Discovery of topological constraints on spatial object classes using a refined topological model

In a typical data collection process, a surveyed spatial object is annotated upon creation, and is classified based on its attributes. This annotation can also be guided by textual definitions of objects. However, interpretations of such definitions may differ among people, and thus result in subjective and inconsistent classification of objects. This problem becomes even more pronounced if the cultural and linguistic differences are considered. As a solution, this paper investigates the role of topology as the defining characteristic of a class of spatial objects. We propose a data mining approach based on frequent itemset mining to learn patterns in topological relations between objects of a given class and other spatial objects. In order to capture topological relations between more than two (linear) objects, this paper further proposes a refinement of the 9-intersection model for topological relations of line geometries. The discovered topological relations form topological constraints of an object class that can be used for spatial object classification. A case study has been carried out on bridges in the OpenStreetMap dataset for the state of Victoria, Australia. The results show that the proposed approach can successfully learn topological constraints for the class bridge, and that the proposed refined topological model for line geometries outperforms the 9-intersection model in this task

Developing a model and a language to identify and specify the integrity constraints in spatial datacubes

La qualité des données dans les cubes de données spatiales est importante étant donné que ces données sont utilisées comme base pour la prise de décision dans les grandes organisations. En effet, une mauvaise qualité de données dans ces cubes pourrait nous conduire à une mauvaise prise de décision. Les contraintes d'intégrité jouent un rôle clé pour améliorer la cohérence logique de toute base de données, l'un des principaux éléments de la qualité des données. Différents modèles de cubes de données spatiales ont été proposés ces dernières années mais aucun n'inclut explicitement les contraintes d'intégrité. En conséquence, les contraintes d'intégrité de cubes de données spatiales sont traitées de façon non-systématique, pragmatique, ce qui rend inefficace le processus de vérification de la cohérence des données dans les cubes de données spatiales. Cette thèse fournit un cadre théorique pour identifier les contraintes d'intégrité dans les cubes de données spatiales ainsi qu'un langage formel pour les spécifier. Pour ce faire, nous avons d'abord proposé un modèle formel pour les cubes de données spatiales qui en décrit les différentes composantes. En nous basant sur ce modèle, nous avons ensuite identifié et catégorisé les différents types de contraintes d'intégrité dans les cubes de données spatiales. En outre, puisque les cubes de données spatiales contiennent typiquement à la fois des données spatiales et temporelles, nous avons proposé une classification des contraintes d'intégrité des bases de données traitant de l'espace et du temps. Ensuite, nous avons présenté un langage formel pour spécifier les contraintes d'intégrité des cubes de données spatiales. Ce langage est basé sur un langage naturel contrôlé et hybride avec des pictogrammes. Plusieurs exemples de contraintes d'intégrité des cubes de données spatiales sont définis en utilisant ce langage. Les designers de cubes de données spatiales (analystes) peuvent utiliser le cadre proposé pour identifier les contraintes d'intégrité et les spécifier au stade de la conception des cubes de données spatiales. D'autre part, le langage formel proposé pour spécifier des contraintes d'intégrité est proche de la façon dont les utilisateurs finaux expriment leurs contraintes d'intégrité. Par conséquent, en utilisant ce langage, les utilisateurs finaux peuvent vérifier et valider les contraintes d'intégrité définies par l'analyste au stade de la conception

A qualitive reasoning approach for improving query results for sketch based queries by topological analysis of spatial aggregation

Dissertation submitted in partial fulfillment of the requirements for the Degree of Master of Science in Geospatial Technologies.Sketch-based spatial query systems provide an intuitive method of user interaction for spatial databases. These systems must be capable of interpreting user sketches in a way that matches the information that the user intended to provide. One challenge that must be overcome is that humans always simplify the environments they have experienced and this is reflected in the sketches they draw. One such simplification is manifested as aggregation or combination of spatial objects into conceptually or spatially related groups. In this thesis I develop a system that uses reasoning tools of the RCC-8 to evaluate sketchbased queries and provide a method for minimizing the effects of aggregation by determining whether a solution to a query can be expanded if some groups of regions are assumed to be parts of a larger aggregate region. If such a group of regions is found, then this group must be included in the solution. The solution is approximate because the approach taken only verifies that assumed parts of an aggregate are not inconsistent with the configuration of the whole solution. Only cases where the size of the solution equals the size of the query minus one are analysed. It is observed that correctly identifying aggregated regions leads to solutions that are more similar to the original query sketch when the size of every other solution is smaller than the size of the query or when a lower limit is placed on the acceptable size of a solution because the new, expanded or refined solution becomes more complete with respect to the sketch of the query

