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Sentinel-2 satellites provide dense image time series exhibiting high spectral, spatial and temporal resolutions. These images are in particular of utter interest for Land-Cover (LC) mapping at large scales. LC maps can now be computed on a yearly basis at the scale of a country with efficient supervised classifiers, assuming suitable training data are available. However, the efficient exploitation of large amount of Sentinel-2 imagery still remain challenging on unexplored areas where state-of-the-art classifiers are prone to fail. This paper focuses on Land-Cover mapping over Cameroon for the purpose of updating the Very High Resolution national topographic geodatabase. The ι2 framework is adopted and tested for the specificity of the country. Here, experiments focus on generic vegetation classes (five) which enables providing robust focusing masks for higher resolution classifications. Two strategies are compared: (i) a LC map is calculated out of a year long time series and (ii) monthly LC maps are generated and merged into a single yearly map. Satisfactory accuracy scores are obtained (>94% in Overall Accuracy), allowing to provide a first step towards finer-grained map retrieval.

In vivobioluminescence imaging facilitates the non-invasive visualization of biological processes in living animals. This system has been used to track virus infections mostly in mice and ferrets; however, until now this approach has not been applied to pathogens in avian species. To visualize the infection of an important avian pathogen, we generated Marek’s disease virus (MDV) recombinants expressing firefly luciferase during lytic replication. Upon characterization of the recombinant virusesin vitro, chickens were infected and the infection visualized in live animals over the course of 14 days. The luminescence signal was consistent with the known spatiotemporal kinetics of infection and the life cycle of MDV, and correlated well with the viral load measured by qPCR. Intriguingly, thisin vivobioimaging approach revealed two novel sites of MDV replication, the beak and the skin of the feet covered in scales. Feet skin infection was confirmed using a complementary fluorescence bioimaging approach with MDV recombinants expressing mRFP or GFP. Infection was detected in the intermediate epidermal layers of the feet skin that was also shown to produce infectious virus, regardless of the animals’ age at and the route of infection. Taken together, this study highlights the value ofin vivowhole body bioimaging in avian species by identifying previously overlooked sites of replication and shedding of MDV in the chicken host. &nbsp

As urbanisation accelerates, the urban landscape reshapes at a fast pace. Consequently, the urban built environment continuously evolves horizontally as well as vertically. However, more attention in the field of spatial analysis is given to horizontal dynamics, despite the importance of geoinformation in the vertical dimension. 3D modelling methods have gained popularity due to their powerful capability of capturing and analysing geoinformation in the vertical dimension and visualising objects lifelike in the urban built environment. Various urban applications with diverse 3D modelling methods at different research scales and purposes have emerged. However, there is no systematic overview of these different modelling methods. Therefore, it is imperative to provide an up-to-date review of these advances. In this paper, we aim to review urban 3D modelling methods widely used in the prior 5-year period (2015–2020). Our analysis focuses on five attributes, i.e., basic characteristics, data requirements, technical requirements, users requirements and ethical considerations. The discussion presents the current status of 3D modelling methods – a wide range of applications yet with substantial development potential. This paper closes with insights for future work regarding the necessities of 3D data structure support as well as interdisciplinary research, specifically for big data management and integration.

This paper presents our contribution to the development of a standardized 3D input data model for solar photovoltaic potential estimation. Presently, different input data and processing steps influence the calculation for estimating the potential of solar energy in the Netherlands. The variety in characteristics of input data and issues with temporal accuracy extracted from the national registers and databases makes it challenging to obtain a consistent and reliable result. To address this issue, we created a point cloud dataset that integrated from LiDAR point cloud and dense image matching which is complete, recent and positionally accurate. Furthermore, we made a 3D building model from the integrated point cloud and identified the effect of finer resolution in the photovoltaic potential analysis.

