https://sam.ensam.eu:443 The DSpace digital repository system captures, stores, indexes, preserves, and distributes digital research material. Mon, 13 Jul 2026 07:24:03 GMT 2026-07-13T07:24:03Z http://hdl.handle.net/10985/6695 Spatial models for context-aware indoor navigation systems: A survey AFYOUNI, Imad; RAY, Cyril; CLARAMUNT, Christophe This paper surveys indoor spatial models developed for research fields ranging from mobile robot mapping, to indoor location-based services (LBS), and most recently to context-aware navigation services applied to indoor environments. Over the past few years, several studies have evaluated the potential of spatial models for robot navigation and ubiquitous computing. In this paper we take a slightly different perspective, considering not only the underlying properties of those spatial models, but also to which degree the notion of context can be taken into account when delivering services in indoor environments. Some preliminary recommendations for the development of indoor spatial models are introduced from a context-aware perspective. A taxonomy of models is then presented and assessed with the aim of providing a flexible spatial data model for navigation purposes, and by taking into account the context dimensions. Sun, 01 Jan 2012 00:00:00 GMT http://hdl.handle.net/10985/6695 2012-01-01T00:00:00Z AFYOUNI, Imad RAY, Cyril CLARAMUNT, Christophe This paper surveys indoor spatial models developed for research fields ranging from mobile robot mapping, to indoor location-based services (LBS), and most recently to context-aware navigation services applied to indoor environments. Over the past few years, several studies have evaluated the potential of spatial models for robot navigation and ubiquitous computing. In this paper we take a slightly different perspective, considering not only the underlying properties of those spatial models, but also to which degree the notion of context can be taken into account when delivering services in indoor environments. Some preliminary recommendations for the development of indoor spatial models are introduced from a context-aware perspective. A taxonomy of models is then presented and assessed with the aim of providing a flexible spatial data model for navigation purposes, and by taking into account the context dimensions. http://hdl.handle.net/10985/8780 A semantic and language-based representation of an environmental scene LE YAOUANC, Jean-Marie; SAUX, Eric; CLARAMUNT, Christophe The modeling of a landscape environment is a cognitive activity that requires appropriate spatial representations. The research presented in this paper introduces a structural and semantic categorization of a landscape view based on panoramic photographs that act as a substitute of a given natural environment. Verbal descriptions of a landscape scene provide themodeling input of our approach. This structure-based model identifies the spatial, relational, and semantic constructs that emerge from these descriptions. Concepts in the environment are qualified according to a semantic classification, their proximity and direction to the observer, and the spatial relations that qualify them. The resulting model is represented in a way that constitutes a modeling support for the study of environmental scenes, and a contribution for further research oriented to the mapping of a verbal description onto a geographical information system-based representation. Fri, 01 Jan 2010 00:00:00 GMT http://hdl.handle.net/10985/8780 2010-01-01T00:00:00Z LE YAOUANC, Jean-Marie SAUX, Eric CLARAMUNT, Christophe The modeling of a landscape environment is a cognitive activity that requires appropriate spatial representations. The research presented in this paper introduces a structural and semantic categorization of a landscape view based on panoramic photographs that act as a substitute of a given natural environment. Verbal descriptions of a landscape scene provide themodeling input of our approach. This structure-based model identifies the spatial, relational, and semantic constructs that emerge from these descriptions. Concepts in the environment are qualified according to a semantic classification, their proximity and direction to the observer, and the spatial relations that qualify them. The resulting model is represented in a way that constitutes a modeling support for the study of environmental scenes, and a contribution for further research oriented to the mapping of a verbal description onto a geographical information system-based representation. http://hdl.handle.net/10985/10299 Local and global spatio-temporal entropy indices based on distance- ratios and co-occurrences distributions LEIBOVICI, Didier G.; CLARAMUNT, Christophe; LE GUYADER, Damien; BROSSET, David When it comes to characterize the distribution of ‘things’ observed spatially and identified by their geometries and attributes, the Shannon entropy has been widely used in different domains such as ecology, regional sciences, epidemiology and image analysis. In particular, recent research has taken into account the spatial patterns derived from topological and metric properties in order to propose extensions to the measure of entropy. Based on two different approaches using either distance-ratios or co-occurrences of observed classes, the research developed in this paper introduces several new indices and explores their extensions to the spatio-temporal domains which