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The generation of photogrammetric point clouds from satellite images is often based on image correlation techniques. Correlation errors can arise for a wide variety of reasons: transient objects, homogeneous areas, shadows, and surface discontinuities. Therefore, a simple 3D Gaussian distribution at the point cloud level is not an appropriate model. In this paper, we propose a new point cloud denoising method integrated into the Multiview Stereo Pipeline CARS, dedicated to satellite imagery. Building upon bilateral filtering principles, our approach introduces a novel utilization of color information, confidence estimation and geometric constraints alongside point positions and normals. While the use of point color increases the level of detail, the addition of geometric constraints and confidence awareness guides processing towards a realistic solution. We propose an ablation study and compare our solution against a previously established bilateral filter with LiDAR data as ground truth.

ABSTRACT The limited temporal completeness and taxonomic accuracy of species lists, made available in a traditional manner in scientific publications, has always represented a problem. These lists are invariably limited to a few taxonomic groups and do not represent up-to-date knowledge of all species and classifications. In this context, the Brazilian megadiverse fauna is no exception, and the Catálogo Taxonômico da Fauna do Brasil (CTFB) (http://fauna.jbrj.gov.br/), made public in 2015, represents a database on biodiversity anchored on a list of valid and expertly recognized scientific names of animals in Brazil. The CTFB is updated in near real time by a team of more than 800 specialists. By January 1, 2024, the CTFB compiled 133,691 nominal species, with 125,138 that were considered valid. Most of the valid species were arthropods (82.3%, with more than 102,000 species) and chordates (7.69%, with over 11,000 species). These taxa were followed by a cluster composed of Mollusca (3,567 species), Platyhelminthes (2,292 species), Annelida (1,833 species), and Nematoda (1,447 species). All remaining groups had less than 1,000 species reported in Brazil, with Cnidaria (831 species), Porifera (628 species), Rotifera (606 species), and Bryozoa (520 species) representing those with more than 500 species. Analysis of the CTFB database can facilitate and direct efforts towards the discovery of new species in Brazil, but it is also fundamental in providing the best available list of valid nominal species to users, including those in science, health, conservation efforts, and any initiative involving animals. The importance of the CTFB is evidenced by the elevated number of citations in the scientific literature in diverse areas of biology, law, anthropology, education, forensic science, and veterinary science, among others.

Abstract A Collapsible Light Trap (CLT) for collecting insects, particularly aquatic insects, is described here. CLT is a modified Pennsylvania Light Trap with the advantage of being collapsible and lightweight to be carried in a small backpack and very easy to set up in the field. CLT is equipped with LED light strip wrapped around a PVC tube and can be connected to a regular 12 V / 7 Ah battery, running for more than 48 uninterrupted hours. Complete CLT weighs 0.8-1.0 kg, depending on the metal used, and the battery weighs around 2 kg, being easily transportable to more remote collecting areas. Over the years, CLTs have been used for collecting and describing the diversity of aquatic insects from Brazil, particularly caddisflies. Depending on the locality, only one trap for one night can collect over a thousand insect specimens and more than 200 individuals of caddisflies.

Urinothorax is a rare cause of pleural effusion. Infected urinothorax is even rarer. Here we present a case of infected urinothorax from renal mass causing obstructive uropathy. Patient improved with pleural drainage and a multidisciplinary approach of treatment between team involving urologist and pulmonologist. This case highlights the complexity in the diagnosis and management of infected urinothorax.

The photogrammetric 3D stereo reconstruction from pairs of strereo images is rising interest in the past few years in space field downstream. Nowadays, it is conceivable that a large production of DSMs from satellite images can become the primary source of 3D information on a global scale. However, in urban areas, DSMs produced with current technology suffer from poor quality. Indeed, even using very high resolution (VHR) images, there is too little information to generate disparity maps that reproduce very well defined shaped objects such as buildings.To address this issue, one solution may be to artificially increase image resolution beyond the sensor limits. Super resolution (SR) algorithms are designed to recover high frequencies, introducing significant information in a scene characterized by strong and frequent discontinuities such as a city. State-of-the-art methods relying on Deep Learning have shown remarkable results in this sense. The aim of this work is therefore to assess the contribution of single image SR Deep Learning techniques to the stereo matching and DSMs generation in an urban context, highlighting potential advantages and limitations that can show up when introducing such a technology in a multi-view stereo pipeline. The proposed contributions are: a methodology for super resolution of VHR data that takes into account realistic simulation of a satellite product; a testbed for the evaluation of the impact of super resolution on 3D photogrammetric reconstruction; a local analysis of the consequences of deep learning SR of VHR images on stereo matching.

