Gloria Bordogna

Modelling Landslides’ Susceptibility by Fuzzy Emerging Patterns

This contribution proposes an approach to model regional landslide susceptibility, based on a supervised learning technique that mines fuzzy emerging patterns on a set of classified data. In our approach the training set contains positive and negative examples of areas, (i.e., slope units), affected or not affected by landslides. The fuzzy emerging patterns characterise the positive and the negative areas exploiting their ability to discriminate between the two classes. The approach consists first, in inducing a set of fuzzy rules, and then in reducing them by retaining those that identify fuzzy emerging patterns for the given training set. The fuzzy rules define the main characteristics of the slope units that are affected or not affected by landslides and are used to classify other slope units in the same region. The classification technique provides an estimate of the hesitation of the decision process, which is a measure of its ability to uniquely associate a slope unit to the susceptible or not susceptible class. In the paper we describe the approach and discuss the preliminary results.

VGI Imperfection in Citizen Science Projects and Its Representation and Retrieval Based on Fuzzy Ontologies and Level-Based Approximate Reasoning

The chapter investigates the kinds of imperfection affecting Volunteer Geographic Information (VGI) created by users eager to participate in some citizen science project. An approach based on the use of fuzzy domain ontologies and level-based approximate reasoning is suggested to represent and manage both the uncertainty of volunteers when describing their observations and the vagueness of ill-defined domain knowledge. This way one can model more reliable smart applications for creating VGI as well as can design less ambiguous spatial data infrastructures (SDIs) for sharing VGI with final stakeholders.

Mobile Information Systems Leveraging Volunteered Geographic Information for Earth Observation

Since OpenStreetMap (OSM) appeared more than ten years ago, new collaborative mapping approaches have emerged in different areas and have become important components of localised information and services based on localisation. There is now increased awareness of the importance of the space-time attributes of almost every event and phenomenon. Citizens now have endless possibilities to quickly geographically locate themselves with an accuracy previously thought impossible. Based on these societal drivers, we proposed a number of collaborative mapping experiments (“mapping parties”) to delegates of a large open-source geospatial conference and to citizens of the conference’s host city during July 2015. M. A. Brovelli (&) L. Biagi M. Minghini Department of Civil and Environmental Engineering, Politecnico di Milano, Piazza Leonardo da Vinci 32, 20133 Milan, Italy e-mail: maria.brovelli@polimi.it L. Biagi e-mail: ludovico.biagi@polimi.it M. Minghini e-mail: marco.minghini@polimi.it P. Mooney Department of Computer Science, Maynooth University, Eolas Building, Maynooth, Co. Kildare, Ireland e-mail: Peter.Mooney@nuim.ie M. Brambilla Dipartimento di Elettronica, Informazione e Bioingegneria (DEIB), Politecnico di Milano, via Ponzio 34/5, 20133 Milan, Italy e-mail: marco.brambilla@polimi.it I. Celino CEFRIEL, via Fucini 2, Milan, Italy e-mail: irene.celino@cefriel.com E. Ciceri Dipartimento di Elettronica, Informazione e Bioingegneria (DEIB), Politecnico di Milano, Como Campus, via Ponzio 34/5, Milan, Italy e-mail: eleonora.ciceri@polimi.it

A Geo-Spatial Data Infrastructure for Flexible Discovery, Retrieval and Fusion of Scenario Maps in Preparedness of Emergency

In order to effectively plan both preparedness and response to emergency situations it is necessary to access and analyse timely information on plausible scenarios of occurrence of ongoing events. Scenario maps representing the estimated susceptibility, hazard or risk of occurrence of an event on a territory are hardly generated real time. In fact the application of physical or statistical models using environmental parameters representing current dynamic conditions is time consuming on low cost hardware equipment. To cope with this practical issue we propose an off line generation of scenario maps under diversified environmental dynamic parameters, and a geo-Spatial Data Infrastructure (SDI) to allow people in charge of emergency preparedness and response activities to flexibly discover, retrieve, fuse and visualize the most plausible scenarios that may happen given some ongoing or forecasted dynamic conditions influencing the event. The novelty described in this chapter is related with both the ability to interpret flexible queries in order to retrieve risk scenario maps that are related to the current situation and to show the most plausible worst and best scenarios that may occur in each elementary area of the territory. Although, the SDI proposal has been conceived and designed to support the management of distinct natural and man-made risks, in the proof of concept prototypal implementation the scenarios maps target wild fire events.