BeSpaceD: Towards a Tool Framework and Methodology for the Specification and Verification of Spatial Behavior of Distributed Software Component Systems

In this report, we present work towards a framework for modeling and checking behavior of spatially distributed component systems. Design goals of our framework are the ability to model spatial behavior in a component oriented, simple and intuitive way, the possibility to automatically analyse and verify systems and integration possibilities with other modeling and verification tools. We present examples and the verification steps necessary to prove properties such as range coverage or the absence of collisions between components and technical details

"Ontological Representation of Constraints for Geographical Reasoning"

We describe a framework that supports multiple types of constraint-based reasoning tasks on a geographic domain, by exploiting a semantic representation of the domain itself and of its constraints. Our approach is based on an abstract graph representation of a geographical area and of its relevant properties, for performing the reasoning tasks. As a test-bed, we consider the domain of Ecological Networks (ENs), which describe the structure of existing real ecosystems and help planning their expansion, conservation and improvement by introducing constraints on land use. While some previous work has been done about supporting the verification of compliance of fully specified ENs, we aim at taking a significant step further, by addressing the automatic suggestion of suitable aggregations of land patches into elements of the EN. This automated generation of EN elements is relevant to support the human planner in the design of public policies for land use because it leverages automated tools to carry out a possibly lengthy and error-prone task

Co-Clustering Network-Constrained Trajectory Data

Recently, clustering moving object trajectories kept gaining interest from both the data mining and machine learning communities. This problem, however, was studied mainly and extensively in the setting where moving objects can move freely on the euclidean space. In this paper, we study the problem of clustering trajectories of vehicles whose movement is restricted by the underlying road network. We model relations between these trajectories and road segments as a bipartite graph and we try to cluster its vertices. We demonstrate our approaches on synthetic data and show how it could be useful in inferring knowledge about the flow dynamics and the behavior of the drivers using the road network

On the heuristic power of mathematical representations

I argue that mathematical representations can have heuristic power since their construction can be ampliative. To this end, I examine how a representation (a) introduces elements and properties into the represented object that it does not contain at the beginning of its construction, and (b) how it guides the manipulations of the represented object in ways that restructure its components by gradually adding new pieces of information to produce a hypothesis in order to solve a problem. In addition, I defend an ‘inferential’ approach to the heuristic power of representations by arguing that these representations draw on ampliative inferences such as analogies and inductions. In effect, in order to construct a representation, we have to ‘assimilate’ diverse things, and this requires identifying similarities between them. These similarities form the basis for ampliative inferences that gradually build hypotheses to solve a problem. To support my thesis, I analyse two examples. The first one is intra-field (intramathematical), that is, the construction of an algebraic representation of 3-manifolds; the second is inter-fields, that is, the construction of a topological representation of DNA supercoiling

Continuous Modeling of 3D Building Rooftops From Airborne LIDAR and Imagery

In recent years, a number of mega-cities have provided 3D photorealistic virtual models to support the decisions making process for maintaining the cities' infrastructure and environment more effectively. 3D virtual city models are static snap-shots of the environment and represent the status quo at the time of their data acquisition. However, cities are dynamic system that continuously change over time. Accordingly, their virtual representation need to be regularly updated in a timely manner to allow for accurate analysis and simulated results that decisions are based upon. The concept of "continuous city modeling" is to progressively reconstruct city models by accommodating their changes recognized in spatio-temporal domain, while preserving unchanged structures. However, developing a universal intelligent machine enabling continuous modeling still remains a challenging task. Therefore, this thesis proposes a novel research framework for continuously reconstructing 3D building rooftops using multi-sensor data. For achieving this goal, we first proposes a 3D building rooftop modeling method using airborne LiDAR data. The main focus is on the implementation of an implicit regularization method which impose a data-driven building regularity to noisy boundaries of roof planes for reconstructing 3D building rooftop models. The implicit regularization process is implemented in the framework of Minimum Description Length (MDL) combined with Hypothesize and Test (HAT). Secondly, we propose a context-based geometric hashing method to align newly acquired image data with existing building models. The novelty is the use of context features to achieve robust and accurate matching results. Thirdly, the existing building models are refined by newly proposed sequential fusion method. The main advantage of the proposed method is its ability to progressively refine modeling errors frequently observed in LiDAR-driven building models. The refinement process is conducted in the framework of MDL combined with HAT. Markov Chain Monte Carlo (MDMC) coupled with Simulated Annealing (SA) is employed to perform a global optimization. The results demonstrates that the proposed continuous rooftop modeling methods show a promising aspects to support various critical decisions by not only reconstructing 3D rooftop models accurately, but also by updating the models using multi-sensor data