The amount of very high resolution optical satellite images at our disposal is continuously increasing. Besides, associated satellite programs often come with high revisit rates and geometric properties that allow for either opportunistic or by-design 3D stereo reconstruction. Digital Surface Models (DSM) computed from these satellite images offer new possibilities. In the past, the high revisit rate has largely benefited glacier monitoring studies. Now, DSM with increased resolution provided on urban areas can be used for smart city applications as well. However, most of these require 3D modeling of buildings with level of details ranging from 0 to 2. This is where the need for better reconstructed buildings inside DSM arises. Indeed, building edges and corners tend to be smoothed and softened by the stereo matching step of a DSM computation pipeline. This undesired behavior can mostly be linked to the difficult task of optimizing the Disparity Space Image, thus finding good balance between smoothing untextured areas while conserving sharp discontinuities where needed. In this paper, we show how the optimization can benefit from an input building semantic segmentation. We also provide a method to create it from a very high satellite image in epipolar geometry using a convolutional neural network. To help our network generalize well on unseen areas we propose an interactive learning method based on clicked annotations. Eventually, we show that annotations can be automatically created, hence removing the need for an operator and making our solution suitable for operational conditions.

Building semantic segmentation is key to many applications relying on 3D modeling of city buildings such as urban planning or business intelligence. Recent works have shown great improvements in this area thanks to artificial intelligence, but even state of the art neural networks encounter difficulties to generalize to buildings that are different from the training dataset. 3D modeling applications also requires the elevation information often retrieved from a pair of High Resolution satellite images. In this article, we show that using both images of a stereo pair as inputs to a neural network trained for building semantic segmentation achieves better results than using a single view. Especially, stereo training gives a greater ability to generalize. We show that using neural networks designed for disparity estimation performs well for building semantic segmentation from a pair of satellite views in epipolar geometry. We also discuss how radiometry and disparity both affect the definition of what a building is depending on the multi-view network architecture.

In a 3D reconstruction pipeline, stereo matching step aims at computing a disparity map representing the depth between image pair. The evaluation of the disparity map can be done through the estimation of a confidence metric. In this article, we propose a new confidence metric, named ambiguity integral metric, to assess the quality of the produced disparity map. This metric is derived from the concept of ambiguity, which characterizes the property of the cost curve profile. It aims to quantify the difficulty in identifying the correct disparity to select. The quality of ambiguity integral metric is evaluated through the ROC curve methodology and compared with other confidence measures. In regards to other measures, the ambiguity integral measure shows a good potential. We also integrate this measure through various steps of the stereo matching pipeline in order to improve the performance estimation of the disparity map. First, we include ambiguity integral measure during the Semi Global Matching optimization step. The objective is to weight, by ambiguity integral measure, the influence of points in the SGM regularization to reduce the impact of ambiguous points. Secondly, we use ambiguity as an input of a disparity refinement deep learning architecture in order to easily locate noisy area and preserve details.

Abstract Background Reduced body weight at the time of intensive care unit (ICU) admission is associated with worse survival, and a paradoxical benefit of obesity has been suggested in critical illness. However, no research has addressed the survival effects of disaggregated body constituents of dry weight such as skeletal muscle, fat, and bone density. Methods Single-center, prospective observational cohort study of medical ICU (MICU) patients from an academic institution in the USA. Five hundred and seven patients requiring CT scanning of chest or abdomen within the first 24 h of ICU admission were evaluated with erector spinae muscle (ESM) and subcutaneous adipose tissue (SAT) areas and with bone density determinations at the time of ICU admission, which were correlated with clinical outcomes accounting for potential confounders. Results Larger admission ESM area was associated with decreased odds of 6-month mortality (OR per cm2, 0.96; 95% CI, 0.94–0.97; p

Caddisflies are a highly diverse group of aquatic insects, particularly in the Neotropical region where there is a high number of endemic taxa. Based on taxonomic contributions published until August 2019, a total of 796 caddisfly species have been recorded from Brazil. Taxonomic data about Brazilian caddisflies are currently open access at the “Catálogo Taxonômico da Fauna do Brasil” website (CTFB), an on-line database with taxonomic information on the animal species occurring in Brazil. The order Trichoptera at CTFB includes a catalog of species recorded for the country, with synonymic lists, distribution throughout six biomes, 12 hydrographic regions, and 27 political states (including Federal District) from Brazil. The database is constantly updated to include newly published data. In this study, we reviewed the taxonomic effort on Brazilian caddisflies based on data currently in CTFB database. The accumulation curve of species described or recorded from the country, by year, shows a strong upward trend in last 25 years, indicating that it is possible that there are many more species to be described. Based on presence/absence of caddisfly species at three geographic levels (biomes, hydrographic regions, and states), second order Jackknife estimated at least 1,586 species occurring in Brazil (with hydrographic regions as unities), indicating we currently know about 50% of the Brazilian caddisfly fauna. Species distribution by Brazilian biomes reveals that the Atlantic Forest is the most diverse, with 490 species (298 endemic), followed by the Amazon Forest, with 255 species (101 endemic). Even though these numbers may be biased because there has been more intense collecting in these two biomes, the percentage of endemic caddisfly species in the Atlantic Forest is remarkable. Considering the distribution throughout hydrographic regions, clustering analyses (UPGMA) based on incidence data reveals two groups: northwestern basins and southeastern. Although these groups have weak bootstrap support and low similarity in species composition, this division of Brazilian caddisfly fauna could be related to Amazon-Atlantic Forest disjunction, with the South American dry diagonal acting as a potential barrier throughout evolutionary time.

