Change detection needs change information: improving deep 3D point cloud change detection

Change detection is an important task that rapidly identifies modified areas, particularly when multi-temporal data are concerned. In landscapes with a complex geometry (e.g., urban environment), vertical information is a very useful source of knowledge that highlights changes and classifies them into different categories. In this study, we focus on change segmentation using raw three-dimensional (3D) point clouds (PCs) directly to avoid any information loss due to the rasterization processes. While deep learning has recently proven its effectiveness for this particular task by encoding the information through Siamese networks, we investigate herein the idea of also using change information in the early steps of deep networks. To do this, we first propose to provide a Siamese KPConv state-of-the-art (SoTA) network with hand-crafted features, especially a change-related one, which improves the mean of the Intersection over Union (IoU) over the classes of change by 4.70%. Considering that a major improvement is obtained due to the change-related feature, we then propose three new architectures to address 3D PC change segmentation: OneConvFusion, Triplet KPConv, and Encoder Fusion SiamKPConv. All these networks consider the change information in the early steps and outperform the SoTA methods. In particular, Encoder Fusion SiamKPConv overtakes the SoTA approaches by more than 5% of the mean of the IoU over the classes of change, emphasizing the value of having the network focus on change information for the change detection task. The code is available at https://github.com/IdeGelis/torch-points3d-SiamKPConvVariants.Comment: This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessibl

Principes de méthodes " non classiques, non statistiques et massivement multivariées " et de réduction de la complexité. Applications en épidémiologie sociale et en médecine légale

Social epidemiology and clinical legal medicine are hybrid objects that articulate several fields, accounting for social and interpersonal relationships. The complexity that characterizes them both is investigated through different viewpoints, scales and dimensions: the individual scale, the group scale and the society scale. The techniques used in biomedicine are not designed to properly deal with such a complexity, in a non-normative way. A wide range of alternative non-statistical, “non-classical” methods exist that can process simultaneously various dimensions so that we can reduce the apparent complexity of data while discovering scientific objects. Here, we present the principles and the use of clustering techniques, applied to social epidemiology. We applied different clustering techniques on data from the SIRS cohort to build a typology of healthcare utilization in the Paris metropolitan area. From an epistemological and technical viewpoint, we explain why these methods should take place beside other recognized but limited techniques such as randomized controlled trials. We introduce another but complementary kind of complexity reduction technique. The concept of intrinsic dimension is explained – the littlest dimension needed to describe properly data – and nonlinear dimensionality reduction techniques are applied in clinical legal medicine. With these tools, we explore whether the integration of multiple information sources is relevant in age estimation of living migrants. Finally, we discuss the pros and cons of these methods, as well as the opportunities they may create for both fields of social epidemiology and clinical legal medicine.La complexité qui traverse l'épidémiologie sociale et la médecine légale du vivant est de celle que l'on cherche à saisir par la variété des observations et par l'intrication de points de vue et d'échelles différentes - l'individu, le groupe, la société. Les méthodes du biomédical sont encore peu adaptées au traitement de la complexité, à sa représentation qui ne soit pas normative, statistique. Il existe un ensemble d'approches non statistiques, " non classiques ", qui puissent traiter simultanément un grand nombre de dimensions et qui permettent de réduire la complexité apparente en dégageant des objets d'étude spécifique. Nous présentons ici les principes et l'utilisation des techniques de reconnaissance de forme dans le cadre de l'épidémiologie sociale, en les appliquant à la recherche d'une typologie de recours aux soins, sur la base des données de la cohorte SIRS. Nous expliquons en quoi ces approches ont leur place, épistémologiquement et techniquement parlant, aux côtés des méthodes expérimentales classiques type essais randomisés contrôlés. Nous exposons également un autre moyen de réduire la complexité des données, tout en en préservant les qualités topologiques. Nous introduisons en médecine légale la notion de dimension intrinsèque, plus petite dimension nécessaire et suffisante à la description des données, et de techniques non linéaires de réduction de la dimension. Nous en appliquons les principes au cas de l'intégration de sources d'information multiples pour l'estimation de l'âge chez les adolescents migrants. Enfin, nous discutons les avantages et limites de ces approches ainsi que les perspectives qu'elles ouvrent à ces deux disciplines complémentaires

PerTurbo manifold learning algorithm for weakly labelled hyperspectral image classification

