Gestion durable : notions et conséquences sur les pratiques.

Depuis le premier Sommet de la Terre tenu il y a dix ans à Rio de Janeiro, la notion de développement durable connaît un succès croissant au point de constituer aujourd'hui un véritable leitmotiv pour une majorité d'acteurs et de secteurs d'activités. Le domaine forestier s'est avéré particulièrement réceptif à l'idée de durabilité, générant une grande diversité de processus, d'initiatives et d'approches. Cet article aborde les différentes questions suivantes : - Il s'agit en premier lieu de donner des clefs de lecture pour se repérer dans la pluralité des conceptions à l' uvre en matière de gestion durable des forêts. - Par confrontation avec le cadre de réflexion précédent, quelques-uns des grands défis auxquels sont confrontés les forêts méditerranéennes permettent de révéler des aspects et des dimensions de la gestion durable trop souvent éludés. - S'il importe de donner un second souffle aujourd'hui à la gestion durable et d 'élaborer des approches de seconde génération, sur quels fondements convient-il de les faire reposer

Joint 3D estimation of vehicles and scene flow

driving. While much progress has been made in recent years, imaging conditions in natural outdoor environments are still very challenging for current reconstruction and recognition methods. In this paper, we propose a novel unified approach which reasons jointly about 3D scene flow as well as the pose, shape and motion of vehicles in the scene. Towards this goal, we incorporate a deformable CAD model into a slanted-plane conditional random field for scene flow estimation and enforce shape consistency between the rendered 3D models and the parameters of all superpixels in the image. The association of superpixels to objects is established by an index variable which implicitly enables model selection. We evaluate our approach on the challenging KITTI scene flow dataset in terms of object and scene flow estimation. Our results provide a prove of concept and demonstrate the usefulness of our method. © 2015 Copernicus GmbH. All Rights Reserved

Global and local sparse subspace optimization for motion segmentation

In this paper, we propose a new framework for segmenting feature-based moving objects under affine subspace model. Since the feature trajectories in practice are high-dimensional and contain a lot of noise, we firstly apply the sparse PCA to represent the original trajectories with a low-dimensional global subspace, which consists of the orthogonal sparse principal vectors. Subsequently, the local subspace separation will be achieved via automatically searching the sparse representation of the nearest neighbors for each projected data. In order to refine the local subspace estimation result, we propose an error estimation to encourage the projected data that span a same local subspace to be clustered together. In the end, the segmentation of different motions is achieved through the spectral clustering on an affinity matrix, which is constructed with both the error estimation and sparse neighbors optimization. We test our method extensively and compare it with state-of-the-art methods on the Hopkins 155 dataset. The results show that our method is comparable with the other motion segmentation methods, and in many cases exceed them in terms of precision and computation time

An iterative inference procedure applying conditional random fields for simultaneous classification of land cover and land use

Land cover and land use exhibit strong contextual dependencies. We propose a novel approach for the simultaneous classification of land cover and land use, where semantic and spatial context is considered. The image sites for land cover and land use classification form a hierarchy consisting of two layers: a land cover layer and a land use layer. We apply Conditional Random Fields (CRF) at both layers. The layers differ with respect to the image entities corresponding to the nodes, the employed features and the classes to be distinguished. In the land cover layer, the nodes represent super-pixels; in the land use layer, the nodes correspond to objects from a geospatial database. Both CRFs model spatial dependencies between neighbouring image sites. The complex semantic relations between land cover and land use are integrated in the classification process by using contextual features. We propose a new iterative inference procedure for the simultaneous classification of land cover and land use, in which the two classification tasks mutually influence each other. This helps to improve the classification accuracy for certain classes. The main idea of this approach is that semantic context helps to refine the class predictions, which, in turn, leads to more expressive context information. Thus, potentially wrong decisions can be reversed at later stages. The approach is designed for input data based on aerial images. Experiments are carried out on a test site to evaluate the performance of the proposed method. We show the effectiveness of the iterative inference procedure and demonstrate that a smaller size of the super-pixels has a positive influence on the classification result

