Towards Interactive Geodata Analysis through a Combination of Domain-Specific Languages and 3D Geo Applications in a Web Portal Environment

Urban planning processes affect a wide range of stakeholders including decision makers, urban planners, business companies as well as citizens. ICT-enabled tools supporting urban planning are considered key to successful and sustainable urban management. Based on previous work in the areas of web-based participation tools for urban planning, rule-based geospatial processing as well as 3D virtual reality applications we present a tool that supports experts from municipalities in planning and decision making but also provides a way for the public to engage in urban planning processes. The main contribution of this work is in the combination of 3D visualization and interaction components with a new ontology-driven rule editor based on domain-specific languages. The 3D visualization, on the one hand, enables stakeholders to present and discuss urban plans. On the other hand, the rule editor particularly targets expert users who need to perform spatial analyses on urban data or want to configure the 3D scene according to custom rules. Compared to previous approaches we propose a portable and interactive solution. Our tool is web-based and uses HTML5 technology making it accessible by a broad audience

Automatic integration of spatial data in viewing services

Geoportals are increasingly used for searching viewing and downloading spatial data. This study concerns methods to improve the visual presentation in viewing services. When spatial data in a viewing service are taken from more than one source there are often syntactic semantic topological and geometrical conflicts that prevent maps being fully consistent. In this study we extend a standard view service with methods to solve these conflicts. The methods are based on: (1) semantic labels of data in basic services (2) a rule-base in the portal layer and (3) integration methods in the portal layer. To evaluate the methodology we use a case study for adding historical borders on top of a base-map. The results show that the borders are overlaid on top of the map without conflicts and that a consistent map is generated automatically as an output. The methodology can be generalized to add other types of data on top of a base-map

Innovative approaches to urban data management using emerging technologies

Many characteristics of Smart cities rely on a sufficient quantity and quality of urban data. Local industry and developers can use this data for application development that improves life of all citizens. Therefore, the handling and usability of this data is a big challenge for smart cities. In this paper we investigate new approaches to urban data management using emerging technologies and give an insight on further research conducted within the EC-funded smarticipate project. Geospatial data cannot be handled well in classical relational database environments. Either they are just put in as binary large objects or have to be broken down into elementary types which can be handled by the database, in many cases resulting in a slow system, since the database technology is not really tuned for delivery on mass data as classical relational databases are optimized for online transaction processing and not analytic processing. Document-based databases provide a better performance, but still struggle with the challenge of large binary objects. Also the heterogeneity of data requires a lot of mapping and data cleansing, in some cases replication can’t be avoided. Another approach is to use Semantic Web technologies to enhance the data and build up relations and connections between entities. However, data formats such as RDF use a different approach and are not suitable for geospatial data leading to a lack on usability. Search engines are a good example of web applications with a high usability. The users must be able to find the right data and get information of related or close matches. This allows information retrieval in an easy to use fashion. The same principles should be applied to geospatial data, which would improve the usability of open data. Combined with data mining and big data technologies those principles would improve the usability of open geospatial data and even lead to new ways to use it. By helping with the interpretation of data in a certain context data is transformed into useful information. In this paper we analyse key features of open geodata portals such as linked data and machine learning in order to show ways of improving the user experience. Based on the Smarticipate projects we show afterwards as open data and geo data online and see the practical application. We also give an outlook on piloting cases where we want to evaluate, how the technologies presented in this paper can be combined to a usefull open data portal. In contrast to the previous EC-funded project urbanapi, where participative processes in smart cities where created with urban data, we go one step further with semantic web and open data. Thereby we achieve a more general approach on open data portals for spatial data and how to improve their usability. The envisioned architecture of the smarticipate project relies on file based storage and a no-copy strategy, which means that data is mostly kept in its original format, a conversion to another format is only done if necessary (e.g. the current format has limitations on domain specific attributes or the user requests a specific format). A strictly functional approach and architecture is envisioned which allows a massively parallel execution and therefore is predestined to be deployed in a cloud environment. The actual search interface uses a domain specific vocabulary which can be customised for special purposes or for users that consider their context and expertise, which should abstract from technology specific peculiarities. Also application programmers will benefit form this architecture as linked data principles will be followed extensively. For example, the JSON and JSON-LD standards will be used, so that web developers can use results of the data store directly without the need for conversion. Also links to further information will be provided within the data, so that a drill down is possible for more details. The remainder of this paper is structured as follows. After the introduction about open data and data in general we look at related work and existing open data portals. This leads to the main chapter about the key technology aspects for an easy-to-use open data portal. This is followed by Chapter five, an introduction of the EC-funded project smarticipate, in which the key technology aspects of chapter four will be included

Development of an interface for ontology‐based transformation between features of different types

Dissertation submitted in partial fulfilment of the requirements for the Degree of Master of Science in Geospatial TechnologiesImplementation of the INSPIRE directive, the spatial data infrastructure for the Europe, has created a necessity for easy and convenient conversion between different models of geospatial data. Data model transformation across heterogeneous systems can be hampered by differences in terminology and conceptualization, particularly when multiple communities are involved. Requirement in current situation is an interface facilitating transformation of data to a desired format and immediate use of the data, which are collected from different formats and models. Ontology-aware software with shared understanding of concepts, enable users to interact with geospatial data models. Thus use of ontologies could make a friendly environment to the user in translating the data conveniently. Feature type ontologies, along with annotations are provided from an ongoing project at the Institute for Geoinformatics (IfGI, University of Münster, Germany), in order to reconcile differences in semantics. FME workbench provides a successful environment to execute set of rules for the data model transformation using a mapping file, which can be developed externally. The thesis work developed a user interface that includes operations to define rules for the translation of geospatial data, from one model to another. Annotated feature types are taken as input, and the results are encoded as FME Mapping files. The overall methodology involves three phases.(...

