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International audience; Publication dans les archives ISPRS des articles soumis dans le cadre de l'évènement ISPRS Geospatial Week 2015.

National audience

Support of social interactions, the blogosphere constitutes an identity support of construction for tee- nagers. From there, this qualitative study proposes a typology of the blogs highlighting the different moda- lities of these interactions between peers. This study also underlines the potentialities offered by the blo- gosphere for brands wishing to understand the practices of the consumption of teenagers and to build with them a durable relationship on the Internet, Support d’interactions sociales, la blogosphère constitue un vecteur de construction identitaire pour les adolescents. À partir de ce constat, cette étude qualitative propose une typologie des blogs adolescents rendant compte des jeux identitaires et relationnels sur les réseaux sociaux. Dans le prolongement, est pro- posé un ensemble de postures que les marques peuvent adopter sur la blogosphère adolescente afin d’ap- prendre à mieux connaître cette population et interagir efficacement avec elle.

International audience

We present an interactive system to manipulate a virtual object by tracking multiple hands in 3D space using a Kinect device. The system segments hand shapes from a captured 3D scene by using depth information and active contours. A hand shape is recognized by a trained naive Bayes classifier, whether it belongs to a palm, a pointing hand form or both hands with simple occlusion. A plane is approximated by using RANSAC for a palm hand form, while a vector from the hand centroid to the fingertip is obtained for a pointing hand form by using ICP as tracking method. A shape of simple occluded two hands is split into a palm and an incomplete pointing hand form, whose missing data is estimated by using PCA. The system works in semi-real time.

National audience

This paper describes a method for automatic verification of 3D building models using airborne oblique images. The problem being tackled is identifying buildings that are demolished or changed since the models were constructed or identifying wrong models using the images. The models verified are of CityGML LOD2 or higher since their edges are expected to coincide with actual building edges. The verification approach is based on information theory. Corresponding variables between building models and oblique images are used for deriving mutual information for individual edges, faces or whole buildings, and combined for all perspective images available for the building. The wireframe model edges are projected to images and verified using low level image features – the image pixel gradient directions. A building part is only checked against images in which it may be visible. The method has been tested with models constructed using laser points against Pictometry images that are available for most cities of Europe and may be publically viewed in the so called Birds Eye view of the Microsoft Bing Maps. Results are that nearly all buildings are correctly categorised as existing or demolished. Because we now concentrate only on roofs we also used the method to test and compare results from nadir images. This comparison made clear that especially height errors in models can be more reliably detected in oblique images because of the tilted view. Besides overall building verification, results per individual edges can be used for improving the 3D building models.

Brands are confronted today with the increasing power of consumers who appropriate their speech and propagate it in their way. In front of these new practices, in particular of the development of the social networks, they must reconsider their communications strategy and cannot content themselves any more with the "transmitter/receiver" traditional diagram. The production of contents can consequently become an interesting option, especially for public companies whose deficit of image is often considerable., Les marques sont aujourd’hui confrontées au pouvoir grandissant des consommateurs qui s’approprient leur discours et le propagent à leur façon. Face à ces nouvelles pratiques, et notamment à l’essor des réseaux sociaux, elles doivent repenser leur stratégie de communication et ne peuvent plus se contenter du schéma classique «émetteur/récepteur». La production de contenu peut dès lors s’avérer une option intéressante, surtout pour les entreprises publiques qui connaissent un déficit d’image. Le cas SNCF présenté ici permet de comprendre les modalités et les enjeux du brand content pour s’attacher la sympathie et l’intérêt des consommateurs.

In this paper we investigate the influence of contextual knowledge for the classification of airborne laser scanning data in Wadden Sea areas. For this propose we use Conditional Random Fields (CRF) for the classification of the point cloud into the classes water, mudflat, and mussel bed based on geometric and intensity features. We learn typical structures in a training step and combine local descriptors with context information in a CRF framework. It is shown that the point-based classification result, especially the completeness rate for water and mussel bed as well as the correction rate of water, can be significantly improved if contextual knowledge is integrated. We evaluate our approach on a test side of the German part of the Wadden Sea and compare the results with a Maximum Likelihood Classification.