Deep learning methods based on Fully convolution networks (FCNs) have shown an impressive progress in building outline delineation from very high resolution (VHR) remote sensing (RS) imagery. Common issues still exist in extracting precise building shapes and outlines, often resulting in irregular edges and over smoothed corners. In this paper, we use PolyMapper, a recently introduced deep-learning framework that is able to predict object outlines in a vector representation directly. We have introduced two main modifications to this baseline method. First, we introduce EffcientNet as backbone feature encoder to our network, which uses compound coefficient to scale up all dimensions of depth/width/resolution uniformly, to improve the processing speed with fewer parameters. Second, we integrate a boundary refinement block (BRB) to strengthen the boundary feature learning and to further improve the accuracy of corner prediction. The results demonstrate that the end-to-end learnable model is capable of delineating polygons of building outlines that closely approximate the structure of reference labels. Experiments on the crowdAI building instance segmentation datasets show that our model outperforms PolyMapper in all COCO metrics, for instance showing a 0.13 higher mean Average Precision (AP) value and a 0.60 higher mean Average Recall value. Also qualitative results show that our method segments building instances of various shapes more accurately.

Semantic segmentation models are often affected by illumination changes, and fail to predict correct labels. Although there has been a lot of research on indoor semantic segmentation, it has not been studied in low-light environments. In this paper we propose a new framework, LISU, for Low-light Indoor Scene Understanding. We first decompose the low-light images into reflectance and illumination components, and then jointly learn reflectance restoration and semantic segmentation. To train and evaluate the proposed framework, we propose a new data set, namely LLRGBD, which consists of a large synthetic low-light indoor data set (LLRGBD-synthetic) and a small real data set (LLRGBD-real). The experimental results show that the illumination-invariant features effectively improve the performance of semantic segmentation. Compared with the baseline model, the mIoU of the proposed LISU framework has increased by 11.5%. In addition, pre-training on our synthetic data set increases the mIoU by 7.2%. Our data sets and models are available on our project website.

State-of-the-art object detection approaches such as Fast/Faster R-CNN, SSD, or YOLO have difficulties detecting dense, small targets with arbitrary orientation in large aerial images. The main reason is that using interpolation to align RoI features can result in a lack of accuracy or even loss of location information. We present the Local-aware Region Convolutional Neural Network (LR-CNN), a novel two-stage approach for vehicle detection in aerial imagery. We enhance translation invariance to detect dense vehicles and address the boundary quantization issue amongst dense vehicles by aggregating the high-precision RoIs' features. Moreover, we resample high-level semantic pooled features, making them regain location information from the features of a shallower convolutional block. This strengthens the local feature invariance for the resampled features and enables detecting vehicles in an arbitrary orientation. The local feature invariance enhances the learning ability of the focal loss function, and the focal loss further helps to focus on the hard examples. Taken together, our method better addresses the challenges of aerial imagery. We evaluate our approach on several challenging datasets (VEDAI, DOTA), demonstrating a significant improvement over state-of-the-art methods. We demonstrate the good generalization ability of our approach on the DLR 3K dataset., Comment: 8 pages

We propose a new approach for the automatic detection of network structures in raster data. The model for the network structure is represented by a graph whose nodes and edges correspond to junction-points and to connecting line segments, respectively; nodes and edges are further described by certain parameters. We embed this model in the probabilistic framework of marked point processes and determine the most probable configuration of objects by stochastic sampling. That is, different graph configurations are constructed randomly by modifying the graph entity parameters, by adding and removing nodes and edges to/ from the current graph configuration. Each configuration is then evaluated based on the probabilities of the changes and an energy function describing the conformity with a predefined model. By using the Reversible Jump Markov Chain Monte Carlo sampler, a global optimum of the energy function is determined. We apply our method to the detection of river and tidal channel networks in digital terrain models. In comparison to our previous work, we introduce constraints concerning the flow direction of water into the energy function. Our goal is to analyse the influence of different parameter settings on the results of network detection in both, synthetic and real data. Our results show the general potential of our method for the detection of river networks in different types of terrain.

