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A pilot regional Spatial Data Infrastructure (SDI) has recently been developed for the Heraklion Prefecture in Crete, Greece, using Geographic Free and Open Source Software (GeoFOSS). This SDI is compatible with the geospatial standards and specifications introduced by the Open Geospatial Consortium (OGC) and it distributes the geospatial content on the web through widely accepted services (e.g., WMS, WFS, WCS and CSW). This paper presents the architecture, the components and the functionality of the Heraklion SDI. Specifically, it focuses on the map server, along with the services that provide accessibility to the data repositories, the spatial database server, the metadata and data catalog, the visualization tools and the web-client interface.

Ponencias, comunicaciones y pósters presentados en el 17th AGILE Conference on Geographic Information Science "Connecting a Digital Europe through Location and Place", celebrado en la Universitat Jaume I del 3 al 6 de junio de 2014. This study addresses the issue of urban sprawl through the application of a Cellular Automata (CA) based model in the area of Thessaloniki, Greece. To link macro-scale to micro-dynamic processes the model integrates a statistical model at the regional level with a CA model at the local level. The model is used to compare two scenarios of growth of Thessaloniki to year 2030; the first one assuming a continuation of existing trends, whereas the second one assuming the enactment of various land use regulations in order to contain urban sprawl. The comparison of the results demonstrate that in the second scenario there is a smaller degree of leapfrog growth, with high percentage of new developed land being inside the existing city plans with development in areas outside the plans and in agricultural areas being minimized.

Recently, through the GMES program of ESA the Urban Atlas dataset was released. The Urban Atlas is providing pan-European comparable land use and land cover data for Large Urban Zones with more than 100.000 inhabitants as defined by the Urban Audit. The production of the various datasets started in 2009 and is expected to be completed by the end of 2011. At presently datasets for more than 150 urban areas have been released. Most importantly the datasets can be freely downloaded and distributed. The availability of such a huge dataset produced with the same standards will have a major impact on the development of urban transportation models and the comparative analysis of the urban areas across Europe. Combined with the data sets that will be developed from the various Census of population it could become the basis for the application of various models in the next ten years. In this paper two major themes are discussed. First, how the current state of art in urban modeling (behavioral, cellular automata and statistical) can use these models, what type of additional data might be needed and how these datasets can be combined with other data for developing land use transportation models. Second, spatial metric techniques are used to define indicators for the landscape that could be used for comparing the structure and the form of the various cities. In the last ten years there has been an increasing interest in applying spatial metric techniques analysis of urban environments, to examine unique spatial components of intra-and inter-city urban structure, as well as, the dynamics of change. The landscape perspective assumes abrupt transitions between individual patches that result in distinct edges. These measures provide a link between the detailed spatial structures that result from urban change processes. The spatial metric indicators were developed for several cities and are then used for a comparative study of city typologies and urban fabric characteristics.

International audience; In this paper, we introduce the idea of documenting operational chains for land degradation assessment using ontologies. We believe that this will help end-users in better understanding the land degradation characteristics and evaluate the results of the assessment process. Since the application domain is wide, various operational chains for land degradation assessment and their associated documentation exist, according to different options. This parameterization causes the development of different ontologies, which, nonetheless are to a certain extent linked because of the common software components of the corresponding operational chains. We therefore propose a hierarchical structure of these ontologies; so that several requirements such as understanding of expert knowledge interconnections and application domain variety, documentation, assimilation of new expert knowledge, and reusability of software components become feasible.

