A anotação geográfica de documentos consiste na adoção de metadados para a identificação de nomes de locais e a posição de suas ocorrências no texto. Esta informação é útil, por exemplo, para mecanismos de busca. A partir dos topônimos mencionados no texto é possível identificar o contexto espacial em que o assunto do texto está inserido, o que permite agrupar documentos que se refiram a um mesmo contexto, atribuindo ao documento um escopo geográfico. Esta Dissertação de Mestrado apresenta um novo método, batizado de Geofier, para determinação do escopo geográfico de documentos. A novidade apresentada pelo Geofier é a possibilidade da identificação do escopo geográfico de um documento por meio de classificadores de aprendizagem de máquina treinados sem o uso de um gazetteer e sem premissas quanto à língua dos textos analisados. A Wikipédia foi utilizada como fonte de um conjunto de documentos anotados geograficamente para o treinamento de uma hierarquia de Classificadores Naive Bayes e Support Vector Machines (SVMs). Uma comparação de desempenho entre o Geofier e uma reimplementação do sistema Web-a-Where foi realizada em relação à determinação do escopo geográfico dos textos da Wikipédia. A hierarquia do Geofier foi treinada e avaliada de duas formas: usando topônimos do mesmo gazetteer que o Web-a-Where e usando n-gramas extraídos dos documentos de treinamento. Como resultado, o Geofier manteve desempenho superior ao obtido pela reimplementação do Web-a-Where. / Automatic text geotagging is the process by which mentions of place names and their positions in text are identified as metadata, allowing this information to be used by specialized applications, like Search Engines. It is possible to identify the geographic scope of a document by analysing the toponyms it mentions and then group documents by their geographic context, effectively adding a geographic scope to the documents. This dissertation presents a new method to identify the geographic scope of text, named Geofier. The novelty in Geofier is that it uses machine learning text classifiers, trained without the need of a gazetteer and without making assumptions regarding the language in which the documents are written. Wikipedia was used as the source for a geotagged text dataset in order to train a hierarchy of Naive Bayes and Support Vector Machine (SVM) classifiers. The Geofier hierarchy was then trained and evaluated, first using toponyms from the same gazetteer as Web-a-Where and then using n-grams extracted from the training samples as attributes. Geofier performed significantly better when compared to a Web-a-Where implementation.

A anotação geográfica de documentos consiste na adoção de metadados para a identificação de nomes de locais e a posição de suas ocorrências no texto. Esta informação é útil, por exemplo, para mecanismos de busca. A partir dos topônimos mencionados no texto é possível identificar o contexto espacial em que o assunto do texto está inserido, o que permite agrupar documentos que se refiram a um mesmo contexto, atribuindo ao documento um escopo geográfico. Esta Dissertação de Mestrado apresenta um novo método, batizado de Geofier, para determinação do escopo geográfico de documentos. A novidade apresentada pelo Geofier é a possibilidade da identificação do escopo geográfico de um documento por meio de classificadores de aprendizagem de máquina treinados sem o uso de um gazetteer e sem premissas quanto à língua dos textos analisados. A Wikipédia foi utilizada como fonte de um conjunto de documentos anotados geograficamente para o treinamento de uma hierarquia de Classificadores Naive Bayes e Support Vector Machines (SVMs). Uma comparação de desempenho entre o Geofier e uma reimplementação do sistema Web-a-Where foi realizada em relação à determinação do escopo geográfico dos textos da Wikipédia. A hierarquia do Geofier foi treinada e avaliada de duas formas: usando topônimos do mesmo gazetteer que o Web-a-Where e usando n-gramas extraídos dos documentos de treinamento. Como resultado, o Geofier manteve desempenho superior ao obtido pela reimplementação do Web-a-Where. / Automatic text geotagging is the process by which mentions of place names and their positions in text are identified as metadata, allowing this information to be used by specialized applications, like Search Engines. It is possible to identify the geographic scope of a document by analysing the toponyms it mentions and then group documents by their geographic context, effectively adding a geographic scope to the documents. This dissertation presents a new method to identify the geographic scope of text, named Geofier. The novelty in Geofier is that it uses machine learning text classifiers, trained without the need of a gazetteer and without making assumptions regarding the language in which the documents are written. Wikipedia was used as the source for a geotagged text dataset in order to train a hierarchy of Naive Bayes and Support Vector Machine (SVM) classifiers. The Geofier hierarchy was then trained and evaluated, first using toponyms from the same gazetteer as Web-a-Where and then using n-grams extracted from the training samples as attributes. Geofier performed significantly better when compared to a Web-a-Where implementation.