This paper proposes a novel approach for linear feature detection. The contribution is twofold: a novel model for spatial point processes and a new method for linear feature detection. It describes a linear feature as a string of points, represents all features in an image as a configuration of a spatial point process, and formulates feature detection as finding the optimal configuration of a spatial point process. Further, a prior term is proposed to favor straight linear configurations, and a data term is constructed to superpose the points on linear features. The proposed approach extracts straight linear features in a global framework. The paper reports ongoing work. As demonstrated in preliminary experiments, globally optimal linear features can be detected. National Natural Science Foundation of China/41071263 National Natural Science Foundation of China/41571335 Zhejiang Provincial Natural Science Foundation of China/LY13D010003 Key Laboratory for National Geographic Census and Monitoring National Administration of Surveying, Mapping and Geoinformation/2014NGCM

We propose a novel hierarchical approach for the classification of airborne 3D lidar points. Spatial and semantic context is incorporated via a two-layer Conditional Random Field (CRF). The first layer operates on a point level and utilises higher order cliques. Segments are generated from the labelling obtained in this way. They are the entities of the second layer, which incorporates larger scale context. The classification result of the segments is introduced as an energy term for the next iteration of the point-based layer. This framework iterates and mutually propagates context to improve the classification results. Potentially wrong decisions can be revised at later stages. The output is a labelled point cloud as well as segments roughly corresponding to object instances. Moreover, we present two new contextual features for the segment classification: the distance and the orientation of a segment with respect to the closest road. It is shown that the classification benefits from these features. In our experiments the hierarchical framework improve the overall accuracies by 2.3% on a point-based level and by 3.0% on a segment-based level, respectively, compared to a purely point-based classification.

Online multi-person tracking in image sequences is commonly guided by recursive filters, whose predictive models define the expected positions of future states. When a predictive model deviates too much from the true motion of a pedestrian, which is often the case in crowded scenes due to unpredicted accelerations, the data association is prone to fail. In this paper we propose a novel predictive model on the basis of Gaussian Process Regression. The model takes into account the motion of every tracked pedestrian in the scene and the prediction is executed with respect to the velocities of all interrelated persons. As shown by the experiments, the model is capable of yielding more plausible predictions even in the presence of mutual occlusions or missing measurements. The approach is evaluated on a publicly available benchmark and outperforms other state-of-the-art trackers.

In this paper we describe learning of a descriptor based on the Siamese Convolutional Neural Network (CNN) architecture and evaluate our results on a standard patch comparison dataset. The descriptor learning architecture is composed of an input module, a Siamese CNN descriptor module and a cost computation module that is based on the L2 Norm. The cost function we use pulls the descriptors of matching patches close to each other in feature space while pushing the descriptors for non-matching pairs away from each other. Compared to related work, we optimize the training parameters by combining a moving average strategy for gradients and Nesterov's Accelerated Gradient. Experiments show that our learned descriptor reaches a good performance and achieves state-of-art results in terms of the false positive rate at a 95 % recall rate on standard benchmark datasets.

International audience; In this chapter we propose the use of a drought index based on a temporal series of NDVI MODIS images for the monitoring of drought in France during the last fifteen years at a spatial resolution of 250 m. The goal is to show how to track and map drought using QGIS free software. The areas of agricultural, urban and forest areas will be provided for drought-affected zones on an example that corresponds to the period of the heat wave that affected Europe in the summer of 2003 (first half of August).; Dans ce chapitre, nous proposons l'utilisation d'un indice de sécheresse fondé sur une série temporelle d'images de NDVI MODIS pour le suivi de la sécheresse en France durant ces dix-sept dernières années, à une résolution spatiale de 250 m. L'objectif est de montrer comment suivre et cartographier la sécheresse en utilisant le logiciel libre QGIS. Les surfaces des zones agricoles, urbaines et forestières seront fournies pour les zones touchées par la sécheresse sur un exemple qui correspond à la période de la canicule qui a touché l'Europe à l'été 2003 (première quinzaine d'août).

International audience; This chapter presents the features offered by the Orfeo ToolBox (OTB) for spatial information extraction from remote sensing images. It describes the general presentation of the OTB namely: history, content, access and internal mechanisms, but also how to get it working, and what the main interfaces are when using it. The chapter examines the problems that are frequently encountered in remote sensing image processing: what applications can address a given problem, and how to use them. The OTB applications aim to provide users with a number of implemented processes. The chapter also presents the use of OTB applications typically encountered in remote sensing, from image preprocessing to retrieval of information. It introduces some preprocessing tools needed to produce data that can be used in a spatial context from raw remote sensing images. The chapter also describes some OTB applications for extracting information from the preprocessed images.; Utilisation des applications de l'Orféo Toolbox pour traiter les images satellitaires.