International audienceHyperspectral data analysis has been given a growing attention due to the scientific challenges it raises and the wide set of applications that can benefit from it. Classification of hyperspectral images has been identified as one of the hottest topics in this context, and has been mainly addressed by discriminative methods such as SVM. In this paper, we argue that generative methods, and especially those based on manifold representation of classes in the hyperspectral space, are relevant alternatives to SVM. To illustrate our point, we focus on the recently published PerTurbo algorithm and benchmark against SVM this generative manifold learning algorithm in the context of hyperspectral image classification. This choice is motivated by the fact that PerTurbo is fitted with numerous interesting properties, such as low sensitivity to dimensionality curse, high accuracy in weakly labelled images classification context (few training samples), straightforward extension to on-line setting, and interpretability for the practitioner. The promising results call for an up-to-date interest toward generative algorithms for hyperspectral image classification

Validation of HelioClim-3 version 4, HelioClim-3 version 5 and MACC-RAD using 14 BSRN stations

International audienceThis communication presents the results of a comparison of three satellite-derived databases covering Africa, Europe, Middle East and part of South America, against corresponding 15 min irradiations of very high quality measured by fourteen Baseline Surface Radiation Network (BSRN) stations. The three databases are accessible via the SoDa Service website, and are the two latest versions of HelioClim-3: versions 4 (HC3v4) and 5 (HC3v5), and the MACC-RAD database. The comparison was performed for durations of 15 min, 1 h, 1 day and 1 month for both the global irradiation received on a horizontal surface (GHI) and the direct irradiation received on a plane normal to sun rays (DNI). It is found that the three satellite-derived radiation databases exhibit satisfactory performances. For most of the fourteen locations, HC3v5 surpasses HC3v4 and MACC-RAD, with a bias ranging from-4 to 5% for the GHI and for all tested duration. The correlation coefficient is large for all databases and most often greater than 0.92 for 15 min and 0.98 for daily irradiation for GHI. The RMSE is fairly constant for all locations for 15 min and is approximately 20 kWh m-2 –slightly greater for MACC-RAD.-For daily irradiation, it ranges between 300 and 400 kWh m-2 for HC3v5, 300 and 500 kWh m-2 for HC3v4, and 400 and 550 kWh m-2 for MACC-RAD. Bias for the DNI is larger in absolute values than for GHI for all databases:-12 to 10% for HC3v5. The correlation coefficient is most often greater than 0.68 for 15 min and 0.84 for daily irradiation. The RMSE for 15 min ranges between 46 and 60 kWh m-2 for HC3v5, 46 and 63 kWh m-2 for HC3v4, and 48 and 66 kWh m-2 for MACC-RAD. For daily irradiation, it ranges between 1100 and 1600 kWh m-2 for HC3v5, between 1300 and 1700 kWh m-2 for HC3v4, and between 1000 and 1850 kWh m-2 for MACC-RAD. The MACC-RAD resource show promises provided the model for cloud properties is improved

Excavations at KIS-008, Buldir Island: Evaluation and Potential

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Uncertainties in solar electricity yield prediction from fluctuation of solar radiation

We have analyzed the variability of solar radiation in the Mediterranean and Black Sea regions by comparing yearly and monthly averages to long-term average values calculated from the HelioClim-1 database. Daily sums of global horizontal irradiation are considered for 18 years in the period 1985-2004. Standard deviation of yearly sums of global horizontal irradiation shows low interannual variability, being mostly in the range of 4% to 6%. While in arid climate of Northern Africa, Middle East, and Southern Europe standard deviation goes below 4%, values up to 10% are identified along coasts and in mountains. In the least sunny year out of 18, the solar resource was generally never more than 9% below the long-term average, and only in a few regions the radiation deficit reached 15%. The most stable weather is found in summer with standard deviation in June below 12%. The least stable season is winter, with variability higher then 20% in December, and regionally going above 35%. The solar resource has distinctive time and geographical patterns that might affect financing of large photovoltaic systems, as well as management of the distributed electricity generation

CORESHINE : a tracer of grain growth in dark clouds

Scattering by dust grains in the interstellar medium is a well-known phenomenon in the optical and near-infrared domains. We serendipitously discovered the effect of scattering in the mid-infrared in the dark cloud L183, and nicknamed the effect "coreshine". We investigated over 200 sources from both the Spitzer Archive and a new warm Spitzer mission program to check the frequency of the phenomenon and found over 50% of the cases to be positive, which is possibly only a lower limit. We see differences depending on the Galactic regions we investigate. Taurus is a highly successful target while the Galactic plane is too bright to let coreshine appear in emission. We present coreshine as a large grain tracer and we discuss its absence in the Gum/Vela region, which would indicate that big grains have been recently destroyed by the supernova blast wave. Finally, we discuss the prospect for future coreshine searches from archives, present and future instruments

Filtrage et classification de nuage de points sur la base d'attributs morphologiques

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New Prospects for Research on Manipulation of Insect Vectors by Pathogens

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