This thesis investigates automatic geolocation (i.e. identification of the location, expressed as latitude/longitude coordinates) of documents. Geolocation can be an effective means of summarizing large document collections and is an important component of geographic information retrieval. We describe several simple supervised methods for document geolocation using only the document’s raw text as evidence. All of our methods predict locations in the context of geodesic grids of varying degrees of resolution. We evaluate the methods on geotagged Wikipedia articles and Twitter feeds. For Wikipedia, our best method obtains a median prediction error of just 11.8 kilometers. Twitter geolocation is more challenging: we obtain a median error of 479 km, an improvement on previous results for the dataset. / text

This master thesis describes development of Photography Geotagging Application from an introduction to the issues, through the analysis and design to the resulting implementation. The reader should be able understand to issues of time in geotagging using the GPS tracks records. He will also understand where and how metadata are stored. The big part of thesis is dedicated to the implementation of application which leads the reader through building the application architecture, the photography representation and loading their metadata with program ExifTool. Gradually will be explained the convertors of coordinates and GPS track parsers, which are used to synchronization. The final application allows the user synchronize photography with GPS track and display them on the map.

This master thesis deals with wireless sensor network with protocol IPv6. In the begining there are explained the priciples of communication in wireless sensor network, and based on this teories are configured a wireles network with predefined development kit. Furthermore, by using geographic coordinates, positions of the nodes are displayed on a website, what is a part of this thesis. On this website are also presented the measured values from sensors. For saving all informations and measured values about each nodes was used remote MySQL server. Communication between this server and 6LoWPAN network provide Perl script which is runs at periodical intervals. With this method automatization of the whole system are ensured.

The aim of this work is to create an application on Windows Mobile 6 platform for photo publication on the internet. Photos will be associated with geographic information. User will be able to attach geographic information to the photograph with use of connected GPS device and subsequently send these photographs to online publication system directly from mobile device.

The usage of location in mobile applications is becoming more explored as a field, but the ability to implement location-specific data has often been limited and the applications mainly focusing on having information location liked. This thesis aims to explore how to make a mobile application that lets users share and take part of each other’s memories. As well as finding an appropriate balance between location based and location linked messaging.

La technologie Web 2.0 a donné lieu à un large éventail de plates-formes de partage de photos. Il est désormais possible d’annoter des images de manière collaborative, au moyen de mots-clés; ce qui permet une gestion et une recherche efficace de ces images. Toutefois, l’annotation manuelle est laborieuse et chronophage. Au cours des dernières années, le nombre grandissant de photos annotées accessibles sur le Web a permis d'expérimenter de nouvelles méthodes d'annotation automatique d'images. L'idée est d’identifier, dans le cas d’une photo non annotée, un ensemble d'images visuellement similaires et, a fortiori, leurs mots-clés, fournis par la communauté. Il existe actuellement un nombre considérable de photos associées à des informations de localisation, c'est-à-dire géo-localisées. Nous exploiterons, dans le cadre de cette thèse, ces informations et proposerons une nouvelle approche pour l'annotation automatique d'images géo-localisées. Notre objectif est de répondre aux principales limites des approches de l'état de l'art, particulièrement concernant la qualité des annotations produites ainsi que la rapidité du processus d'annotation. Tout d'abord, nous présenterons une méthode de collecte de données annotées à partir du Web, en se basant sur la localisation des photos et les liens sociaux entre leurs auteurs. Par la suite, nous proposerons une nouvelle approche afin de résoudre l’ambiguïté propre aux tags d’utilisateurs, le tout afin d’assurer la qualité des annotations. L'approche démontre l'efficacité de l'algorithme de recherche de caractéristiques discriminantes, dit de Laplace, dans le but d’améliorer la représentation de l'annotation. En outre, une nouvelle mesure de distance entre mots-clés sera présentée, qui étend la divergence de Jensen-Shannon en tenant compte des fluctuations statistiques. Dans le but d'identifier efficacement les images visuellement proches, la thèse étend sur deux point l'algorithme d'état de l'art en comparaison d'images, appelé SURF (Speeded-Up Robust Features). Premièrement, nous présenterons une solution pour filtrer les points-clés SURF les plus significatifs, au moyen de techniques de classification, ce qui accélère l'exécution de l'algorithme. Deuxièmement, la précision du SURF sera améliorée, grâce à une comparaison itérative des images. Nous proposerons une un modèle statistique pour classer les annotations récupérées selon leur pertinence du point de vue de l'image-cible. Ce modèle combine différents critères, il est centré sur la règle de Bayes. Enfin, l'efficacité de l'approche d'annotation ainsi que celle des contributions individuelles sera démontrée expérimentalement. / In the Web 2.0 era, platforms for sharing and collaboratively annotating images with keywords, called tags, became very popular. Tags are a powerful means for organizing and retrieving photos. However, manual tagging is time consuming. Recently, the sheer amount of user-tagged photos available on the Web encouraged researchers to explore new techniques for automatic image annotation. The idea is to annotate an unlabeled image by propagating the labels of community photos that are visually similar to it. Most recently, an ever increasing amount of community photos is also associated with location information, i.e., geotagged. In this thesis, we aim at exploiting the location context and propose an approach for automatically annotating geotagged photos. Our objective is to address the main limitations of state-of-the-art approaches in terms of the quality of the produced tags and the speed of the complete annotation process. To achieve these goals, we, first, deal with the problem of collecting images with the associated metadata from online repositories. Accordingly, we introduce a strategy for data crawling that takes advantage of location information and the social relationships among the contributors of the photos. To improve the quality of the collected user-tags, we present a method for resolving their ambiguity based on tag relatedness information. In this respect, we propose an approach for representing tags as probability distributions based on the algorithm of Laplacian Score feature selection. Furthermore, we propose a new metric for calculating the distance between tag probability distributions by extending Jensen-Shannon Divergence to account for statistical fluctuations. To efficiently identify the visual neighbors, the thesis introduces two extensions to the state-of-the-art image matching algorithm, known as Speeded Up Robust Features (SURF). To speed up the matching, we present a solution for reducing the number of compared SURF descriptors based on classification techniques, while the accuracy of SURF is improved through an efficient method for iterative image matching. Furthermore, we propose a statistical model for ranking the mined annotations according to their relevance to the target image. This is achieved by combining multi-modal information in a statistical framework based on Bayes' Rule. Finally, the effectiveness of each of mentioned contributions as well as the complete automatic annotation process are evaluated experimentally.

