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This exploratory study aimed to evaluate the preferences, expectations, and sense of safety of blind or visually impaired persons regarding three types of pedestrian phasing with audible pedestrian signals configurations that exist in Quebec City (Canada). These include: 1) exclusive phasing with non-directional audible pedestrian signals; 2) exclusive phasing with directional audible pedestrian signals; and 3) concurrent phasing with directional audible pedestrian signals. Thirty-two blind or visually impaired persons were asked to complete a survey. Their preferences and expectations regarding audible pedestrian signals were documented through a series of simulations. Their sense of safety regarding the three existing configurations were also documented. Subsequently, semi-directed, individual interviews with 11 of the individuals who had completed the survey were conducted to build off the collected information. No formal consensus regarding many of the issues discussed were established as participants’ responses varied too significantly. However, findings suggest that the exclusive phasing with directional audible pedestrian signals configuration is perceived to be the safest option by the participants. This study may have practical implications on the design of intersections (e.g., selection of a type of pedestrian phasing with audible pedestrian signal) and the training of blind or visually impaired pedestrians.IMPLICATIONS FOR REHABILITATIONThe addition of audible pedestrian signals to pedestrian signals heightens the sense of safety of blind or visually impaired persons.This study may have practical implications on the design of intersections with audible pedestrian signals and the selection of a type of audible pedestrian signals based on intersection characteristics.Since many participants reported a lower sense of safety when faced with concurrent phasing, it is recommended that more intensive orientation and mobility interventions be provided to blind or visually impaired pedestrians who use this type of traffic signals.Educating road users about blind or visually impaired pedestrians appears also essential. The addition of audible pedestrian signals to pedestrian signals heightens the sense of safety of blind or visually impaired persons. This study may have practical implications on the design of intersections with audible pedestrian signals and the selection of a type of audible pedestrian signals based on intersection characteristics. Since many participants reported a lower sense of safety when faced with concurrent phasing, it is recommended that more intensive orientation and mobility interventions be provided to blind or visually impaired pedestrians who use this type of traffic signals. Educating road users about blind or visually impaired pedestrians appears also essential.

Background Canada’s national parks are world-renowned. However, despite recent attempts to improve access, many are not accessible to people with disabilities. With the advent of provincial and federal legislation, standards are being developed to assist with the design and management of parks. Objective The overarching objective of this study is to inform accessibility standards for federal parks that meet the needs of all park visitors, regardless of ability. The specific objectives of this study are to identify park accessibility standards that exist internationally, identify the accessibility challenges that people with disabilities face in park environments, and prioritize and recommend accessibility standards for national parks. Methods A 3-phase approach will be used to achieve the study objectives. In the first phase, a scoping review of the existing accessibility standards will be conducted. The second phase will include objective audits of trails and features in 6 parks, 3 in western Canada and 3 in eastern Canada, as well as mobile interviews with 24 diverse participants in each region regarding their experiences of and recommendations for improving the park’s accessibility. In the final phase, a Delphi participatory consensus development process will be used, based on the data gathered in the first 2 phases, to prioritize recommendations for standards. Results We expect to find gaps in existing standards that do not account for the diverse range of accessibility requirements that people with disabilities have for visiting parks. We also expect to find that existing standards, on their own, may not be enough to ensure equitable access to all the experiences and amenities that parks have to offer. Development of subsequent guidelines and best practices may be necessary to address complex scenarios for which standards may not be the best approach to ensuring accessibility. Conclusions The participatory and mixed methods approaches used in this study will provide rich insights for developing accessible park standards that consider the diverse needs of people with disabilities. The findings will also support the development or enhancement of park standards at all levels of government. International Registered Report Identifier (IRRID) DERR1-10.2196/33611

Biomethanation projects across the world struggle with multiple challenges related to the location selection and optimization of the treatment facilities. Important aspects such as treatment plant location and treatment process chain configuration depend on the waste sources to be treated, the required end-product type and quality, as well as its final use destination, all of which are variable in time and space. This research describes the development and use of an integrated decision-support software tool that allows setting up optimal organic waste value chains, named optim-O. Key features of the tool include a multidimensional spatiotemporal database, a model-based decision module for simulation and optimization, as well as a user-friendly interface. The availability of such a software tool will not only allow to save time and money on data collection and calculations, but will also induce more comprehensive decisions by simultaneously taking into account a variety of factors, thereby significantly facilitating and enhancing the decision-making process.

Automatic semantic segmentation of point clouds observed in a 3D complex urban scene is a challenging issue. Semantic segmentation of urban scenes based on machine learning algorithm requires appropriate features to distinguish objects from mobile terrestrial and airborne LiDAR point clouds in point level. In this paper, we propose a pointwise semantic segmentation method based on our proposed features derived from Difference of Normal and the features “directional height above” that compare height difference between a given point and neighbors in eight directions in addition to the features based on normal estimation. Random forest classifier is chosen to classify points in mobile terrestrial and airborne LiDAR point clouds. The results obtained from our experiments show that the proposed features are effective for semantic segmentation of mobile terrestrial and airborne LiDAR point clouds, especially for vegetation, building and ground classes in an airborne LiDAR point clouds in urban areas.

