Geomatic based Urban Sprawl Detection of Salem City, India

Urban sprawl refers to the extent of urbanisation, which is a global phenomenon mainly driven by population growth and large scale migration. In developing countries like India, where the population is over one billion, one-sixth of the world’s population, urban sprawl is taking its toll on the natural resources at an alarming pace. Urban planners require information related to the rate of growth, pattern and extent of sprawl to provide basic amenities such as water, sanitation, electricity, etc. In the absence of such information, most of the sprawl areas lack basic infrastructure facilities. Pattern and extent of sprawl could be dectected with the help of  statelite images  and temporal data. This  is used to analysing the growth, pattern and extent of sprawl. This paper brings out the extent of sprawl taking place over a period of nearly four decades using GIS and Remote Sensing

Formalizing spatiotemporal knowledge in remote sensing applications to improve image interpretation

Technological tools allow the generation of large volumes of data. For example satellite images aid in the study of spatiotemporal phenomena in a range of disciplines such as urban planning environmental sciences and health care. Thus remote-sensing experts must handle various and complex image sets for their interpretations. The GIS community has undertaken significant work in describing spatiotemporal features and standard specifications nowadays provide design foundations for GIS software and spatial databases. We argue that this spatiotemporal knowledge and expertise would provide invaluable support for the field of image interpretation. As a result we propose a high level conceptual framework based on existing and standardized approaches offering enough modularity and adaptability to represent the various dimensions of spatiotemporal knowledge

An Integrated Software Framework to Support Semantic Modeling and Reasoning of Spatiotemporal Change of Geographical Objects: A Use Case of Land Use and Land Cover Change Study

abstract: Evolving Earth observation and change detection techniques enable the automatic identification of Land Use and Land Cover Change (LULCC) over a large extent from massive amounts of remote sensing data. It at the same time poses a major challenge in effective organization, representation and modeling of such information. This study proposes and implements an integrated computational framework to support the modeling, semantic and spatial reasoning of change information with regard to space, time and topology. We first proposed a conceptual model to formally represent the spatiotemporal variation of change data, which is essential knowledge to support various environmental and social studies, such as deforestation and urbanization studies. Then, a spatial ontology was created to encode these semantic spatiotemporal data in a machine-understandable format. Based on the knowledge defined in the ontology and related reasoning rules, a semantic platform was developed to support the semantic query and change trajectory reasoning of areas with LULCC. This semantic platform is innovative, as it integrates semantic and spatial reasoning into a coherent computational and operational software framework to support automated semantic analysis of time series data that can go beyond LULC datasets. In addition, this system scales well as the amount of data increases, validated by a number of experimental results. This work contributes significantly to both the geospatial Semantic Web and GIScience communities in terms of the establishment of the (web-based) semantic platform for collaborative question answering and decision-making

Geographic ontology for major disasters: methodology and implementation

During a catastrophic event, the International Charter1 "Space and Major Disasters" is regularly activated and provides the rescue teams damage maps prepared by a photo-interpreter team basing on pre and post-disaster satellite images. A satellite image manual processing must be accomplished in most cases to build these maps, a complex and demanding process. Given the importance of time in such critical situations, automatic or semiautomatic tools are highly recommended. Despite the quick treatment presented by automatic processing, it usually presents a semantic gap issue. Our aim is to express expert knowledge using a well-defined knowledge representation method: ontologies and make semantics explicit in geographic and remote sensing applications by taking the ontology advantages in knowledge representation, expression, and knowledge discovery. This research focuses on the design and implementation of a comprehensive geographic ontology in the case of major disasters, that we named GEO-MD, and illustrates its application in the case of Haiti 2010 earthquake. Results show how the ontology integration reduces the semantic gap and improves the automatic classification accuracy