are derived whilst investigating further their application as global and local indices. Using a multiplicative space-time integration approach either at a macro or micro-level, the approach leads to a series of spatio-temporal entropy indices including from combining co-occurrence and distances-ratios approaches. The framework developed is complementary to the spatio-temporal clustering problem, introducing a more spatial and spatio-temporal structuring perspective using several indices characterizing the distribution of several class instances in space and time. The whole approach is first illustrated on simulated data evolutions of three classes over seven time stamps. Preliminary results are discussed for a study of conflicting maritime activities in the Bay of Brest where the objective is to explore the spatio-temporal patterns exhibited by a categorical variable with six classes, each representing a conflict between two maritime activities. Wed, 01 Jan 2014 00:00:00 GMT http://hdl.handle.net/10985/10299 2014-01-01T00:00:00Z LEIBOVICI, Didier G. CLARAMUNT, Christophe LE GUYADER, Damien BROSSET, David When it comes to characterize the distribution of ‘things’ observed spatially and identified by their geometries and attributes, the Shannon entropy has been widely used in different domains such as ecology, regional sciences, epidemiology and image analysis. In particular, recent research has taken into account the spatial patterns derived from topological and metric properties in order to propose extensions to the measure of entropy. Based on two different approaches using either distance-ratios or co-occurrences of observed classes, the research developed in this paper introduces several new indices and explores their extensions to the spatio-temporal domains which are derived whilst investigating further their application as global and local indices. Using a multiplicative space-time integration approach either at a macro or micro-level, the approach leads to a series of spatio-temporal entropy indices including from combining co-occurrence and distances-ratios approaches. The framework developed is complementary to the spatio-temporal clustering problem, introducing a more spatial and spatio-temporal structuring perspective using several indices characterizing the distribution of several class instances in space and time. The whole approach is first illustrated on simulated data evolutions of three classes over seven time stamps. Preliminary results are discussed for a study of conflicting maritime activities in the Bay of Brest where the objective is to explore the spatio-temporal patterns exhibited by a categorical variable with six classes, each representing a conflict between two maritime activities. http://hdl.handle.net/10985/10297 Exploring Geographical Crowd’s Emotions with Twitter WAKAMIYA, Shoko; BELOUAER, Lamia; BROSSET, David; KAWAI, Yukiko; CLARAMUNT, Christophe; SUMIYA, Kazutoshi The research introduced in this paper develops a semantic model whose objective is to analyze the geographical and emotion-based distribution of tweets at a large country scale. The approach extracts and categorizes tweets based on semantic orientations of terms in a dictionary, and explores their spatial and temporal distribution. Tweets are classified into different emotional classes, qualified and valued using different interval distributions that favor identification of significant trends that are compared to some of the main properties of the underlying geographical space. The whole approach is applied to a large tweets database in Japan, and illustrated by some experimental but real data that trigger some surprising and puzzling outcomes that are discussed in the paper. Thu, 01 Jan 2015 00:00:00 GMT http://hdl.handle.net/10985/10297 2015-01-01T00:00:00Z WAKAMIYA, Shoko BELOUAER, Lamia BROSSET, David KAWAI, Yukiko CLARAMUNT, Christophe SUMIYA, Kazutoshi The research introduced in this paper develops a semantic model whose objective is to analyze the geographical and emotion-based distribution of tweets at a large country scale. The approach extracts and categorizes tweets based on semantic orientations of terms in a dictionary, and explores their spatial and temporal distribution. Tweets are classified into different emotional classes, qualified and valued using different interval distributions that favor identification of significant trends that are compared to some of the main properties of the underlying geographical space. The whole approach is applied to a large tweets database in Japan, and illustrated by some experimental but real data that trigger some surprising and puzzling outcomes that are discussed in the paper. http://hdl.handle.net/10985/13797 A Spatio-Temporal Entropy-based Framework for the Detection of Trajectories Similarity HOSSEINPOOR MILAGHARDAN, Amin; ABBASPOUR, Rahim Ali; CLARAMUNT, Christophe The rapid proliferation of sensors and big data repositories offer many new opportunities for data science. Among many application domains, the analysis of large trajectory datasets generated from people’s movements at the city scale is one of the most promising research avenues still to explore. Extracting trajectory patterns and outliers in urban environments is a direction still requiring exploration for many management and planning tasks. The research developed in this paper introduces a spatio-temporal framework, so-called STE-SD (Spatio-Temporal Entropy for Similarity Detection), based on the initial concept of entropy as introduced by Shannon in his seminal theory of information and as recently extended to the spatial and temporal dimensions. Our approach considers several complementary trajectory descriptors whose distribution in space and time are quantitatively evaluated. The trajectory primitives considered include curvatures, stop-points, self-intersections and velocities. These primitives are identified and then qualified using the notion of entropy as applied to the spatial and temporal dimensions. The whole approach is experimented and applied to urban trajectories derived from the Geolife dataset, a reference data benchmark available in the city of Beijing. Mon, 01 Jan 2018 00:00:00 GMT http://hdl.handle.net/10985/13797 2018-01-01T00:00:00Z HOSSEINPOOR MILAGHARDAN, Amin ABBASPOUR, Rahim Ali CLARAMUNT, Christophe The rapid proliferation of sensors and big data repositories offer many new opportunities for data science. Among many application domains, the analysis of large trajectory datasets generated from people’s movements at the city scale is one of the most promising research avenues still to explore. Extracting trajectory patterns and outliers in urban environments is a direction still requiring exploration for many management and planning tasks. The research developed in this paper introduces a spatio-temporal framework, so-called STE-SD (Spatio-Temporal Entropy for Similarity Detection), based on the initial concept of entropy as introduced by Shannon in his seminal theory of information and as recently extended to the spatial and temporal dimensions. Our approach considers several complementary trajectory descriptors whose distribution in space and time are quantitatively evaluated. The trajectory primitives considered include curvatures, stop-points, self-intersections and velocities. These primitives are identified and then qualified using the notion of entropy as applied to the spatial and temporal dimensions. The whole approach is experimented and applied to urban trajectories derived from the Geolife dataset, a reference data benchmark available in the city of Beijing. http://hdl.handle.net/10985/13795 A Geometric Framework for Detection of Critical Points in a Trajectory Using Convex Hulls HOSSEINPOOR MILAGHARDAN, Amin; ABBASPOUR, Rahim Ali; CLARAMUNT, Christophe Large volumes of trajectory-based data require development of appropriate data manipulation mechanisms that will offer efficient computational solutions. In particular, identification of meaningful geometric points of such trajectories is still an open research issue. Detection of these critical points implies to identify self-intersecting, turning and curvature points so that specific geometric characteristics that are worth identifying could be denoted. This research introduces an approach called Trajectory Critical Point detection using Convex Hull (TCP-CH) to identify a minimum number of critical points. The results can be applied to large trajectory data sets in order to reduce storage costs and complexity for further data mining and analysis. The main principles of the TCP-CH algorithm include computing: convex areas, convex hull curvatures, turning points, and intersecting points. The experimental validation applied to Geolife trajectory dataset reveals that the proposed framework can identify most of intersecting points in reasonable computing time. Finally, comparison of the proposed algorithm with other methods, such as turning function shows that our approach performs relatively well when considering the overall detection quality and computing time. Mon, 01 Jan 2018 00:00:00 GMT http://hdl.handle.net/10985/13795 2018-01-01T00:00:00Z HOSSEINPOOR MILAGHARDAN, Amin ABBASPOUR, Rahim Ali CLARAMUNT, Christophe Large volumes of trajectory-based data require development of appropriate data manipulation mechanisms that will offer efficient computational solutions. In particular, identification of meaningful geometric points of such trajectories is still an open research issue. Detection of these critical points implies to identify self-intersecting, turning and curvature points so that specific geometric characteristics that are worth identifying could be denoted. This research introduces an approach called Trajectory Critical Point detection using Convex Hull (TCP-CH) to identify a minimum number of critical points. The results can be applied to large trajectory data sets in order to reduce storage costs and complexity for further data mining and analysis. The main principles of the TCP-CH algorithm include computing: convex areas, convex hull curvatures, turning points, and intersecting points. The experimental validation applied to Geolife trajectory dataset reveals that the proposed framework can identify most of intersecting points in reasonable computing time. Finally, comparison of the proposed algorithm with other methods, such as turning function shows that our approach performs relatively well when considering the overall detection quality and computing time. http://hdl.handle.net/10985/13806 A Survey of Modelling Trends in Temporal GIS SIABATO, Willington; CLARAMUNT, Christophe; ILARRI, Sergio; MANSO-CALLEJO, Miguel Angel The main achievements of spatio-temporal modelling in the field of Geographic Information Science that spans over the past three decades are surveyed. This article offers an