Modelling and simulation of spatial processes is increasingly used for a wide variety of applications including water resources protection and management, meteorological prevision and forest fire monitoring. As an example, an accurate spatial modelling of a hydrological system can assist hydrologists to answer questions such as: "where does ground water come from?", “how does it travel through a complex geological system?" and “how is water pollution behaviour in an aquifer?” It also allows users and decision-makers to better understand, analyze and predict the groundwater behaviour. In this chapter, we briefly present an overview of spatial modelling and simulating of a dynamic continuous process such as a fluid flow. Most of the research in this area is based on the numerical modelling and approximation of the dynamic behaviour of a fluid flow. Dynamic continuous process is typically described by a set of partial differential equations (PDE) and their numerical solution is carried out using a spatial tessellation that covers the domain of interest. An efficient solution of the PDE requires methods that are adaptive in both space and time. The existing numerical methods are applied in either a static manner from the Eulerian point of view, where the equations are solved using a fixed tessellation during a simulation process, or in a dynamic manner from the Lagrangian point of view, where the tessellation moves. Some methods are also based on the mixed EulerianLagrangian point of view. However, our literature review reveals that, unfortunately, these methods are unable to efficiently handle the spatial-dynamic behaviour of phenomenon. Therefore, in this chapter, we investigate spatial tessellation based on Voronoi diagram (VD) and its dual Delaunay tessellation (DT) which is good candidate to deal with dynamics behaviour of fluid flow. Voronoi diagram is a topological data structure that discretizes the dynamic phenomenon to a tessellation adaptive in space and time.

Background: Social participation is beneficial for individuals’ health. However, people with disabilities that may lead to mobility limitations tend to experience lower levels of social participation. Information and communication technologies such as the OnRoule mobile application (app) can help promote social participation. Objectives: To obtain potential users’ perceptions on the usability and content of the OnRoule app for providing information on accessibility, as well as its potential to optimize social participation. Materials and Methods: Cross-sectional user-centered design study. Individuals with physical disabilities (n = 18) were recruited through community organizations and interviewed using a semi-structured guide. Interviews were recorded, transcribed, and analyzed using thematic analysis. Results: Three main themes were identified: (1) “user-friendliness”, (2) “balance between the amount and relevance of information”, and (3) “potential use of the app”. Discussion and Conclusion: Findings from this study indicated that the app was easy to use, had pertinent information, and enabled a positive experience of finding information. However, several areas of improvement were identified, such as the clarity of specific elements, organization and amount of information, optimization of features, and inclusiveness. Apps such as OnRoule could optimize social participation by facilitating the process of finding resources in the community and building a sense of connectedness between users.

Social dimension of environment is an important aspect that should be reflected in research works related to studying the interactions between human and the environment. However, this dimension is usually neglected when representing the environment in geographic information systems for different applications. For instance, disability as a result of the interaction between human and environment is influenced by social and physical dimensions of environment. Although, this aspect is highlighted in most conceptual disability models by defining various taxonomies of the environment, from ontological perspective justifying and connecting social dimension to the physical dimension of the environment is not clearly determined. Integrating social dimension of the environment with its physical dimension for disability studies is a challenging task, which is the main objective of the present study. Here, we review some of the disability models and their perspective about classifying the environment. Then, from ontological perspective, their limitations are discussed and a new approach for the classification of concepts form the environment is presented. This approach facilitates and simplifies integration of social dimension in ontologies for more effective assessment of disability issue in Geographic Information System.

Adequate coverage is an important issue in geosensor networks in order to fulfill the sensing applications in urban areas. GIS as well as Optimization methods are widely used to distribute geosensors in the network to achieve the desired level of coverage. Most of the algorithms applied in urban domain suffer from the lack of considering real environmental information. In this paper, the problem of placing sensors to get optimum coverage is studied by investigating the concept of urban contextual information in sensor network. Then, a local GIS-based context-aware framework of sensor network deployment optimization method is introduced. Obtained results of our algorithm under different working conditions and applications show the effectiveness of our approach.