An object-relational prototype of a GIS-based disaster database

Natural disasters cause billions of dollars of property and infrastructure damage, unexpected disruption to socio-economic activities and tragic loss of human lives each year. The importance of collecting and maintaining detailed and accurate records on disastrous events for an effective risk assessment and disaster mitigation has been widely recognised. Considerable efforts have been directed towards the establishment of databases on historic disasters but many disaster databases built are primarily a set of lists of historical disaster events. Disaster phenomena vary dramatically with both space and time. It is therefore important to integrate spatial-temporal dimensions of disaster events in a disaster database to support efficient and interactive querying and reporting operations. It is also important to make such a database readily accessible by a variety of users from government agencies, non-government organisations, research institutes and local communities, to enable effective and efficient emergency response, impact and risk assessment, and mitigation planning. This thesis presents a study that investigates effective and efficient geographical information system (GIS) based approaches to the representation, organisation and access of disaster information - including logical data models for representing disastrous events, the object-relational approach to database implementation, and internet-based user-interfaces for database queries and report generation. Key aspects of a disaster event, including the spatial-temporal dimensions of the hazard and its impacts, are considered in the development of data models and database implementation in order to support user-friendly querying and reporting operations. The technological strengths of GIS, database management systems, and Internet-related toolboxes are leveraged for developing a prototype of a GIS-based, object-relational disaster database with an Internet-based user interface that supports multi-mode (including map-based) database queries and flexible facilities for report generation

An Integrated Representation of Spatial and Temporal Relationships between Evolving Regions

Abstract. The study of relationships between evolving regions within GIS still needs the development of operators that integrate the spatial and temporal dimensions. This paper introduces a new approach that combines topological relationships between regions in 2-dimensional space with temporal relationships between convex intervals in time. Resulting relationships are defined and visually presented within a 3-dimensional space that integrates the geographical space as a 2-dimensional space and the time line as the third dimension. Conceptual neighbourhoods are identified and extended by the concept of semi-transitions and transitions. Such a flexible framework presents the advantage of being derived from accepted relationships in both space and time. Its computational implementation is therefore compatible with current spatial and temporal GIS models. Key words: GIS, time, space, relationships