overview of: (i) the origins and history of Temporal Geographic Information Systems (T-GIS); (ii) relevant spatio-temporal data models proposed; (iii) the evolution of spatio-temporal modelling trends; and (iv) an analysis of the future trends and developments in T-GIS. It also presents some current theories and concepts that have emerged from the research performed, as well as a summary of the current progress and the upcoming challenges and potential research directions for T-GIS. One relevant result of this survey is the proposed taxonomy of spatio-temporal modelling trends, which classifies 186 modelling proposals surveyed from more than 1450 articles Mon, 01 Jan 2018 00:00:00 GMT http://hdl.handle.net/10985/13806 2018-01-01T00:00:00Z SIABATO, Willington CLARAMUNT, Christophe ILARRI, Sergio MANSO-CALLEJO, Miguel Angel The main achievements of spatio-temporal modelling in the field of Geographic Information Science that spans over the past three decades are surveyed. This article offers an overview of: (i) the origins and history of Temporal Geographic Information Systems (T-GIS); (ii) relevant spatio-temporal data models proposed; (iii) the evolution of spatio-temporal modelling trends; and (iv) an analysis of the future trends and developments in T-GIS. It also presents some current theories and concepts that have emerged from the research performed, as well as a summary of the current progress and the upcoming challenges and potential research directions for T-GIS. One relevant result of this survey is the proposed taxonomy of spatio-temporal modelling trends, which classifies 186 modelling proposals surveyed from more than 1450 articles http://hdl.handle.net/10985/15035 Fictive motion extraction and classification EGOROVA, Ekaterina; MONCLA, Ludovic; GAIO, Mauro; CLARAMUNT, Christophe; PURVES, Ross Fictive motion (e.g. ‘The highway runs along the coast’) is a pervasive phenomenon in language that can imply both a static and a moving observer. In a corpus of alpine narratives, it is used in three types of spatial descriptions: conveying the actual motion of the observer, describing a vista and communicating encyclopaedic spatial knowledge. This study takes a knowledge-based approach to develop rules for automated extraction and classification of these types based on an annotated corpus of fictive motion instances. In particular, we identify the differences in the set of concepts involved into the production of the three types of descriptions, followed by their linguistic operationalization. Based on that, we build a set of rules that classify fictive motion with an overall precision of 0.87 and recall of 0.71. The article highlights the importance of examining spatially rich, naturally occurring corpora for the lines of work dealing with the automated interpretation of spatial information in texts, as well as, more broadly, investigation of spatial language involved into various types of spatial discourse. Mon, 01 Jan 2018 00:00:00 GMT http://hdl.handle.net/10985/15035 2018-01-01T00:00:00Z EGOROVA, Ekaterina MONCLA, Ludovic GAIO, Mauro CLARAMUNT, Christophe PURVES, Ross Fictive motion (e.g. ‘The highway runs along the coast’) is a pervasive phenomenon in language that can imply both a static and a moving observer. In a corpus of alpine narratives, it is used in three types of spatial descriptions: conveying the actual motion of the observer, describing a vista and communicating encyclopaedic spatial knowledge. This study takes a knowledge-based approach to develop rules for automated extraction and classification of these types based on an annotated corpus of fictive motion instances. In particular, we identify the differences in the set of concepts involved into the production of the three types of descriptions, followed by their linguistic operationalization. Based on that, we build a set of rules that classify fictive motion with an overall precision of 0.87 and recall of 0.71. The article highlights the importance of examining spatially rich, naturally occurring corpora for the lines of work dealing with the automated interpretation of spatial information in texts, as well as, more broadly, investigation of spatial language involved into various types of spatial discourse. http://hdl.handle.net/10985/15017 A graph-based approach for the structural analysis of road and building layouts DOMINGO, Mathieu; THIBAUD, Rémy; CLARAMUNT, Christophe A better understanding of the relationship between the structure and functions of urban and suburban spaces is one of the avenues of research still open for geographical information science. The research presented in this paper develops several graph-based metrics whose objective is to characterize some local and global structural properties that reflect the way the overall building layout can be cross-related to the one of the road layout. Such structural properties are modeled as an aggregation of parcels, buildings, and road networks. We introduce several computational measures (Ratio Minimum Distance, Minimum Ratio Minimum Distance, and Metric Compactness) that respectively evaluate the capability for a given road to be connected with the whole road network. These measures reveal emerging sub-network structures and point out differences between less-connective and moreconnective parts of the network. Based on these local and global properties derived from the topological and graph-based representation, and on