Several 3D reconstruction pipelines are being developed around the world for satellite imagery. Most of them implement their own versions of Semi-Global Matching, as an option for the matching step. However, deep learning based solutions already outperform every SGM derived algorithms on Kitti and Middlebury stereo datasets. But these deep learning based solutions need huge quantities of ground truths for training. This implies that the generation of ground truth stereo datasets, from satellite imagery and lidar, seems to be of great interest for the scientific community. It will aim at reducing the potential transfer learning difficulties, that could arise from a training done on datasets such as Middlebury or Kitti. In this work, we present a new ground truth generation pipeline. It produces stereo-rectified images and ground truth disparity maps, from satellite imagery and lidar. We also assess the rectification and the disparity accuracies of these outputs. We finally train a deep learning network on our preliminary ground truth dataset.

ABSTRACT The caddisfly genus Contulma Flint, 1969, with 30 described species, is endemic to the Neotropics. In addition to the five species currently recorded from Brazil, a new species of Contulma is described and illustrated in this work. Contulma vascaina sp. n. is similar to C. sana Jardim and Nessimian, 2011 in prominent, slender and rod-like posterolateral lobe of segment IX, but the new species has a shorter posterolateral lobe, with long, spine-like seta apically. Contulma vascaina sp. n. can also be characterized by the presence of a large, hood-like sclerotized plate on the endothecal phallic membrane. The female genital structure is also described and illustrated.

In the frame of its earth observation missions, CNES created a library called QPEC, and one of its launcher called Medicis. QPEC / Medicis is a sub-pixel two-dimensional stereo matching algorithm that works on an image pair. This tool is a block matching algorithm, which means that it is based on a local method. Moreover it does not regularize the results found. It proposes several matching costs, such as the Zero mean Normalised Cross-Correlation or statistical measures (the Mutual Information being one of them), and different match validation flags. QPEC / Medicis is able to compute a two-dimensional dense disparity map with a subpixel precision. Hence, it is more versatile than disparity estimation methods found in computer vision literature, which often assume an epipolar geometry. CNES uses Medicis, among other applications, during the in-orbit image quality commissioning of earth observation satellites. For instance the Pléiades-HR 1A & 1B and the Sentinel-2 geometric calibrations are based on this block matching algorithm. Over the years, it has become a common tool in ground segments for in-flight monitoring purposes. For these two kinds of applications, the two-dimensional search and the local sub-pixel measure without regularization can be essential. This tool is also used to generate automatic digital elevation models, for which it was not initially dedicated. This paper deals with the QPEC / Medicis algorithm. It also presents some of its CNES applications (in-orbit commissioning, in flight monitoring or digital elevation model generation). Medicis software is distributed outside the CNES as well. This paper finally describes some of these external applications using Medicis, such as ground displacement measurement, or intra-oral scanner in the dental domain.

ABSTRACT Two new species of Oxyethira Eaton, 1873 are described: Oxyethira cascadanta sp. n. and Oxyethira quadrilobata sp. n. Description and illustrations of the new species are provided based on specimens collected in Parque Nacional da Serra da Canastra, Minas Gerais State, Southeast Brazil. Both species are assigned to the subgenus Loxotrichia but differ from other species mainly in morphology of the subgenital plate, subgenital processes and inferior appendages. Additionally, we provide an updated checklist of the Oxyethira species from Brazil.

Phylloicus monneorum sp. nov. is described and illustrated based on specimens collected in the Mantiqueira mountain range, Itatiaia, state of Rio de Janeiro, Brazil. The new species is the smallest within the genus. The male can be distinguished by the preanal appendages short, wide, flattened and with irregular margins, and by the deeply V-shaped tergum X. The female is also described and figured. The immature stages are unknown.

This study aims to reveal patterns of e-petition co-signing behavior that are indicative of the political mobilization of online “communities”. We discuss the case of We the People , a US national experiment in the use of social media technology to enable users to propose and solicit support for policy suggestions to the White House. We apply Baumgartner and Jones's work on agenda setting and punctuated equilibrium, which suggests that policy issues may lie dormant for periods of time until some event triggers attention from the media, interest groups, and elected representatives. In the case study presented, we focus on 21 petitions initiated during the week after the Sandy Hook shooting (14–21 December 2012) in opposition to gun control or in support of policy proposals that are alternatives to gun control, which we view as mobilized efforts to maintain stability and equilibrium in a policy system threatening to change. Using market basket analysis and social network analysis we found a core group of petitions in the “support law-abiding gun owners” theme that were highly connected and four “communities” of e-petitioners mobilizing in opposition to change in gun control policies and in favor of alternative proposals.