Modeling Visit Potential of Geographic Locations Based on Mobility Data

Every day people interact with the environment by passing or visiting geographic locations. Information about such entity-location interactions can be used in a number of applications and its value has been recognized by companies and public institutions. However, although the necessary tracking technologies such as GPS, GSM or RFID have long found their way into everyday life, the practical usage of visit information is still limited. Besides economic and ethical reasons for the restricted usage of entity-location interactions there are also two very basic problems. First, no formal definition of entity-location interaction quantities exists. Second, at the current state of technology, no tracking technology guarantees complete observations, and the treatment of missing data in mobility applications has been neglected in trajectory data mining so far. This thesis therefore focuses on the definition and estimation of quantities about the visiting behavior between mobile entities and geographic locations from incomplete mobility data. In a first step we provide an application-independent language to evaluate entity-location interactions. Based on a uniform notation, we define a family of quantities called visit potential, which contains the most basic interaction quantities and can be extended on need. By identifying the common background of all quantities we are able to analyze relationships between different quantities and to infer consistency requirements between related parameterizations of the quantities. We demonstrate the general applicability of visit potential using two real-world applications for which we give a precise definition of the employed entity-location interaction quantities in terms of visit potential. Second, this thesis provides the first systematic analysis of methods for the handling of missing data in mobility mining. We select a set of promising methods that take different approaches to handling missing data and test their robustness with respect to different scenarios. Our analyses consider different mechanisms and intensities of missing data under artificial censoring as well as varying visit intensities. We hereby analyze not only the applicability of the selected methods but also provide a systematic approach for parameterization and testing that can also be applied to the analysis of other mobility data sets. Our experiments show that only two of the tested methods supply unbiased estimates of visit potential quantities and are applicable to the domain. In addition, both methods supply unbiased estimates only of a single quantity. Therefore, it will be a future challenge to design methods for the entire collection of visit potential quantities. The topic of this thesis is motivated by applied research at the Fraunhofer Institute for Intelligent Analysis and Information Systems IAIS for business applications in outdoor advertisement. We will use the outdoor advertisement scenario throughout this thesis for demonstration and experimentation.Modellierung von Besuchsgrößen geographischer Orte anhand von Mobilitätsdaten Täglich interagieren Menschen mit ihrer Umgebung, indem sie sich im geografischen Raum bewegen oder gezielt geografische Orte aufsuchen. Informationen über derartige Besuche sind sehr wertvoll und können in einer Reihe von Anwendungen eingesetzt werden. Üblicherweise werden dazu die Bewegungen von Personen mit Hilfe von GPS, GSM oder RFID Technologien verfolgt. Durch eine räumliche Verschneidung der Trajektorien mit der Positionsangabe eines bestimmten Ortes können dann die Besuche extrahiert werden. Allerdings ist derzeitig die Verwendung von Besuchsinformationen in der Praxis begrenzt. Dies hat, neben ökonomischen und ethischen Gründen, vor allem zwei grundlegende Ursachen. Erstens existiert keine formelle Definition von Größen, um Besuchsinformationen einheitlich auszuwerten. Zweitens können aktuelle Technologien keine vollständige Erfassung von Bewegungsinformationen garantieren. Das bedeutet, dass die Basisdaten zur Auswertung von Besuchsinformationen grundsätzlich Lücken enthalten. Für eine fehlerfreie Auswertung der Daten müssen diese Lücken adäquat behandelt werden. Allerdings wurde dieses Thema in der bisherigen Data Mining Literatur zur Auswertung von Bewegungsdaten vernachlässigt. Daher widmet sich diese Dissertation der Definition von Größen zur Auswertung von Besuchsinformationen sowie dem Schätzen dieser Größen aus unvollständigen Bewegungsdaten. Im ersten Teil der Dissertation wird eine anwendungsunabhängige Beschreibungssprache formuliert, um Besuchsinformationen auszuwerten. Auf Basis einer einheitlichen Notation wird eine Familie von Größen namens visit potential definiert, die grundlegende Besuchsgrößen enthält und offen für Erweiterungen ist. Die gemeinsame Basis aller Besuchsgrößen erlaubt weiterhin, Beziehungen zwischen verschiedenen Größen zu analysieren sowie Konsistenzanforderungen zwischen ähnlichen Parametrisierungen der Größen abzuleiten. Abschließend zeigt die Arbeit die generelle Anwendbarkeit der definierten Besuchsgrößen in zwei realen Anwendungen, für die eine präzise Definition der eingesetzten Statistiken mit Hilfe der Besuchsgrößen gegeben wird. Der zweite Teil der Dissertation enthält die erste systematische Methodenanalyse für die Handhabung von unvollständigen Bewegungsdaten. Hierfür werden vier vielversprechende Methoden aus unterschiedlichen Bereichen zur Behandlung von fehlenden Daten ausgewählt und auf ihre Robustheit unter verschiedenen Annahmen getestet. Mit Hilfe einer künstlichen Zensur werden verschiedene Mechanismen und Grade von fehlenden Daten untersucht. Außerdem wird die Robustheit der Methoden für verschieden hohe Besuchsniveaus betrachtet. Die durchgeführten Experimente geben dabei nicht nur Auskunft über die Anwendbarkeit der getesteten Methoden, sondern stellen auch ein systematisches Vorgehen für das Testen und Parametrisieren weiterer Methoden zur Verfügung. Die Ergebnisse der Experimente belegen, dass nur zwei der vier ausgewählten Methoden für die Schätzung von Besuchsgrößen geeignet sind. Beide Methoden liefern jedoch nur für jeweils eine Besuchsgröße erwartungstreue Schätzwerte. Daher besteht eine zukünftige Herausforderung darin, Schätzmethoden für die Gesamtheit an Besuchsgrößen zu entwickeln. Diese Arbeit ist durch anwendungsorientierte Forschung am Fraunhofer-Institut für Intelligente Analyse- und Informationssysteme IAIS im Bereich der Außenwerbung motiviert. Das Außenwerbeszenario sowie die darüber zur Verfügung gestellten Anwendungsdaten werden durchgängig zur Demonstration und für die Experimente in der Arbeit eingesetzt