The United Nations Convention on the Rights of People with Disabilities recognizes the right of people with disabilities to attain full social participation without discrimination on the basis of disability. Furthermore, mobility is one of the most important life habits for achieving such participation. Providing people with disabilities with information regarding accessible paths and accessible urban places therefore plays a vital role in achieving these goals. The accessibility of urban places and pedestrian networks depends, however, on the interaction between human capabilities and environmental factors, and may be subdivided into physical or social factors. An optimal analysis of accessibility requires both kinds of factors, social as well as physical. Although there has been considerable work concerning the physical aspects of the environment, social aspects have been largely neglected. In this paper, we highlight the importance of the social dimension of environments and consider a more integrated approach for accessibility assessment. We highlight the ways by which social factors such as policies can be incorporated into accessibility assessment of pedestrian networks for people with motor disabilities. Furthermore, we propose a framework to assess the accessibility of pedestrian network segments that incorporates the confidence level of people with motor disabilities. This framework is then used as a tool to investigate the influence of different policies on accessibility conditions of pedestrian networks. The methodology is implemented in the Saint-Roch neighborhood in Quebec City and the effectiveness of three policy actions is examined by way of illustration.

Recent advances in geospatial technologies have made available large amount of geospatial data. Meanwhile, new developments in Internet and communication technologies created a shift from isolated geospatial databases to ad hoc networks of geospatial data sources, where data sources can join or leave the network, and form groups to share data and services. However, effective integration and sharing of geospatial data among these data sources and their users are hampered by semantic heterogeneities. These heterogeneities affect the spatial, temporal and thematic aspects of geospatial concepts. There have been many efforts to address semantic interoperability issues in the geospatial domain. These efforts were mainly focused on resolving heterogeneities caused by different and implicit representations of the concepts. However, many approaches have focused on the thematic aspects, leaving aside the explicit representation of spatial and temporal aspects. Also, most semantic interoperability approaches for networks have focused on automating the semantic mapping process. However, the ad hoc network structure is continuously modified by source addition or removal, formation of groups, etc. This dynamic aspect is often neglected in those approaches. This paper proposes a conceptual framework for real time semantic interoperability in ad hoc networks of geospatial data sources. The conceptual framework presents the fundamental elements of real time semantic interoperability through a hierarchy of interrelated semantic states and processes. Then, we use the conceptual framework to set the discussion on the achievements that have already been made, the challenges that remain to be addressed and perspectives with respect to these challenges.

In a traditional Geographical Information System (GIS), one usually characterizes spatial entities by their individual placename (or toponym) and a set of defined attributes. These entities and their attributes are structured in table frames with functional relations, plus coordinates of their geometric primitives for location in a quantified space. In such a case, one considers the geographical names of these entities as unique identifiers under the standardized ‘generic-specific’ binomial, in one-to-one relationships with features of interest on the ground. This technical perspective convenes quite well to administrators of land-based public mandates needing univocal references to particular places for planning as well as daily applications (e.g., municipal, postal, emergency). But, from a linguistic point of view, the exact location and dimensions of places are among other attributes of the toponym itself, since the study of placenames looks mainly to its meaning, etymology, and evolution in particular language(s) through the epochs of naming practices, forming kind of toponymic “strata”. So, that is investigatory, documented, critical, often anecdotal, sometimes even searching for identity, but devotes few considerations to the geographical place per se, its landscape nor its limits. On the other hand, the geographical perspective to placenames comes more concerned by a real or identifiable place within its vicinity and the dynamics of situations occurring in that location at multiple scales in the same period, whatever the names covering part or all the area. Thus, looking for a place based information system to structure perceived or unofficial places of specified interest require the design and development of tools and functionalities able to support analysis of such information for particular purposes (personal, commercial, military, transit, indigenous, participatory, tourism, etc.). In such a context, definition of their substantial components requires other technical means than their descriptive attributes and geometric primitives. Over the rational spatial databases as a base map, the user of a platial information system would design place-entities on an autonomous fashion, more subjective and colloquial, while referring to landmarks for identity or distinction with respect to different people, being inhabitant, local worker or just visitor. The place-entity comes to the mind like a shapeless mass with a core and some peripheries, where it shows an oriented front or façade, or a force line as a trend, and more fuzzy and moveable limits on the other sides. For instance, downtown and the central business district may not correspond to each other as platial synonyms. For this purpose, the necessary data structures remain to be developed. To do so, Voronoi tessellation, with its flexible spatial proximity definition and its topological (instead of geometrical) properties, represents an interesting alternative model for further researches. For short, a Voronoi diagram partitions the space into regions such that any location is associated with its nearest Voronoi generators (centroids representing places). This allows an adaptive discretization of the space, and provides a simple and intuitive basis for the definition of adjacency relations between the generation points. Depending on the distribution of generating points, Voronoi cells can approximate place extensions close to human perception of those places with flexibility and still keep the fundamental qualitative relations between places, thanks to topological properties of this model. Irregularity of such Voronoi model also has a significant advantage that allow to better approximate places and their variable distribution in the geographical space. Here in this paper, we also consider scale as an important factor for the representation and analysis of the places approximated with Voronoi cells. Indeed, in higher level, a place may be constituted either by the aggregation of several places in fine scales or by their parts. Hierarchical Voronoi diagram can also be considered to model such relations between places and their vertical relations, for instance where a same particular placename identifies different features or entities that overlap, evolve, or have various limits or meanings. In such a model, moving between different scales or data “levels” is done without being worried about exact aggregation of lower geometries in a high-level place representation. In order to structure Voronoi hierarchies for a set of points representing centroids of places, one must start by constructing Voronoi cells for the finest level and then creating pointers that relate higher level places to the lower ones. Based on its unique properties, Voronoi tessellation, among strong solutions for platial information systems, can provide a firm base for representing the complexity of places, either as entities (or features) and toponyms that identify or refer to them. Paralleling relational GIS data frames, it would allow to adapt to unusual or fuzzy places, to represent their geographical evolution, to preserve their name and spatial extension, and to take good notes of local or ancient variants and even of exonyms applied to such places from abroad. Thus, it presents an opportunity to map the sense, if not the spirit, of place.

