types to facilitate place-affordance geographic information retrieval

In order to facilitate place-affordance queries on the Web, this work proposes the employment of an ontology of place and service types. While other works defined place-affordance by associating a place with its physical objects, the conceptual view of a place-affordance in this work is based on associating a place type with its typical service types, which is reflected in the ontology construction methodology. Preliminary results, as well as an overview of the current work, are briefly introduced

Semantic and geometric enrichment of 3D geo-spatial models with captioned photos and labelled illustrations

There are many 3D digital models of buildings with cultural heritage interest, but most of them lack semantic annotation that could be used to inform users of mobile and desktop applications about their origins and architectural features. We describe methods in an ongoing project for enriching 3D models with generic annotation, derived from examples of images of building components and from labelled plans and diagrams, and with object-specific descriptions obtained from photo captions. This is the first stage of research that aims to annotate 3D models with facts extracted from the text of authoritative architectural guides

A filter flow visual querying language and interface for spatial databases

In this paper a visual approach to querying in spatial databases is presented. A filter flow methodology is used to consistently express different types of queries in these systems. Filters are used to represent operations on the database and pictorial icons are used throughout the language for filters, operators and spatial relations. Different granularities of the relations are presented in a hierarchical fashion for spatial constraints. The language framework and functions are described and examples are used to demonstrate its capabilities in representing different levels of queries, including spatial joins and composite spatial joins. Here, the primary focus is on the query language itself but an overvie