Paolo Fogliaroni

A qualitative approach to localization and navigation based on visibility information

In this paper we describe a model for navigation of an autonomous agent in which localization, path planning, and locomotion is performed in a qualitative manner instead of relying on exact coordinates. Our approach is grounded in a decomposition of navigable space based on a novel model of visibility and occlusion relations between extended objects for agents with very limited sensor abilities. A graph representation reflecting the adjacency between the regions of the decomposition is used as a topological map of the environment. The visibility-based representation can be constructed autonomously by the agent and navigation can be performed by simple reactive navigation behaviors. Moreover, the representation is well-qualified to be shared between multiple agents.

Modeling Visibility in 3D Space: A Qualitative Frame of Reference

This paper introduces and formalizes a frame of reference for projective relations in 3D space that can be used to model human visual perception. While in 2D space visibility information can be derived from the concept of collinearity (thus, as ternary relations), in 3D space it can be derived from coplanarity, which calls for quaternary relations. Yet, we can retain ternary relations by anchoring our frame to an ubiquitous reference element: a general sense of vertical direction that, on Earth, can be the expression of gravity force or, in other cases, of the asymmetries of an autonomous agent, either human or robotic, that is, its vertical axis. Based on these observations, the presented frame of reference can be used to model projective and visibility information as ternary relations. Granularity and complexity of the models can be adjusted: we present two differently detailed realizations and discuss possible applications in Geographic Information Systems.

Deriving the Geographic Footprint of Cognitive Regions

The characterization of place and its representation in current Geographic Information System (GIS) has become a prominent research topic. This paper concentrates on places that are cognitive regions, and presents a computational framework to derive the geographic footprint of these regions. The main idea is to use Natural Language Processing (NLP) tools to identify unique geographic features from User Generated Content (UGC) sources consisting of textual descriptions of places. These features are used to detect on a map an initial area that the descriptions refer to. A semantic representation of this area is extracted from a GIS and passed over to a Machine Learning (ML) algorithm that locates other areas according to semantic similarity. As a case study, we employ the proposed framework to derive the geographic footprint of the historic center of Vienna and validate the results by comparing the derived region against a historical map of the city.

A Semantic Region Growing Algorithm: Extraction of Urban Settings

Recent years have witnessed a growing production of Volunteer Geographic Information (VGI). This led to the general availability of semantically rich datasets, allowing for novel ways to understand, analyze or generalize urban areas. This paper presents an approach that exploits this semantic richness to extract urban settings, i.e., conceptually-uniform geographic areas with respect to certain activities. We argue that urban settings are a more accurate way of generalizing cities, since it more closely models human sense-making of urban spaces. To this end, we formalized and implemented a semantic region growing algorithm—a modification of a standard image segmentation procedure. To evaluate our approach, shopping areas of two European capital cities (Vienna and London) were extracted from an OpenStreetMap dataset. Finally, we explored the use of our approach to search for urban settings (e.g., shopping areas) in one city, that are similar to a setting in another.

A hybrid geometric-qualitative spatial reasoning system and its application in GIS

We propose a hybrid geometric-qualitative spatial reasoning system that is able to simultaneously deal with input information that is partially given geometrically and partially qualitatively using spatial relations of different qualitative spatial calculi. The reasoning system combines a geometric reasoning component based on computational geometry methods with a qualitative reasoning component employing relation algebraic reasoning techniques. An egg-yolk representation approach is used to maintain information about objects with underdetermined geometry and also allows for vague objects in the input. In an experimental evaluation we apply the reasoning system to infer geometric information for a set of only qualitatively described objects. The experiments demonstrate that the hybrid reasoning approach produces better results than geometric and qualitative reasoning individually.

A Computational Model for Context and Spatial Concepts

A natural language interface can improve human-computer interaction with Geographic Information Systems (GIS). A prerequisite for this is the mapping of natural language expressions onto spatial queries. Previous mapping approaches, using, for example, fuzzy sets, failed because of the flexible and context-dependent use of spatial terms. Context changes the interpretation drastically. For example, the spatial relation “near” can be mapped onto distances ranging anywhere from kilometers to centimeters. We present a context-enriched semiotic triangle that allows us to distinguish between multiple interpretations. As formalization we introduce the notation of contextualized concepts that is tied to one context. One concept inherits multiple contextualized concepts such that multiple interpretations can be distinguished. The interpretation for one contextualized concept corresponds to the intention of the spatial term, and is used as input for a spatial query. To demonstrate our computational model, a next generation GIS is envisioned that maps the spatial relation “near” to spatial queries differently according to the influencing context.

Qualitative Spatial Configuration Search

Current Geographic Information Systems (gis) lack, to a large extent, support for spatial searches in which a user wants to find a configuration of objects described in qualitative terms. At the database level, these searches can be supported by providing new spatial operators that can capture the semantics of the qualitative spatial relations used in the description and by developing resolution strategies tailored for queries including these operators. We suggest a technical approach to include an open number of qualitative spatial relations in gis and discuss a processing workflow leading from a qualitative spatial description to a database query and to the search result. We present and empirically evaluate a resolution strategy for these queries that is based on hypergraph matching and that exploits their semantic and structural characteristics. Finally, we envision some realizations of these searches and discuss challenges that relate to the cognitive aspect.

Domain model of an agricultural information system based on standards

IZVLEČEK Nikola Janković, Miro Govedarica, Gerhard Navratil, Paolo Fogliaroni | MODEL DOMENE KMETIJSKEGA INFORMACIJSKEGA SISTEMA, UTEMELJENEGA NA STANDARDIH | DOMAIN MODEL OF AN AGRICULTURAL INFORMATION SYSTEM BASED ON STANDARDS | 51-67 |

Data trustworthiness and user reputation as indicators of VGI quality

ABSTRACT Volunteered geographic information (VGI) has entered a phase where there are both a substantial amount of crowdsourced information available and a big interest in using it by organizations. But the issue of deciding the quality of VGI without resorting to a comparison with authoritative data remains an open challenge. This article first formulates the problem of quality assessment of VGI data. Then presents a model to measure trustworthiness of information and reputation of contributors by analyzing geometric, qualitative, and semantic aspects of edits over time. An implementation of the model is running on a small data-set for a preliminary empirical validation. The results indicate that the computed trustworthiness provides a valid approximation of VGI quality.

Matching geo-spatial information by qualitative spatial relations

Current Geographic Information Systems (GISs) do not adequately allow users to query spatial databases by means of qualitative terms like left, near, or above. Hence, we propose a matching framework that enables users to formulate configurations in a spatial query in an intuitive and qualitative manner. Spatial queries are translated into the formal query language Structured Query Language (SQL) which is used to query and retrieve results from spatial databases. In order to demonstrate the applicability of our approach we developed the Bremen Tourists Advisor with the matching framework as prominent component. Finally, we conduct experiments in the BTA context which exhibit the efficiency of our framework.