Wildfire simulators based on empirical or physical models need to be locally calibrated and validated when used under conditions that differ from those where the simulators were originally developed. This study aims to calibrate the FARSITE fire spread model considering a set of recent wildfires that occurred in northern Iranian forests. Site-specific fuel models in the study areas were selected by sampling the main natural vegetation type complexes and assigning standard fuel models. Overall, simulated fires presented reliable outputs that accurately replicated the observed fire perimeters and behavior. Standard fuel models of Scott and Burgan (2005) afforded better accuracy in the simulated fire perimeters than the standard fuel models of Anderson (1982). The best match between observed and modeled burned areas was observed on herbaceous fuel models. Fire modeling showed a high potential for estimating spatial variability in fire spread and behavior in the study areas. This work represents a first step in the application of fire spread modeling in northern Iran for wildfire risk monitoring and management.

Recent advances in sensor and platform technologies, such as satellite systems, unmanned aerial vehicles (UAV), manned aerial platforms, and ground-based sensor networks have resulted in massive volumes of data being produced and collected about the earth. Processing, managing, and analyzing these data is one of the main challenges in 3D synthetic representation used in modeling and simulation (M&S) of the natural environment. M&S devices, such as flight simulators, traditionally require a variety of different databases to provide a synthetic representation of the world. M&S often requires integration of data from a variety of sources stored in different formats. Thus, for simulation of a complex synthetic environment, such as a 3D terrain model, tackling interoperability among its components (geospatial data, natural and man-made objects, dynamic and static models) is a critical challenge. Conventional approaches used local proprietary data models and formats. These approaches often lacked interoperability and created silos of content within the simulation community. Therefore, open geospatial standards are increasingly perceived as a means to promote interoperability and reusability for 3D M&S. In this paper, the Open Geospatial Consortium (OGC) CDB Standard is introduced. “CDB” originally referred to Common DataBase, which is currently considered as a name with no abbreviation in the OGC community. The OGC CDB is an international standard for structuring, modeling, and storing geospatial information required in high-performance modeling and simulation applications. CDB defines the core conceptual models, use cases, requirements, and specifications for employing geospatial data in 3D M&S. The main features of the OGC CDB Standard are described as the run-time performance, full plug-and-play interoperable geospatial data store, usefulness in 3D and dynamic simulation environment, ability to integrate proprietary and open-source data formats. Furthermore, compatibility with the OGC standards baseline reduces the complexity of discovering, transforming, and streaming geospatial data into the synthetic environment and makes them more widely acceptable to major geospatial data/software producers. This paper includes an overview of OGC CDB version 1.0, which defines a conceptual model and file structure for the storage, access, and modification of a multi-resolution 3D synthetic environment data store. Finally, this paper presents a perspective of future versions of the OGC CDB and what the steps are for humanizing the OGC CDB standard with the other OGC/ISO standards baseline.

Recent advances in sensor and platform technologies such as satellite systems, unmanned aerial vehicles (UAV), manned aerial platforms, and ground-based sensor networks have resulted in massive volumes of data is produced and collected about the earth. Processing, managing, and analyzing these data is one of the main challenges in 3D synthetic representation used in modeling and simulation (M&S) of the natural environment. M&S devices, such as flight simulators, traditionally require a variety of different databases to provide a synthetic representation of the world. M&S often requires integration of data from a variety of sources stored in different formats. Thus, for simulation of a complex synthetic environment, such as a 3D terrain model, tackling interoperability among its components (geospatial data, natural and man-made objects, dynamic and static models) is a critical challenge. Conventional approaches used local proprietary data models and formats. These approaches often lacked interoperability and created silos of content within the simulation community. Therefore, open geospatial standards are increasingly perceived as a means to promote interoperability and reusability for 3D M&S. In this paper, the Open Geospatial Consortium (OGC) CDB Standard is introduced. “CDB” originally refers to Common DataBase which is currently considered as a name with no abbreviation in the OGC community. The OGC CDB is an international standard for structuring, modeling, and storing geospatial information required in high performance modeling and simulation applications. CDB defines the core conceptual models, use cases, requirements, and specifications for employing geospatial data in 3D M&S. The main features of the OGC CDB Standard are described as run-time performance, full plug-and-play interoperable geospatial data store, usefulness in 3D and dynamic simulation environment, ability to integrate proprietary and open-source data formats. Furthermore, compatibility with the OGC standards baseline reduces the complexity of discovering, transforming, and streaming geospatial data into the synthetic environment and makes them more widely acceptable to major geospatial data/software producers. This paper includes an overview of OGC CDB version 1.0 which defines a conceptual model and file structure for the storage, access, and modification of a multi-resolution 3D synthetic environment data store. Finally, this paper presents a perspective of future versions of the OGC CDB and what the steps are for humanizing the OGC CDB standard with the other OGC/ISO standards baseline.