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.

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.

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.

© 2017 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY license Background Continuous electronic fetal heart-rate monitoring is widely used during labour, and computerised interpretation could increase its usefulness. We aimed to establish whether the addition of decision-support software to assist in the interpretation of cardiotocographs affected the number of poor neonatal outcomes. Methods In this unmasked randomised controlled trial, we recruited women in labour aged 16 years or older having continuous electronic fetal monitoring, with a singleton or twin pregnancy, and at 35 weeks' gestation or more at 24 maternity units in the UK and Ireland. They were randomly assigned (1:1) to decision support with the INFANT system or no decision support via a computer-generated stratified block randomisation schedule. The primary outcomes were poor neonatal outcome (intrapartum stillbirth or early neonatal death excluding lethal congenital anomalies, or neonatal encephalopathy, admission to the neonatal unit within 24 h for ≥48 h with evidence of feeding difficulties, respiratory illness, or encephalopathy with evidence of compromise at birth), and developmental assessment at age 2 years in a subset of surviving children. Analyses were done by intention to treat. This trial is completed and is registered with the ISRCTN Registry, number 98680152. Findings Between Jan 6, 2010, and Aug 31, 2013, 47 062 women were randomly assigned (23 515 in the decision-support group and 23 547 in the no-decision-support group) and 46 042 were analysed (22 987 in the decision-support group and 23 055 in the no-decision-support group). We noted no difference in the incidence of poor neonatal outcome between the groups—172 (0·7%) babies in the decision-support group compared with 171 (0·7%) babies in the no-decision-support group (adjusted risk ratio 1·01, 95% CI 0·82–1·25). At 2 years, no significant differences were noted in terms of developmental assessment. Interpretation Use of computerised interpretation of cardiotocographs in women who have continuous electronic fetal monitoring in labour does not improve clinical outcomes for mothers or babies. Funding National Institute for Health Research.

International audience; In the framework of the Chemistry-Aerosol Mediterranean Experiment (ChArMEx; http://charmex.lsce.ipsl.fr/) initiative, a field campaign took place in the western Mediterranean Basin between 10 June and 5 July 2013 within the ADRIMED (Aerosol Direct Radiative Impact on the regional climate in the MEDiterranean region) project. The scientific objectives of ADRIMED are the characterization of the most common ‘Mediterranean aerosols’ and their direct radiative forcing (column closure and regional scale). During 15–24 June a multi-intrusion dust event took place over the western and central Mediterranean Basin. Extra measurements were carried out by some EARLINET/ACTRIS (European Aerosol Research Lidar Network /Aerosols, Clouds, and Trace gases Research InfraStructure Network, http://www.actris.net/) lidar stations in Spain and Italy, in particular on 22 June in support to the flight over southern Italy of the Falcon 20 aircraft involved in the campaign. This article describes the physical and optical properties of dust observed at the different lidar stations in terms of dust plume centre of mass, optical depth, lidar ratio, and particle depolarization ratio. To link the differences found in the origin of dust plumes, the results are discussed on the basis of back-trajectories and air- and space-borne lidars. This work puts forward the collaboration between a European research infrastructure (ACTRIS) and an international project (ChArMEx) on topics of interest for both parties, and more generally for the atmospheric community.

Matching and fusion of 3D point clouds, such as close range laser scans, is important for creating an integrated 3D model data infrastructure. The Iterative Closest Point algorithm for alignment of point clouds is one of the most commonly used algorithms for matching of rigid bodies. Evidently, scans are acquired from different positions and might present different data characterization and accuracies, forcing complex data-handling issues. The growing demand for near real-time applications also introduces new computational requirements and constraints into such processes. This research proposes a methodology to solving the computational and processing complexities in the ICP algorithm by introducing specific performance enhancements to enable more efficient analysis and processing. An Octree data structure together with the caching of localized Delaunay triangulation-based surface meshes is implemented to increase computation efficiency and handling of data. Parallelization of the ICP process is carried out by using the Single Instruction, Multiple Data processing scheme – based on the Divide and Conquer multi-branched paradigm – enabling multiple processing elements to be performed on the same operation on multiple data independently and simultaneously. When compared to the traditional non-parallel list processing the Octree-based SIMD strategy showed a sharp increase in computation performance and efficiency, together with a reliable and accurate alignment of large 3D point clouds, contributing to a qualitative and efficient application.