A key concern in transportation planning and traffic management is the ability to forecast traffic flows on a street network. Traffic flows forecasts can be transformed to obtain travel time estimates and then use these as input to travel demand models, dynamic route guidance and congestion management procedures. A variety of mathematical techniques have been proposed for modeling traffic flow on a street network. Briefly, the most widely used theories are: -Kinetic models based on partial differential equations that describe waves of different traffic densities, -deterministic models that use nonlinear equations for the estimation of different car routes, -large scale simulation models such as cellular automata and, -stochastic modeling of traffic density at distinct points in space. One problem with these approaches is that the traffic flow process is characterized by nonstationarities that cannot be taken into account by the vast majority of modeling strategies. However, recent advances in statistical modeling in fields such as econometrics or environmetrics enable us to overcome this problem. The aim of this work is to present how two statistical techniques, namely, vector autoregressive modeling and dynamic space-time modeling can be used to develop efficient and reliable forecasts of traffic flow. The former approach is encountered in the econometrics literature, whereas the later is mostly used in environmetrics. Recent advances in statistical methodology provide powerful tools for traffic flow description and forecasting. For a purely statistical approach to travel time prediction one may consult Rice and van Zwet (2002). In this work, the authors employ a time varying coefficients regression technique that can be easily implemented computationally, but is sensitive to nonstationarities and does not take into account traffic flow information from neighboring points in the network that can significantly improve forecasts. According to our approach, traffic flow measurements, that is count of vehicles and road occupancy obtained at constants time intervals through loop detectors located at various distinct points of a road network, form a multiple time series set. This set can be described by a vector autoregressive process that models each series as a linear combination of past observations of some (optimally selected) components of the vector; in our case the vector is comprised by the different measurement points of traffic flow. For a thorough technical discussion on vector autoregressive processes we refer to Lutkerpohl (1987), whereas a number of applications can be found in Ooms (1994). Nowadays, these models are easily implemented in commercial software like SAS or MATLAB; see for example LeSage (1999). The spatial distribution of the measurement locations and their neighboring relations cannot be incorporated in a vector autoregressive model. However, accounting for this information may optimize model fitting and provide insight into spatial correlation structures that evolve through time. This can be accomplished by applying space-time modeling techniques. The main difference of space-time models encountered in literature with the vector autoregressive ones lies in the inclusion of a weight matrix that defines the neighboring relations and places the appropriate restrictions. For some early references on space-time models, one could consult Pfeifer and Deutsch (1980 a,b); for a Bayesian approach, insensitive to nonstationarities we refer to Wikle, Berliner and Cressie (1998). In this work, we discuss how the space-time methodology can be implemented to traffic flow modeling. The aforementioned modeling strategies are applied in a subset of traffic flow measurements collected every 15 minutes through loop detectors at 74 locations in the city of Athens. A comparative study in terms of model fitting and forecasting accuracy is performed. Univariate time series models are also fitted in each measurement location in order to investigate the relation between a model's dimension and performance. References: LeSage J. P. (1999). Applied Econometrics using MATLAB. Manuscript, Dept. of Economics, University of Toronto Lutkerpohl H. (1987). Forecasting Aggregated Vector ARMA Processes. Lecture Notes in Economics and Mathematical Systems. Springer Verlag Berlin Heidelberg Ooms M. (1994). Empirical Vector Autoregressive Modeling. Springer Verlag Berlin Heidelberg Pfeifer P. E., and Deutsch S. J. (1980a). A three-stage iterative procedure for Space-Time Modeling. Technometrics, 22, 35-47 Pfeifer P. E., and Deutsch S. J. (1980b). Identification and Interpretation of First-Order Space-Time ARMA models. Technometrics, 22, 397-408 Rice J., and van Zwet E. (2002). A simple and effective method for predicting travel times on freeways. Manuscript, Dept. of Statistics, University of California at Berkeley Wikle C. K., Berliner L. M. and Cressie N. (1998). Hierarchical Bayesian space-time models. Environmental and Ecological Statistics, 5, 117-154