Sensor deployment optimization to achieve the maximum spatial coverage is one of the main issues in Wireless geoSensor Networks (WSN). The model of the environment is an imperative parameter that influences the accuracy of geosensor coverage. In most of recent studies, the environment has been modeled by Digital Surface Model (DSM). However, the advances in technology to collect 3D vector data at different levels, especially in urban models can enhance the quality of geosensor deployment in order to achieve more accurate coverage estimations. This paper proposes an approach to calculate the geosensor coverage in 3D vector environments. The approach is applied on some case studies and compared with DSM based methods.

Sensors play an increasingly critical role in capturing and distributing observa- tions of phenomena in our environment. The vision of the semantic sensor web is to enable the interoperability of various applications that use sensor data provided by semantically heterogeneous sensor services. However, several challenges still need to be addressed to achieve this vision. More particularly, mechanisms that can support context-aware seman- tic mapping and that can adapt to the dynamic metadata of sensors are required. Semantic mapping for the sensor web is required to support sensor data fusion, sensor data discov- ery and retrieval, and automatic semantic annotation, to name only a few tasks. This pa- per presents a context-awareontology-based semantic mediation service for heterogeneous sensor services. The semantic mediation service is context-aware and dynamic because it takes into account the real-time variability of thematic, spatial, and temporal elements that describe sensor data in different contexts. The semantic mediation service integrates rule- based reasoning to support the resolution of semantic heterogeneities. An application sce- nario is presented showing how the semantic mediation service can improve sensor data interpretation, reuse, and sharing in static and dynamic settings.

One of the main challenges in Coastal Erosion Risk Assessment (CERA) is integrating and analysis of conflicting data in various time periods and spatial scales through dissimilar environmental, social, and economic criteria. Currently, Geographical Information Systems (GIS) are widely used in risk assessment despite their drawbacks and limitations as transactional systems for multi-scales, multi-epochs, and multi-themes analysis. Hence, an analytical conceptual framework is proposed in this paper based on geospatial business intelligence paradigm to develop a Spatial Multidimensional Conceptual Model (SMCM) to assess coastal erosion risk. The model is designed based on Spatial On-Line Analytical Processing (SOLAP) platform, on the top of both analytical and transactional paradigms, to allow fast synthesis of cross-tabulated data and easy comparisons over space, scales, epochs, and themes. This objective is achieved through a comprehensive integration of multiple environmental, social, and economic criteria as well as their interactions at various scales. It also takes into account multiple elements at risk such as people, infrastructure, and built environment as different dimensions of analysis. Using this solution allows decision makers to benefit from on-demand, interactive, and comprehensive information in a way that is not possible using GIS alone. The developed model can easily be adapted for any other coastal region through the proposed framework to perform risk assessment. The advantages and drawbacks of the proposed framework are also discussed and new research perspectives are presented.

This paper proposes a probabilistic sensor model for the optimization of sensor placement. Traditional schemes rely on simple sensor behaviour and environmental factors. The consequences of these oversimplifications are unrealistic simulation of sensor performance and, thus, suboptimal sensor placement. In this paper, we develop a novel probabilistic sensing model for sensors with line-of-sight-based coverage (e.g., cameras) to tackle the sensor placement problem for these sensors. The probabilistic sensing model consists of membership functions for sensing range and sensing angle, which takes into consideration sensing capacity probability as well as critical environmental factors such as terrain topography. We then implement several optimization schemes for sensor placement optimization, including simulated annealing, limited-memory Broyden-Fletcher-Goldfarb-Shanno method, and covariance matrix adaptation evolution strategy.