Video surveillance systems are no longer a collection of independent cameras, manually controlled by human operators. Instead, smart sensor networks are developed, able to fulfil certain tasks on their own and thus supporting security personnel by automated analyses. One well-known task is the derivation of people's positions on a given ground plane from monocular video footage. An improved accuracy for the ground position as well as a more detailed representation of single salient people can be expected from a stereoscopic processing of overlapping views. Related work mostly relies on dedicated stereo devices or camera pairs with a small baseline. While this set-up is helpful for the essential step of image matching, the high accuracy potential of a wide baseline and the according good intersection geometry is not utilised. In this paper we present a stereoscopic approach, working on overlapping views of standard pan-tilt-zoom cameras which can easily be generated for arbitrary points of interest by an appropriate reconfiguration of parts of a sensor network. Experiments are conducted on realistic surveillance footage to show the potential of the suggested approach and to investigate the influence of different baselines on the quality of the derived surface model. Promising estimations of people's position and height are retrieved. Although standard matching approaches show helpful results, future work will incorporate temporal dependencies available from image sequences in order to reduce computational effort and improve the derived level of detail. BMBF/13N10809 BMBF/13N10810 BMBF/13N10811 BMBF/13N10812 BMBF/13N10813 BMBF/13N10814

For more than two decades, many efforts have been made to develop methods for extracting urban objects from data acquired by airborne sensors. In order to make the results of such algorithms more comparable, benchmarking data sets are of paramount importance. Such a data set, consisting of airborne image and laserscanner data, has been made available to the scientific community. Researchers were encouraged to submit results of urban object detection and 3D building reconstruction, which were evaluated based on reference data. This paper presents the outcomes of the evaluation for building detection, tree detection, and 3D building reconstruction. The results achieved by different methods are compared and analysed to identify promising strategies for automatic urban object extraction from current airborne sensor data, but also common problems of state-of-the-art methods.

Detection of buildings and vegetation, and even more reconstruction of urban terrain from sequences of aerial images and videos is known to be a challenging task. It has been established that those methods that have as input a high-quality Digital Surface Model (DSM), are more straight-forward and produce more robust and reliable results than those image-based methods that require matching line segments or even whole regions. This motivated us to develop a new dense matching technique for DSM generation that is capable of simultaneous integration of multiple images in the reconstruction process. The DSMs generated by this new multi-image matching technique can be used for urban object extraction. In the first contribution of this paper, two examples of external sources of information added to the reconstruction pipeline will be shown. The GIS layers are used for recognition of streets and suppressing false alarms in the depth maps that were caused by moving vehicles while the near infrared channel is applied for separating vegetation from buildings. Three examples of data sets including both UAV-borne video sequences with a relatively high number of frames and high-resolution (10 cm ground sample distance) data sets consisting of (few spatial-temporarily diverse) images from large-format aerial frame cameras, will be presented. By an extensive quantitative evaluation of the Vaihingen block from the ISPRS benchmark on urban object detection, it will become clear that our procedure allows a straight-forward, efficient, and reliable instantiation of 3D city models.

Our work addresses the problem of long-term visual people tracking in complex environments. Tracking a varying number of objects entails the problem of associating detected objects to tracked targets. To overcome the data association problem, we apply a Tracking-by-Detection strategy that uses Randomized Forests as a classifier together with a Kalman filter. Randomized Forests build a strong classifier for multi-class problems through aggregating simple decision trees. Due to their modular setup, Randomized Forests can be built incrementally, which makes them useful for unsupervised learning of object features in real-time. New training samples can be incorporated on the fly, while not drifting away from previously learnt features. To support further analysis of the automatically generated trajectories, we annotate them with quality metrics based on the association confidence. To build the metrics we analyse the confidence values that derive from the Randomized Forests and the similarity of detected and tracked objects. We evaluate the performance of the overall approach with respect to available reference data of people crossing the scene.