Gaussian process for activity modeling and anomaly detection

Complex activity modeling and identification of anomaly is one of the most interesting and desired capabilities for automated video behavior analysis. A number of different approaches have been proposed in the past to tackle this problem. There are two main challenges for activity modeling and anomaly detection: 1) most existing approaches require sufficient data and supervision for learning; 2) the most interesting abnormal activities arise rarely and are ambiguous among typical activities, i.e. hard to be precisely defined. In this paper, we propose a novel approach to model complex activities and detect anomalies by using non-parametric Gaussian Process (GP) models in a crowded and complicated traffic scene. In comparison with parametric models such as HMM, GP models are nonparametric and have their advantages. Our GP models exploit implicit spatial-temporal dependence among local activity patterns. The learned GP regression models give a probabilistic prediction of regional activities at next time interval based on observations at present. An anomaly will be detected by comparing the actual observations with the prediction at real time. We verify the effectiveness and robustness of the proposed model on the QMUL Junction Dataset. Furthermore, we provide a publicly available manually labeled ground truth of this data set

Assessing is not the same thing rs manrging - considerations that undermine the power of criteria and indicrtors*

Dans le domaine forestier, le succès mondial de l'approche "critères et indicateurs" s'explique par la volonté de définir précisément la notion de gestion durable. Recevable en théorie, ce projet s'avère contestable en pratique, car il s'apparente à la quête d'un modèle de forêt idéale, censé satisfaire, partout et toujours, aux différentes "fonctions forestières". Portée par le courant de la certification, cette approche dominante aboutit ainsi à un paradoxe étonnant : personne ne sait vraiment dire ce qu'est la gestion durable, mais tout le monde sait l'évaluer. La confrontation avec la réalité du terrain, à partir d'un travail réalisé dans le territoire de Belledonne-Grésivaudan, permet de mieux comprendre en quoi les critères et les indicateurs s'avèrent très insuffisants pour assurer une meilleure prise en charge des problèmes auxquels sont confrontés les acteurs liés à la forêt et au bois

Modélisation 3D de bâtiments (reconstruction automatique de superstructures de toits et recalage cinétique de toits polyédriques prenant en compte la topologie)

Il existe aujourd'hui une demande croissante pour des modèles numériques de ville de plus en plus précis. Alors que les travaux récents ont permis la production robuste de modèles polyédriques de bâtiments, les superstructures de toits telles que les cheminées et les chiens assis ne sont pas modélisées, et les erreurs géométriques et topologiques peuvent être importantes. L'approche itérative proposée affine géométriquement et sémantiquement un modèle de bâtiment approché sans superstructures, à l'aide d'un Modèle Numérique de Surface (MNS). Elle alterne la reconstruction de superstructures et le recalage des pans de toit principaux. La détection et la reconstruction de superstructures sont basées sur une bibliothèque de modèles paramétriques de superstructures. Un ensemble de superstructures disjointes est recherché, en se réduisant au problème de recherche d'une clique pondérée maximale. La phase de recalage tire parti des superstructures précédemment détectées afin de mieux estimer les pans de toit principaux. Elle corrige des simplifications tant géométriques telles qu'une symétrie erronée des toits, que topologiques telles que la fusion de sommets proches. Nous utilisons une représentation géométrique des bâtiments par les plans porteurs de chaque facette polyédrique, plus intuitive dans ce contexte que la représentation habituelle par la position de ses sommets. Nous introduisons le problème de triédralisation qui scinde les sommets surcontraints en sommets bien définis à l'intersection de 3 plans seulement. Nous proposons une structure de donnée cinétique garantissant des facettes non auto-intersectantes au cours de la réestimation itérative de leurs plans porteurs.There is nowadays a growing demand for increasingly more accurate 3D city models. Whereas recent works have lead to the robust generation of polyhedral building models, they do not model roof superstructures such as chimneys or dormer windows, and may feature large geometric and topological errors. We propose an approach to refine geometrically and semantically a superstructure-less approximate building model, using a Digital Surface Model (DSM). This iterative approach alternates between superstructure reconstructions and geometric fitting of the main roof planes. Superstructure detection and reconstruction are based on a library of parametric superstructure models. A set of disjoint superstructures is searched to explain the height differences between the DSM and the building model, reducing the search to a maximum weighted clique problem. The fitting step uses the previously detected superstructures to refine the main roof plane estimations. It corrects both geometric simplifications such as an erroneous roof symmetry, and topological simplifications such as the merging of close vertices of the polyhedral building model. The proposed representation of the building geometry uses the planes supporting each polyhedral facet, which is more intuitive in this context than the usual representation using the vertex locations. We introduce the trihedralization problem of splitting vertices that become over-constrained after updating their adjacent facet supports into well-defined vertices at the intersection of 3 planes. We propose a novel kinetic data structure that prevents facet self-intersections throughout the iterative reestimation of their supporting planes.PARIS-Télécom ParisTech (751132302) / SudocSudocFranceF