A precise estimation and the characterization of the spatial variability of microclimate conditions (MCCs) are essential for risk assessment and site-specific management of vector-borne diseases and crop pests. The objective of this study was to estimate at local scale, and assess the uncertainties of Surface Microclimate Indicators (SMIs) derived from airborne infrared thermography and multispectral imaging. SMIs including Surface Temperature (ST) were estimated in southern Quebec, Canada. The formulation of their uncertainties was based on in-situ observations and the law of propagation of uncertainty. SMIs showed strong local variability and intra-plot variability of MCCs in the study area. The ST values ranged from 290 K to 331 K. They varied more than 17 K on vegetable crop fields. The correlation between ST and in-situ observations was very high (r = 0.99, p = 0.010). The uncertainty and the bias of ST compared to in-situ observations were 0.73 K and ±1.42 K respectively. This study demonstrated that very high spatial resolution multispectral imaging and infrared thermography present a good potential for the characterization of the MCCs that govern the abundance and the behavior of disease vectors and crop pests in a given area. (Resume d'auteur)

GIScience 2016 Short Paper Proceedings A novel method for probabilistic coverage estimation of sensor networks based on 3D vector representation in complex urban environments A. Afghantoloee 1 , F. Karimipour 2 , M. A. Mostafavi 1 Center for Research in Geomatics, Department of Geomatics, Universite Laval, Quebec, Canada Email: ali.afghantoloee.1@ulaval.ca;mir-abolfazl.mostafavi@scg.ulaval.ca Dept. of Geomatic Engineering, University of Tehran, Amir-abad Street, Tehran, Iran Email: fkarimipr@ut.ac.ir Abstract Wireless Sensor Networks (WSNs) are widely used for monitoring and observation of dynamic phenomena. A sensor in WSNs covers only a limited region, depending on its sensing and communicating ranges, as well as the environment configuration. For efficient deployment of sensors in a WSN the coverage estimation is a critical issue. Probabilistic methods are among the most accurate models proposed for sensor coverage estimation. However, most of these methods are based on raster representation of the environment for coverage estimation which limits their quality. In this paper, we propose a probabilistic method for estimation of the coverage of a sensor network based on 3D vector representation of the environment. 1. Introduction Nowadays, WSNs have found various applications in industry, security, agriculture, military and disaster management. Efficient monitoring and management of dynamic phenomena in the real world necessitates its efficient and accurate coverage. The efficiency of the coverage of a sensor network depends on optimal position of each sensor node within the network. An individual sensor covers only a limited area, which depends on its sensing capacity, range of communication as well as the environment’s complexity. The total area covered by a WSN is obtained from the union of the regions covered by individual sensors. Therefore, efficient deployment of sensors in a WSN is a critical issue that affects the coverage as well as communication between sensors. Several optimization methods (i.e., global or local, deterministic or stochastic, etc.) have been proposed to detect and eliminate coverage holes and hence increase the coverage of sensor networks (Argany et al. 2015). One of the key issues of all deployment optimization algorithms is accurate estimation of the coverage of an individual sensor. Sensing model of individual sensors ─which could be binary or probabilistic, omnidirectional or directional─ has significant impact on the precise coverage estimation of a sensor network using diverse optimization algorithms. Most of the sensor coverage estimation methods use a raster representation of the environment (Akbarzadeh et al. 2013) for optimization purposes that limits their precision and efficiency. This is because raster representations are constrained by their spatial resolution, and their regular shapes result in redundant data for unoccupied areas. Few vector-based optimization algorithms are proposed in the literature, which are mostly based on 2D vector-based representation of the environment and do not adequately consider the presence of manmade and natural obstacles in the sensing areas (Wang and Cao 2011). To overcome these limitations, in this paper we propose a probabilistic sensor coverage estimation method based on precise 3D vector-base representation of the environment and we present some results of an ongoing research project that aims at better optimization of a sensor network in a 3D complex urban area.