Περίληψη: Οι κλασικές αλλά και σύγχρονες μέθοδοι τοπογραφικής αποτύπωσης και γεωμετρικής τεκμηρίωσης π.χ. μέσω σαρώσεων με LiDAR, ή με φωτογραμμετρικές μεθόδους παράγουν μεγάλο όγκο δεδομένων, συνήθως με τη μορφή τρισδιάστατων νεφών σημείων (3D point clouds). Τα υπάρχοντα υπολογιστικά συστήματα και λογισμικά επεξεργασίας χωρικών δεδομένων αδυνατούν συχνά να τα διαχειριστούν λόγω του όγκου τους ή δεν παρέχουν την επιθυμητή λειτουργικότητα για την περαιτέρω ανάλυσή τους. Υπάρχει λοιπόν ανάγκη για λογισμικά και εργαλεία επεξεργασίας τα οποία θα καλύψουν αυτό το κενό. Στόχος της παρούσας διπλωματικής εργασίας είναι η ανάπτυξη μίας διαδικτυακής εφαρμογής, στα πρότυπα χρήσης Ελεύθερου Λογισμικού & Λογισμικού Ανοικτού Κώδικα (ΕΛΛΑΚ), η οποία θα λειτουργεί ως αποθετήριο δεδομένων μορφής νέφους σημείων, παρέχοντας παράλληλα δυνατότητες τόσο επεξεργασίας όσο και οπτικοποίησής τους. Η εφαρμογή θα συνδέεται με μία βάση δεδομένων για την αποθήκευση και ανάκτηση των απαραίτητων για τη λειτουργία της πληροφοριών. Το περιβάλλον της θα πρέπει να είναι φιλικό προς τον χρήστη, απλοποιώντας τις διαδικασίες χειρισμού και επεξεργασίας των δεδομένων. Έτσι, τα βασικά χαρακτηριστικά της εφαρμογής που υλοποιήθηκε περιλαμβάνουν ένα εύχρηστο γραφικό περιβάλλον που επιτρέπει τη διαχείριση χρηστών, τη δυνατότητα μεταφόρτωσης των δεδομένων, την κατάλληλη οπτικοποίηση καθώς και σειρά εργαλείων επεξεργασίας νεφών σημείων. Η δημιουργία της εφαρμογής βασίστηκε στη χρήση του πλαισίου ανάπτυξης διαδικτυακών εφαρμογών Django και της βάσης δεδομένων PostgreSQL με τις γεωχωρικές της επεκτάσεις PostGIS και PGPointCloud. Ως γλώσσα προγραμματισμού για τη σύνδεση των επιλεγμένων λογισμικών χρησιμοποιήθηκε η Python, που είναι και η γλώσσα προγραμματισμού του Django. Ο χειρισμός γεωχωρικών δεδομένων και δεδομένων νεφών σημείων έγινε χρησιμοποιώντας κατάλληλες βιβλιοθήκες, οι κυριότερες εκ των οποίων είναι οι Leaflet, GDAL και PDAL. Η οπτικοποίηση τρισδιάστατων δεδομένων σε περιηγητές ιστού έγινε με χρήση του λογισμικού Potree, που βασίζεται στην τεχνολογία WebGL. Η υλοποίηση της επιθυμητής πρόσθετης λειτουργικότητας του Potree επιτεύχθηκε με παραμετροποίησή του μέσω των βιβλιοθηκών PDAL & GDAL, εντός του πλαισίου ανάπτυξης Django. Επίσης, έγινε προσπάθεια για δημοσίευση της εφαρμογής στο Διαδίκτυο με τον Apache web server. Τέλος, η εφαρμογή που αναπτύχθηκε (PC Repository) δίνει τη δυνατότητα σε χρήστες με περιορισμένους υπολογιστικούς πόρους να αποθηκεύουν, οπτικοποιούν, διαχειρίζονται και επεξεργάζονται δεδομένα νεφών σημείων χρησιμοποιώντας διαδεδομένους περιηγητές ιστού, χωρίς να χρειάζεται να διαθέτουν εξειδικευμένο λογισμικό. Summarization: Classical as well as contemporary methods of topographic surveying and geometric documentation such as LiDAR scans or photogrammetry can produce large volumes of data, usually in the form of 3D point clouds. Existing computing systems and spatial data processing software often fail to manage them due to their size, or do not offer the desired functionality for further analysis. Therefore, there is a need for software and processing tools that will bridge this gap. The aim of this master thesis is the development, through Free & Open Source Software (FOSS), of a web application that will operate as a repository of point cloud data, offering at the same time features such as processing and visualizing of such data. The application will connect to a database for storing and retrieving information necessary for its operation. It should engage a user-friendly environment, simplifying the procedures of data handling and processing. Thus, the basic features of the developed application include an easy to use graphical interface that allows user management, uploading, visualizing as well as some processing capabilities of point cloud data. The development of the application was based on the use of Django web framework and PostgreSQL database with PostGIS and PGPointCloud spatial extensions. Python was used as a programming language for connecting the selected software components, which is also the programming language of Django. Spatial data and point cloud data were managed using appropriate libraries, mainly Leaflet, GDAL and PDAL. The visualization of 3D data in web browsers was accomplished using Potree software, which is based on WebGL technology. The implementation of the desired additional functionality of Potree was achieved by customizing it through libraries PDAL & GDAL, within the Django framework. Publishing the application to the Internet was attempted using the Apache web server. Finally, the developed application (PC Repository) allows users with limited computing resources to store, visualize, manage and process point cloud data using standard web browsers, without the need for specialized software.