Anotação semântica de dados geoespaciais para a agricultura.

Dados geoespaciais são base para sistemas de decisão em vários domínios. Para serem usados esses dados precisam ser analisados e interpretados por especialistas. Essas interpretações geralmente não são armazenadas ou correspondem apenas a alguma informação textual e em linguagem própria, gravadas em arquivos técnicos. A ausência de soluções eficientes para armazená-las leva a problemas como retrabalho e dificuldades de compartilhamento de informação. Este trabalho apresenta uma solução para esse problema que baseia-se no uso de anotações semânticas, uma abordagem que promove um entendimento comum dos conceitos usados. Com a adoção de workflows científicos e também de um esquema de metadados e de ontologias bem conhecidos, foi especificado e parcialmente implementado um framework para anotação semântica de dados geoespaciais, focando na solução de problemas em agricultura.bitstream/item/32414/1/BolPesq25.pd

Process Modelling, Web Services and Geoprocessing

Process modelling has always been an important part of research in generalisation. In the early days this would take the form of a static sequence of generalisation actions, but currently the focus is on modelling much more complex processes, capable of generalising geographic data into various maps according to specific user requirements. To channel the growing complexity of the processes required, better process models had to be developed. This chapter discusses several aspects of the problem of building such systems. As the system gets more complex, it becomes important to be able to reuse components which already exist. Web services have been used to encapsulate generalisation processes in a way that maximises their interoperability and therefore reusability. However, for a system to discover and trigger such a service, it needs to be formalised and described in a machine understandable way, and the system needs to have the knowledge about where and when to use such tools. This chapter therefore explores the requirements and potential approaches to the design and building of such systems

Learning Aerial Image Segmentation from Online Maps

This study deals with semantic segmentation of high-resolution (aerial) images where a semantic class label is assigned to each pixel via supervised classification as a basis for automatic map generation. Recently, deep convolutional neural networks (CNNs) have shown impressive performance and have quickly become the de-facto standard for semantic segmentation, with the added benefit that task-specific feature design is no longer necessary. However, a major downside of deep learning methods is that they are extremely data-hungry, thus aggravating the perennial bottleneck of supervised classification, to obtain enough annotated training data. On the other hand, it has been observed that they are rather robust against noise in the training labels. This opens up the intriguing possibility to avoid annotating huge amounts of training data, and instead train the classifier from existing legacy data or crowd-sourced maps which can exhibit high levels of noise. The question addressed in this paper is: can training with large-scale, publicly available labels replace a substantial part of the manual labeling effort and still achieve sufficient performance? Such data will inevitably contain a significant portion of errors, but in return virtually unlimited quantities of it are available in larger parts of the world. We adapt a state-of-the-art CNN architecture for semantic segmentation of buildings and roads in aerial images, and compare its performance when using different training data sets, ranging from manually labeled, pixel-accurate ground truth of the same city to automatic training data derived from OpenStreetMap data from distant locations. We report our results that indicate that satisfying performance can be obtained with significantly less manual annotation effort, by exploiting noisy large-scale training data.Comment: Published in IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSIN

Methods to Improve and Evaluate Spatial Data Infrastructures

This thesis mainly focuses on methods for improving and evaluating Spatial Data Infrastructures (SDIs). The aim has been threefold: to develop a framework for the management and evaluation of an SDI, to improve the accessibility of spatial data in an SDI, and to improve the cartography in view services in an SDI. Spatial Data Infrastructure has been identified as an umbrella covering spatial data handling procedures. The long-term implementation of SDI increases the need for short/middle term feedbacks from different perspectives. Thus, a precise strategic plan and accurate objectives have to be defined for the implementation of an efficient environment for spatial data collection and exchange in a region. In this thesis, a comprehensive study was conducted to review the current methods in the business management literature to approach to an integrated framework for the implementation and evaluation of SDIs. In this context, four techniques were described and the usability of each technique in several aspects of SDI implementation was discussed. SDI evaluation has been considered as one of the main challenges in recent years. Lack of a general goal oriented framework to assess an SDI from different perspectives was one of the main concerns of this thesis. Among a number of the current methods in this research area, we focused on the Balanced Scorecard (BSC) as a general evaluation framework covering all perspectives in an SDI. The assessment study opened a window to a number of important issues that ranged from the technical to the cartographic aspects of spatial data exchange in an SDI. To access the required datasets in an SDI, clearinghouse networks have been developed as a gateway to the data repositories. However, traditional clearinghouse networks do not satisfy the end user requirements. By adding a number of functionalities, we proposed a methodology to increase the percentage of accessing required data. These methods were based on predefined rules and additional procedures within web processing services and service composition subjects to develop an expert system based clearinghouses. From the cartography viewpoint, current methods for spatial data presentation do not satisfy the user requirements in an SDI environment. The main presentation problem occurs when spatial data are integrated from different sources. For appropriate cartography, we propose a number of methods, such as the polygon overlay method, which is an icon placement approach, to emphasize the more important layers and the color saturation method to decrease the color saturation of the unimportant layers and emphasize the foreground layer according to the visual hierarchy concept. Another cartographic challenge is the geometrical and topological conflicts in data shown in view services. The geometrical inconsistency is due to the artificial discrepancy that occurs when displaying connected information from different sources, which is caused by inaccuracies and different levels of details in the datasets. The semantic conflict is related to the definition of the related features, i.e., to the information models of the datasets. To overcome these conflicts and to fix the topological and geometric conflicts we use a semantic based expert system by utilizing an automatic cartography core containing a semantic rule based component. We proposed a system architecture that has an OWL (Web Ontology Language) based expert system to improve the cartography by adjusting and resolving topological and geometrical conflicts in geoportals