GIScience 2016 Short Paper Proceedings An Object Based Approach for Submarine Canyon Identification from Surface Networks Andres Cortes Murcia 1,2 , Eric Guilbert 1,2 , Mir Abolfazl Mostafavi 1,2 Dept. of Geomatics Sciences, Universite Laval, Quebec, G1V 0A6 (QC) Canada Center for research in geomatics, Universite Laval, Quebec, G1V 0A6 (QC) Canada Email: andres.cortes-murcia.1@ulaval.ca {eric.guilbert; mir-abolfazl.mostafavi}@scg.ulaval.ca Abstract In this paper we propose using surface networks to identify submarine canyons from bathymetric data. Identification is done in two steps. First, thalweg lines that fit the canyon definition are extracted; second, the floor around each thalweg is measured to separate steep narrow canyons from broader channels. Results are validated against a classification provided by geomorphologists. 1. Introduction Submarine canyons are relevant features for geomorphologists because they can explain the origin and evolution of marine landscape. Although there is a common understanding of what a canyon is, its description is often vague and its definition is not applicable for automatic classification. This issue leads to define parameters with arbitrary values that are difficult to establish. Traditional methods require image segmentation and classification. These approaches compute discrete local descriptors such as the curvature and depend on threshold parameters chosen by the user. Furtermore, image classification can omit global photo-interpretative characteristics. In general, photo-interpreters identify canyons by their overall shape (narrow, elongated, steep slopes) and position (running across the continental slope in a straight line), observed around salient thalwegs. This paper proposes an approach where thalwegs are extracted from a terrain surface network and canyons are built around them. We move away from pixel classification to an object-oriented approach built on a topological structure. The surface network is a graph where critical points such as pits and peaks are connected by ridges and thalwegs. Its extraction does not require any parameter. Relevant thalwegs are selected by simplifying the surface network. Simplification parameters are not set locally at pixel level but at the structure level. The valley floor is computed around each thalweg and used to classify canyons and other channels. The method is illustrated on a triangulated irregular network generated from multibeam sounding data from the St-Lawrence estuary (Canada). Results were validated against a manual classification performed by geomorphologists (Normandeau et a.l 2015). 2. Surface Network Construction A surface network is a topological graph. Its nodes are pits (local minima), peaks (local maxima) and saddles (points being a local maximum in one direction and a local minimum in another direction). Saddles are connected to peaks by ridges and to pits by thalwegs (Figure 1). The integrity of the surface network is guaranteed by several topological constraints.

GIScience 2016 Short Paper Proceedings Modelling Vague Shape Dynamic Phenomena from Sensor Network data using a Decentralized Fuzzy Rule-Based Approach Roger Cesarie Ntankouo Njila 1 , Mir Abolfazl Mostafavi 1 , Jean Brodeur 1 Centre de Recherche en Geomatique, 2314 Pavillon Casault; Universite Laval, Quebec, Canada, G1K 7P4 Email: roger-cesarie.ntankouo-njila.1@ulaval.ca, Mir-Abolfazl.Mostafavi@scg.ulaval.ca, recherchegeosemantic@videotron.ca Abstract Modelling dynamic phenomena of vague shape from sensor data is still a challenging problem for many applications. In this paper, we propose a decentralized fuzzy rule-based approach based on fuzzy object model to build a more realistic spatiotemporal representation for such phenomena. This approach has been successfully implemented in a simulation case of bushfire monitoring, showing advantages for spatial decision making in a disaster management context. Keywords: sensors, sensor data, fuzzy objects, disaster management, spatial decision support 1. Introduction Extracting geospatial information from geosensor data can help to better understand a complex phenomenon for real time decision making process (Sadeq et al. 2013). Several approaches are used for the extraction of geospatial information from sensor data. Many of these approaches are developed based on the assumption that monitored phenomena are of crisp shape with well- defined boundaries. However, many dynamic phenomena have vague spatial boundaries, and their accurate detection and extraction from sensor data is a challenging problem. In this paper we propose a decentralized fuzzy rule-based approach to address this problem. In the proposed method, sensors detect vague shape phenomena using a fuzzy logic reasoning approach and collaborate with their neighboring sensors to infer vague spatial extent of the phenomena and its dynamics. We adopt Crisp-Fuzzy objects (Pauly and Schneider 2008) as a more realistic model for large scale and vague shape dynamic phenomena. This paper is organized as follows. After a brief background presented in section 2, section 3 describes the proposed approach implemented in section 4 for a bushfire simulation case showing it applicability for real time spatial decision process in disaster management. Section 5 presents conclusions and future works. 2. Background Spatial computing can be undertaken in a sensor system following a centralized (all data are sent to process center), a decentralized (located at sensor site or other site) or a hybrid approach (Chong and Kumar 2003). Crisp vector objects are extracted in the existing approaches using statistics or filters (Chintalapudi and Govindan 2003) or qualitative reasoning (Guan and Duckham 2009). Real-world phenomena are inherently uncertain (Carniel et al. 2015). Crisp-Fuzzy objects model (Pauly and Schneider 2008) is an interesting candidate for the representation of such phenomenon. In this model the geometry of object is composed of a kernel and a conjecture part, the kernel part belongs definitely and always to the vague object but one can’t say with certainty whether conjecture part considered as the broad boundary belongs to the vague object.

Large volumes of geospatial data are increasingly made available through dynamic networks, such as ad hoc networks. Consequently, new adapted query propagation approaches that take into account geospatial aspects of data are needed. Different existing query propagation approaches use various criteria to select relevant sources. In addition, many approaches rely on existing semantic mappings between sources; however, in ad hoc networks, sources move autonomously and in a dynamic fashion. Our goal is rather to reduce the number of sources that must be accessed to answer a query and, therefore, to reduce the volume of semantic mappings that needs to be computed to answer the query. In this paper, we propose three real time query propagation strategies to address this issue. Those strategies reproduce the behavior of members of a social network. The strategies are designed to be part of a real time semantic interoperability framework for ad hoc networks of geospatial databases. The strategies have been formalized with the Lightweight Coordination Calculus (LCC), which support distributed interactions based on social norms and constraints. The implementation and testing of the strategies show that they complement each other to provide optimal query answers.