building density metrics, this paper proposes an analysis of road and building layouts at different levels of granularity. The metrics developed are applied to a case study in which the derived properties reveal coherent as well as incoherent neighborhoods that illustrate the potential of the approach and the way buildings and roads can be relatively connected in a given urban environment. Overall, and by integrating the parcels and buildings layouts, this approach complements other previous and related works that mainly retain the configurational structure of the urban network as well as morphological studies whose focus is generally limited to the analysis of the building layout. Tue, 01 Jan 2019 00:00:00 GMT http://hdl.handle.net/10985/15017 2019-01-01T00:00:00Z DOMINGO, Mathieu THIBAUD, Rémy CLARAMUNT, Christophe A better understanding of the relationship between the structure and functions of urban and suburban spaces is one of the avenues of research still open for geographical information science. The research presented in this paper develops several graph-based metrics whose objective is to characterize some local and global structural properties that reflect the way the overall building layout can be cross-related to the one of the road layout. Such structural properties are modeled as an aggregation of parcels, buildings, and road networks. We introduce several computational measures (Ratio Minimum Distance, Minimum Ratio Minimum Distance, and Metric Compactness) that respectively evaluate the capability for a given road to be connected with the whole road network. These measures reveal emerging sub-network structures and point out differences between less-connective and moreconnective parts of the network. Based on these local and global properties derived from the topological and graph-based representation, and on building density metrics, this paper proposes an analysis of road and building layouts at different levels of granularity. The metrics developed are applied to a case study in which the derived properties reveal coherent as well as incoherent neighborhoods that illustrate the potential of the approach and the way buildings and roads can be relatively connected in a given urban environment. Overall, and by integrating the parcels and buildings layouts, this approach complements other previous and related works that mainly retain the configurational structure of the urban network as well as morphological studies whose focus is generally limited to the analysis of the building layout. http://hdl.handle.net/10985/15023 A semantic model for human mobility in an urban region JIN, Meihan; CLARAMUNT, Christophe The continuous development and complexity of many modern cities offer many research challenges for urban scientists searching for a better understanding of mobility patterns that happen in space and time. Today, very large trajectory datasets are often publicly generated thanks to the availability of many positioning sensors and location-based services. However, the successful integration of mobility data still requires the development of conceptual and database frameworks that will support appropriate data representation and manipulation capabilities. The research presented in this paper introduces a conceptual modeling and database management approach for representing and analyzing human trajectories in urban spaces. The model considers the spatial, temporal and semantic dimensions in order to take into account the full range of properties that emerge from mobility patterns. Several object data types and data manipulation constructs are developed and experimented on top of an urban dataset testbed currently available in the city of Beijing. The interest of the approach is twofold: first, it clearly appears that very large mobility datasets can be integrated in current extensible GIS; second, significant patterns can be derived at the database manipulation level using some specifically developed query functions. Mon, 01 Jan 2018 00:00:00 GMT http://hdl.handle.net/10985/15023 2018-01-01T00:00:00Z JIN, Meihan CLARAMUNT, Christophe The continuous development and complexity of many modern cities offer many research challenges for urban scientists searching for a better understanding of mobility patterns that happen in space and time. Today, very large trajectory datasets are often publicly generated thanks to the availability of many positioning sensors and location-based services. However, the successful integration of mobility data still requires the development of conceptual and database frameworks that will support appropriate data representation and manipulation capabilities. The research presented in this paper introduces a conceptual modeling and database management approach for representing and analyzing human trajectories in urban spaces. The model considers the spatial, temporal and semantic dimensions in order to take into account the full range of properties that emerge from mobility patterns. Several object data types and data manipulation constructs are developed and experimented on top of an urban dataset testbed currently available in the city of Beijing. The interest of the approach is twofold: first, it clearly appears that very large mobility datasets can be integrated in current extensible GIS; second, significant patterns can be derived at the database manipulation level using some specifically developed query functions.