I arbetet undersöks, utifrån ett telekrigperspektiv med inriktning från ett signalspaningshot, riskerna med nyttjandet av smarta mobiltelefoner i svenska markförband. Undersökningen baseras på ett fiktivt scenario i en nationell kontext och utgår ifrån två aktörer där ena aktören nyttjar smarta mobiltelefoner och andra aktören nyttjar högteknologiska telekrigsystem för att exploatera dess risker. Utifrån, i arbetet framtagna riskindikatorer, skapas olika händelseutvecklingar vilka undersöks utifrån en kvalitativ metod med inslag av kvantitativa beräkningar. Resultatet påvisar att smarta mobiltelefoner, nyttjade inom ramen för svenska markförband, riskerar utsätta såväl individer som hela förband för risker. Riskerna vilka undersöks är avlyssning, lägesbestämning och avslöjande av information genom mobiltelefonernas GNSS-förmåga och geotaggningsfunktioner. Användning av smarta mobiltelefoner riskerar således oavsiktligt röja information varför nyttjandet kräver förståelse för de risker dessa genererar. Riskerna kan inte undvikas varför nyttan med smarta mobiltelefoner måste vägas mot risken i varje situation. / In this thesis, the risks of smartphones in Swedish land forces are analysed from anElectronic Warfare perspective, focusing on Signals Intelligence. The analysis is based on a fictitious scenario in a national context involving two differentactors. One actor uses smartphones and the other actor uses high technology ElectronicWarfare systems to exploit the phones. Based on risk indicators developed in the thesis, twodifferent cases are created, which are then analysed using a qualitative method with elementsof quantitative calculations. The results reveal that smartphones, used in Swedish land forces, put both individuals andunits at risk. The risks, analysed, are interception, location and revealing information fromthe phone’s Global Navigation Satellite System geotagging and functions. Therefore, the useof smartphones might unintentionally reveal information. Consequently, the use ofsmartphones requires an understanding of the risks generated. It is not possible to avoid risks,so the benefit of using smartphones must be weighed against the risk in each situation.

Increasingly, online social media networks allow users to use geotagging. This method of adding location data to various content shared in real time has introduced privacy related issues and threats to the users of such networks. Previous research present opposing findings on whether users actually care about their location privacy or not, and it has also been shown that users often display a behaviour inconsistent with their concerns. When asked, users tend to report high privacy concerns, but in contrast, they will then not let their privacy concerns affect or limit their behaviour online; the privacy paradox is a description of this dichotomy. The problem, however, is not only that location privacy seems to be a paradoxical issue; the sharing of location data provides users with new possibilities that can potentially have negative consequences for them, such as someone else being able to identify one’s identity, home location, habits or other sensitive information. Social media network users communicate that a part of this is due to the lack of control over which information they share, with whom and where.This study employs a qualitative method, using unstructured interviews in a pre-study and a self-completion questionnaire. The purpose of the study is to examine and gain a better understanding of how the privacy paradox can help to better explain users’ location data disclosure preferences in the context of social media networking, and to help social media network developers in order to reduce privacy-related issues in social media networking applications with geotagging capabilities. The findings indicate that the paradox indeed is evident in user’s stated geotagging behaviour, and that users are slightly more worried about their location privacy than their overall online privacy. The conclusions offer a couple of different explanations for the paradox, and we argue that the contradiction of the paradox can be seen as a constant trade-off between benefits and risks of geotagging. We also give some examples of such advantages and disadvantages.