Geospatial modelling of the marine pelagic ecosystem is challenging due to its dynamic and volumetric nature. Consequently, conventional oceanographic spatial analysis of this environment is in a 2D environment, limited to static cutting planes in horizontal and vertical sections to present various phenomena. In this paper, we explore the contribution of recent 3D development in GIS and in scientific visualization tools for representation and analyses of oceanographic data sets. The advantages of a 3D solution are illustrated with a 3D geospatial voxel representation of water masses distribution in the southeastern Beaufort Sea (west of the Canadian Arctic).

LiDAR technology can provide very detailed and highly accurate geospatial information on an urban scene for the creation of Virtual Geographic Environments (VGEs) for different applications. However, automatic 3D modeling and feature recognition from LiDAR point clouds are very complex tasks. This becomes even more complex when the data is incomplete (occlusion problem) or uncertain. In this paper, we propose to build a knowledge base comprising of ontology and semantic rules aiming at automatic feature recognition from point clouds in support of 3D modeling. First, several modules for ontology are defined from different perspectives to describe an urban scene. For instance, the spatial relations module allows the formalized representation of possible topological relations extracted from point clouds. Then, a knowledge base is proposed that contains different concepts, their properties and their relations, together with constraints and semantic rules. Then, instances and their specific relations form an urban scene and are added to the knowledge base as facts. Based on the knowledge and semantic rules, a reasoning process is carried out to extract semantic features of the objects and their components in the urban scene. Finally, several experiments are presented to show the validity of our approach to recognize different semantic features of buildings from LiDAR point clouds.

Wireless sensor networks are increasingly used for tracking and monitoring dynamic phenomena in urban and natural areas. Spatial coverage is an important issue in sensor networks in order to fulfill the needs of sensing applications. Optimization methods are widely used to efficiently distribute sensor nodes in the network to achieve a desired level of coverage. Most of the existing algorithms do not consider the characteristics of the real environment in the optimization process. In this paper, we propose the integration of contextual information in optimization algorithms to improve sensor network coverage. First, we investigate the implication of contextual information in sensor networks. Then, a conceptual framework for local context-aware sensor network deployment optimization method is introduced and related algorithms are presented in detail. Finally, several experiments are carried out to evaluate the validity of the proposed method. The results obtained from these experiments show the effectiveness of our approach in different contextual situations.

Wildfire simulators based on empirical or physical models need to be locally calibrated and validated when used under conditions that differ from those where the simulators were originally developed. This study aims to calibrate FARSITE fire spread model considering a set of recent wildfires occurred in Northern Iran forests. Site specific fuel models in the study areas were selected by sampling the main natural vegetation type complexes and assigning standard fuel models. Overall, simulated fires presented reliable outputs that accurately replicated the observed fire perimeters and behavior. Standard fuel models of Scott and Burgan (2005) afforded better accuracy in the simulated fire perimeters than the standard fuel models of Anderson (1982). The best match between observed and modeled burned areas was observed on herbaceous type fuel models. Fire modeling showed a high potential for estimating spatial variability in fire spread and behavior in the study areas. This work represents a first step in the application of fire spread modeling on Northern Iran for wildfire risk monitoring and management.

Spatial information for coastal risk assessment is inherently uncertain. This uncertainty may be due to different spatial and temporal components of geospatial data and to their semantics. The spatial uncertainty can be expressed either quantitatively or qualitatively. Spatial uncertainty in coastal risk assessment itself arises from poor spatial representation of risk zones. Indeed, coastal risk is inherently a dynamic, complex, scale-dependent, and vague, phenomenon in concept. In addition, representing the associated zones with polygons having well-defined boundaries does not provide a realistic method for efficient and accurate representing of the risk. This paper proposes a conceptual framework, based on fuzzy set theory, to deal with the problems of ill-defined risk zone boundaries and the inherent uncertainty issues. To do so, the nature and level of uncertainty, as well as the way to model it are characterized. Then, a fuzzy representation method is developed where the membership functions are derived based on expert-knowledge. The proposed approach is then applied in the Perce region (Eastern Quebec, Canada) and results are presented and discussed.

Recent advances in electrical, mechanical and communication systems have led to development of efficient low-cost and multi-function geosensor networks. The efficiency of a geosensor network is significantly based on network coverage, which is the result of network deployment. Several optimization methods have been proposed to enhance the deployment efficiency and hence increase the coverage, but most of them considered the problem in the 2D environment models, which is usually far from the real situation. This paper extends a Voronoi-based deployment algorithm to 3D environment, which takes the 3D features into account. The proposed approach is applied on two case studies whose results are evaluated and discussed.