The precise classification and reconstruction of crossroads from multiple aerial images is a challenging problem in remote sensing. We apply the Markov Random Fields (MRF) approach to this problem, a probabilistic model that can be used to consider context in classification. A simple appearance-based model is combined with a probabilistic model of the co-occurrence of class label at neighbouring image sites to distinguish up to 14 different classes that are relevant for scenes containing crossroads. The parameters of these models are learnt from training data. We use multiple overlap aerial images to derive a digital surface model (DSM) and a true orthophoto without moving cars. From the DSM and the orthophoto we derive feature vectors that are used in the classification. One of the features is a car confidence value that is supposed to support the classification when the road surface is occluded by static cars. Our approach is evaluated on a dataset of airborne photos of an urban area by a comparison of the results to reference data. Whereas the method has problems in distinguishing classes having a similar appearance, it is shown to produce promising results if a reduced set of classes is considered, yielding an overall classification accuracy of 74.8%.

The main objective of this article is the setting in light of the existence of the concept of consideration set among children. In our knowledge, no study tried this day to study the formation of the evoked set within the framework of the children, the existence of this phenomenon thus remaining to be verified. After a short synthesis of the literature on consideration set among adult, we lead a reflection on the potential existence of this concept to the consumer child and deliver the results of a research led with 265 children with the aim of bringing elements of answer as for the existence of this concept among children, and for the modalities of its formation, in particular in term of size., Cette étude a pour objet principal la mise en lumière de l'existence du concept d'ensemble de considération chez l'enfant. A notre connaissance, aucune étude n'a tenté à ce jour d'étudier la formation de ces ensembles dans le cadre de la population enfantine, l'existence même de ce phénomène restant donc à être vérifiée. En parallèle de la littérature sur l'ensemble de considération chez l'adulte, nous menons une réflexion sur l'existence potentielle de ce concept chez l'enfant consommateur et livrons les résultats d'une recherche menée auprès de 265 enfants dans le but d'apporter des éléments de réponse quant à l'existence de ce concept chez l'enfant, et aux modalités de sa formation, notamment en terme de taille.

International audience

In this paper we propose a probabilistic supervised classification algorithm for LiDAR (Light Detection And Ranging) point clouds. Several object classes (i.e. ground, building and vegetation) can be separated reliably by considering each point's neighbourhood. Based on Conditional Random Fields (CRF) this contextual information can be incorporated into classification process in order to improve results. Since we want to perform a point-wise classification, no primarily segmentation is needed. Therefore, each 3D point is regarded as a graph's node, whereas edges represent links to the nearest neighbours. Both nodes and edges are associated with features and have effect on the classification. We use some features available from full waveform technology such as amplitude, echo width and number of echoes as well as some extracted geometrical features. The aim of the paper is to describe the CRF model set-up for irregular point clouds, present the features used for classification, and to discuss some results. The resulting overall accuracy is about 94 %.

Confronted with the generation of the Digital Natives for whom surfing on the Internet is as natural as speaking, brands have no other choice but to question the traditional diagrams of communication and to move towards a 2.0 communication. In this context, this study tries to identify the various methods of presence offered by the blogosphere to brands, via the analysis of the Hatier campaign in particular., Confrontées à la génération des Digital Natives pour qui surfer sur Internet est aussi naturel que parler, les marques n’ont d’autres choix que de remettre en cause les schémas classiques de communication au profit d’une communication 2.0. Dans ce contexte, cette étude tente d’identifier les différentes modalités de présence offertes par la blogosphère aux marques, via l’analyse de la campagne Hatier.