Detection, segmentation and localization of individual trees from mms point cloud data

International audienc

Some Maverick Considerations on Forest Certification

Au cours de la dernière décennie, le mécanisme de certification a considérablement influencé le débat sur la gestion forestière durable en l'immergeant dans la sphère marchande. Il s'est imposé aussi rapidement du fait que trois grandes conditions se sont trouvées réunies. La première est liée au changement radical de posture stratégique des grandes organisations de conservation de la nature, qui ne jouent plus désormais contre le commerce mais avec le marché. La seconde a pour origine une extension des composantes de la qualité totale des produits à des dimensions toujours plus détachées du produit lui-même. La dernière réside dans la réduction de la problématique de la gestion durable à une approche par critères et niveaux de performance qui s'apparente à une gestion plus "écologiquement correcte" que "durable". Finalement, ces "propos indiscrets" donnent lieu à une mise en perspective originale de certaines questions clefs liées à la certification forestière.Over the last decade, certification has greatly influenced discussions on sustainable forest management and driven it into the realm of commodities. The concomitance of three major conditions was instrumental in rapidly accrediting the principle of certification; one derives from the radical change in the strategic stance of major nature conservation organisations which are now no longer working against but with the market. The second is rooted in the tendency to extend the constituents of total quality in products to areas that are increasingly unattached to the product itse lf. The last arises from the reduction of the whole issue of sustainable management to a criteria- and performance-based approach, which resembles "ecologically correct" management more than it does sustainable management. Finally, these maverick considerations set a new perspective on some of the key questions relating to forest certification

Multi-mode TFM imaging with artifacts filtering using CIVA UT forwards models

Conference of 40th Annual Review of Progress in Quantitative Nondestructive Evaluation, QNDE 2013, Incorporating the 10th International Conference on Barkhausen and Micro-Magnetics, ICBM 2013 ; Conference Date: 21 July 2013 Through 26 July 2013; Conference Code:105840International audienceTFM (Total Focusing Method) is an advanced post-processing imaging algorithm of ultrasonic array data that shows great potential in defect detection and characterization. This technique can be performed using several propagation modes (direct or over skip imaging) and several types of waves (longitudinal or transverse) allowing the imaging of extended defects of complex geometry. However, non physical indications can be observed, leading to misinterpretation. These imaging artifacts are due to the coexistence of several contributions involving several mode of propagation and interactions with possible defects and / or the geometry of the part. In several configurations, a simple time of flight criterion is not sufficient for their identification. This paper presents tools based on the forward CIVA UT models which allow to analyze and to filter these artifacts, without any tuning parameters. The performances achieved are compared to those of conventional TFM on simulated and experimental data