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.

International audience; The propagation of signals through the atmosphere plays a major role in the quality of communications between ground terminals and satellites. Its characteristics have to be known accurately for appropriate communications equipment to be selected. In the band of frequencies used by operators in the future generation of satellites (beyond 20 GHz), the quality of transmission is especially affected by the attenuation of received signals because of rain, and by other less significant but much more frequent effects due to atmospheric gases, and nonprecipitating water. These phenomena have a direct impact on the availability ratio of a link between a ground terminal and a satellite. Our main goal in this study is to measure the atmospheric attenuation, using dual-frequency ground-based radiometers measuring the sky radiation at different pointing directions, so as to perform a statistical study. A new algorithm, based on a neural approach, is thus developed for estimating atmospheric attenuation, in various meteorological conditions, for several elevation angles and for frequencies between 20 and 50 GHz, from dual-frequency radiometric measurements. A validation of the obtained algorithm is performed on Olympus experimental data for the 20 and 30 GHz channels. At the end of this paper some applications are then presented to underline the usefulness of this new algorithm. The applicability of the algorithm to satellite beacon calibration in Ka or Q band with accuracy of 0.1 dB is shown. Preliminary joint statistics between attenuation at various pointing directions obtained at 40 GHz show what improvement can be expected from satellite diversity in the case of satellite constellations.

Advances in unmanned aerial vehicles (UAV) and especially micro aerial vehicle (MAV) technology together with increasing quality and decreasing price of imaging devices have resulted in growing use of MAVs in photogrammetry. The practicality of MAV mapping is seriously enhanced with the ability to determine parameters of exterior orientation (EO) with sufficient accuracy, in both absolute and relative senses (change of attitude between successive images). While differential carrier phase GNSS satisfies cm-level positioning accuracy, precise attitude determination is essential for both direct sensor orientation (DiSO) and integrated sensor orientation (ISO) in corridor mapping or in block configuration imaging over surfaces with low texture. Limited cost, size, and weight of MAVs represent limitations on quality of onboard navigation sensors and puts emphasis on exploiting full capacity of available resources. Typically short flying times (10-30 minutes) also limit the possibility of estimating and/or correcting factors such as sensor misalignment and poor attitude initialization of inertial navigation system (INS). This research aims at increasing the accuracy of attitude determination in both absolute and relative senses with no extra sensors onboard. In comparison to classical INS/GNSS setup, novel approach is presented here to integrated state estimation, in which vehicle dynamic model (VDM) is used as the main process model. Such system benefits from available information from autopilot and physical properties of the platform in enhancing performance of determination of trajectory and parameters of exterior orientation consequently. The navigation system employs a differential carrier phase GNSS receiver and a micro electro-mechanical system (MEMS) grade inertial measurement unit (IMU), together with MAV control input from autopilot. Monte-Carlo simulation has been performed on trajectories for typical corridor mapping and block imaging. Results reveal considerable reduction in attitude errors with respect to conventional INS/GNSS system, in both absolute and relative senses. This eventually translates into higher redundancy and accuracy for photogrammetry applications.

This paper compares two generic approaches for the reconstruction of buildings. Synthesized and real oblique and vertical aerial imagery is transformed on the one hand into a dense photogrammetric 3D point cloud and on the other hand into photogrammetric 2.5D surface models depicting a scene from different cardinal directions. One approach evaluates the 3D point cloud statistically in order to extract the hull of structures, while the other approach makes use of salient line segments in 2.5D surface models, so that the hull of 3D structures can be recovered. With orders of magnitudes more analyzed 3D points, the point cloud based approach is an order of magnitude more accurate for the synthetic dataset compared to the lower dimensioned, but therefor orders of magnitude faster, image processing based approach. For real world data the difference in accuracy between both approaches is not significant anymore. In both cases the reconstructed polyhedra supply information about their inherent semantic and can be used for subsequent and more differentiated semantic annotations through exploitation of texture information.