National audience

[Departement_IRSTEA]Territoires [TR1_IRSTEA]SYNERGIE; Le projet ExFOLIO avait comme objectifs scientifiques (1) d’aider à la spécification de capteurs LiDAR spatiaux pour la caractérisation du sol et du sursol et (2) de définir des chaînes de traitement pour extraire à partir de différents types de données lidar une information adaptée aux besoins des utilisateurs. Il avait aussi comme objectif de contribuer au niveau national à l’animation et à la fédération des activités de recherche en LiDAR pour les surfaces continentales et d’établir les bases de partenariats internationaux. Le projet ExFOLIO a fait l’objet d’un soutien du CNES de 2006 à 2010. Les premières années le contenu du projet a évolué au fur et à mesure de la structuration du partenariat qui a évolué afin de répondre au mieux aux objectifs fixés initialement. Pour atteindre les objectifs scientifiques le travail a été axé sur l’analyse de la nature des informations contenues dans le retour d’onde complet de faisceaux laser pour caractériser le terrain et le sursol dans différents types de milieux (urbains, forestiers, badlands). Des méthodes d’analyse du signal lidar ont été développées pour caractériser le terrain, le milieu urbain et la végétation forestière à partir de données acquises par des lidar topographiques à petite empreinte et à retour d’onde complète. Des méthodes de qualification de l’information extraite ont aussi été développées en s’appuyant sur des données de référence acquises lors de campagnes de mesure sur le terrain qui ont nécessité la mise en place de protocoles de mesure adaptés. Pour l’étude du milieu forestier des expérimentations ont été réalisées à l’aide d’un prototype développé par le CEA/LSCE ont débuté en 2008. Ces expérimentations ont permis d’analyser des données avec des caractéristiques différentes (longueur d’onde, taille d ‘empreinte) de celles acquises avec des systèmes industriels apportant ainsi des éléments clés pour le développement de systèmes Lidar adaptés à la caractérisation et au suivi des couverts forestiers. Par ailleurs, en complément de l’analyse de données expérimentales, un travail sur la simulation du signal lidar sur de la végétation a été réalisé. En effet disposer d’un outil permettant de simuler le signal dans plusieurs types de végétations est indispensable à la spécification de futurs systèmes embarqués et permet de tester l’impact de différents paramètres systèmes (taille d’empreinte, longueur d’onde, puissance émise…) sur la qualité du signal enregistré. Les premiers résultats obtenus par modélisation du signal Lidar (modèle de transfert radiatif développés par l’UMR AMAP par lancer de rayons sur des maquettes architecturales de plantes) se sont révélés très prometteurs. Les simulations ont en particulier permis d’analyser l’effet des rétrodiffusions multiples sur la qualité du signal retour selon la longueur d’onde. Sur la base de l’expérience acquise dans le cadre du projet ExFOLIO le projet de mission spatiale LEAF (Lidar for EArth and Forests) a été soumis à l’ESA en 2010.

International audience; The trophic fate of benthic bacteria in an intertidal mudflat (Brouage, Marennes-Oléron, France) was evaluated in situ, and environmental parameters that potentially influence the consumption of bacteria by the most abundant organisms of the meio- and macrofauna were identified. Variations in grazing rates were estimated using 15N pre-enriched bacteria at different temporal and spatial scales on a cross-shore transect over the mudflat. Grazing incubations were performed in microcosms with freshly collected grazers. Environmental factors varied more by season than by day or sampling station. Bacterial uptake by grazers did not appear to be strongly influenced by abiotic factors and was not linked to bacterial abundance. Algal abundance was negatively correlated with bacterivory in both the nematode community and the foraminifer Ammonia tepida, suggesting that bacteria constitute an alternative resource that is consumed when algae are less abundant. Bacteria were mainly ingested by the mudsnail Hydrobia ulvae and secondarily by nematodes with grazing rates of copepods and A. tepida being considerably lower. The estimated grazing in the upper and middle part of the mudflat represented 7% and 28% of bacterial production, respectively. In the lower mudflat, daily grazing never represented >3% of bacterial production throughout the year. Consequently, grazing appears to be a minor factor in the regulation of bacterial production. Bacterivory did not vary clearly according to season; consequently, the fate of bacteria in this benthic food web is poorly structured by season.

Because of their similarity to humans, rabbits are a good animal model for the study of atherosclerosis associated with high serum low-density lipoprotein (LDL) values. Two assays were developed to measure apolipoprotein B (apoB), the major structural and functional apolipoprotein of LDL in rabbits and to distinguish endogenous LDL in transgenic rabbits from that of human apoB.Two procedures, an electroimmunoassay (EIA) and an immunonephelometric assay (INA), along with a goat-origin rabbit antiserum were developed to measure serum apoB concentration in rabbits.Use of either assay resulted in ability to measure rabbit species-specific apoB concentration.These assays should have broad applications: to screen compounds or diets that might lower serum apoB concentrations; to specifically measure human apoB concentration in transgenic rabbits; to measure serum apoB concentration in rabbits overexpressing other human proteins.