This study highlights the benefits of precise aerial position and attitude control in the context of mapping with Micro Aerial Vehicles (MAVs). Accurate mapping with MAVs is gaining importance in applications such as corridor mapping, road and pipeline inspections or mapping of large areas with homogeneous surface structure, e.g. forests or agricultural fields. There, accurate aerial control plays a major role in successful terrain reconstruction and artifact-free ortophoto generation. The presented experiments focus on new approaches of aerial control. We confirm practically that the relative aerial position and attitude control can improve accuracy in difficult mapping scenarios. Indeed, the relative orientation method represents an attractive alternative in the context of MAVs for two reasons. First, the procedure is somewhat simplified, e.g. the angular misalignment, so called boresight, between the camera and the inertial measurement unit (IMU) does not have to be determined and, second, the effect of possible systematic errors in satellite positioning (e.g. due to multipath and/or incorrect recovery of differential carrier-phase ambiguities) is mitigated. First, we present a typical mapping project over an agricultural field and second, we perform a corridor road mapping. We evaluate the proposed methods in scenarios with and without automated image observations. We investigate a recently proposed concept where adjustment is performed using image observations limited to ground control and check points, so called fast aerial triangulation (Fast AT). In this context we show that accurate aerial control (absolute or relative) together with a few image observations can deliver accurate results comparable to classical aerial triangulation with thousands of image measurements. This procedure in turns reduces the demands on processing time and the requirements on the existence of surface texture. Finally, we compare the above mentioned procedures with direct sensor orientation (DiSO) to show its potential for rapid mapping.

We describe an effective and efficient method for point-wise semantic classification of 3D point clouds. The method can handle unstructured and inhomogeneous point clouds such as those derived from static terrestrial LiDAR or photogammetric reconstruction; and it is computationally efficient, making it possible to process point clouds with many millions of points in a matter of minutes. The key issue, both to cope with strong variations in point density and to bring down computation time, turns out to be careful handling of neighborhood relations. By choosing appropriate definitions of a point’s (multi-scale) neighborhood, we obtain a feature set that is both expressive and fast to compute. We evaluate our classification method both on benchmark data from a mobile mapping platform and on a variety of large, terrestrial laser scans with greatly varying point density. The proposed feature set outperforms the state of the art with respect to per-point classification accuracy, while at the same time being much faster to compute., ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, III-3, ISSN:2194-9042, ISSN:2194-9050

International audience; Deux extensions de QGIS pour des applications en santé : GeoHealth et QuickOSM 7.1. Contexte de l'utilisation des SIG en santé et du développement d'extensions dédiées à la santé dans QGIS Les cartes et les analyses spatiales sont de plus en plus utilisées dans le domaine de la santé pour illustrer l'organisation spatio-temporelle des données et permettre un regard géographique sur les données sanitaires. Les systèmes d'information géographique (SIG), et plus généralement les outils de la géomatique, sont ainsi utilisés par les organisations de santé (locales ou internationales) pour informer sur la distribution des maladies, par les gestionnaires pour suivre et contrôler les épidémies, et par les médecins et les chercheurs pour comprendre les distributions des maladies. Les cartes et les analyses spatiales sont aujourd'hui régulièrement utilisées dans les publications scientifiques du domaine de la santé. Les grandes organisations comme l'Organisation mondiale de la santé, les ministères de la Santé de nombreux pays, des organismes de recherche, comme les Instituts Pasteur, se sont dotés de « cellules SIG » avec des géomaticiens ou des géographes de la santé pour réaliser ces tâches. Toutefois, beaucoup d'organismes n'ont pas les compétences en interne et souhaitent tout de même réaliser de telles analyses. De plus en plus de personnes du domaine de la santé se forment ainsi à la géomatique, mais n'en faisant usage que ponctuellement et avec des outils complexes, la mise en pratique reste concrètement difficile. Chapitre rédigé par Vincent HERBRETEAU, Christophe RÉVILLION et Étienne TRIMAILLE.