Modelado en 3D de una puerta de la ciudad de Rennes del siglo XV: Portes Mordelaises

[EN] The Portes Mordelaises, remnants of the medieval city walls of Rennes, France, has been the subject of several archaeological excavations until 2017. From these excavations, we created a three-dimensional (3D) model of the site reconstructed as it would have appeared during the 15th century, including the surrounding plus the interiors of its famed towers. Once our efforts and results were officially recognised as being of national interest by the French Ministry of Culture and Communication, Department of Heritage and the National Museum Service of France, we presented our virtual model reconstruction in an exhibition curated by the Museum of Bretagne, entitled "Rennes, les vies d'une ville" (Rennes, the Lives of a City). This approach to 3D reconstruction of the site served to further study Rennes’ origins, its construction, organisation, as well as its historic relationship to surrounding territories. The main objective of this work was to investigate, using of a significant and com[ES] Las Portes Mordelaises, restos de las murallas medievales de la ciudad de Rennes, Francia, han sido objeto de varias excavaciones arqueológicas hasta el año 2017. A partir de estas excavaciones, pudimos crear un modelo tridimensional (3D) del sitio reconstruido tal y como habría aparecido durante el siglo XV, incluyendo los terrenos circundantes así como los interiores de sus famosas torres. Una vez que nuestros esfuerzos y resultados fueron reconocidos oficialmente como de interés nacional por el Ministerio de Cultura y Comunicación de Francia, el Departamento de Patrimonio así como el Servicio Nacional de Museos de Francia, presentamos nuestra reconstrucción del modelo virtual en una exposición gestionada por el Museo de Bretaña titulada "Rennes, las vidas de una ciudad”. Este enfoque de la reconstrucción en 3D del sitio sirvió para profundizar en el estudio de los orígenes de Rennes, su construcción, su organización, así como su relación histórica con los territorios circundantes. EBarreau, J.; Esnault, E.; Foucher, J.; Six, M.; Le Faou, C. (2020). 3D modelling of a 15th century city gate of Rennes: Portes Mordelaises. Virtual Archaeology Review. 11(22):41-55. https://doi.org/10.4995/var.2020.12653OJS41551122Ahmad, T., Afzal, M., Hayat, F., Asif, H. S., Ahsan, S., & Saleem, Y. (2012). Need for software design methodology for remote sensing applications. Life Sci Journal, 9(3), 2152-2156.Al-Baghdadi, M. A. S. (2017). 3D printing and 3D scanning of our ancient history: Preservation and protection of our cultural heritage and identity. International Journal of Energy and Environment, 8(5), 441-456.Alix, C., Carron, D., Roux-Capron, E., & Josserand, L. (2016). La porte Bannier, entrée principale de la ville d'Orléans aux XIVe-XVe siècles. 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Towards the automation of large mammal aerial survey in Africa

editorial reviewedIn African open protected areas, large mammals are often surveyed using manned aircrafts which actively count the animals in sample strips for later density extrapolation to the whole area. Nevertheless, this method may be biased among others by the observer’s detection capability. The use of on-board oblique cameras has recently shown an increase in counting accuracy as a result of indirect photo-interpretation. While this approach appears to reduce some biases, the processing time of the generated data is currently a bottleneck. In recent years, Deep Learning (DL) techniques through dense convolutional neural networks (CNNs) have emerged as a very promising avenue for managing such datasets. However, we are not yet at the stage of full automation of the process (i.e. from acquisition to population estimation). Three challenges were identified: 1) reducing false positives, 2) increasing the precision in close-by individuals, and 3) properly managing the overlap between images to avoid double counting. We focused on the two first aspects and developed a new point-based DL model inspired by crowd counting, that was applied on a challenging oblique aerial dataset containing free ranging livestock herds in heterogeneous open arid landscapes. The model’s performances were then evaluated using localization and counting metrics. The DL model achieved a global F1 score of 0.74 and a RMSE of 9.8 animals per 24 megapixel image, at a processing speed of 3.6 s/image. It showed a valuable ability to detect both isolated animals and those in dense herds. This is auspicious for automation of African mammal surveys but the developed approach still needs to be improved to manage double counting on entire transects. These results emphasize the importance of standardization of data acquisition, with strong spatial and temporal heterogeneities, in order to build robust models that can be used in similar environments and conditions

Counting African Mammal Herds in Aerial Imagery Using Deep Learning: Are Anchor-Based Algorithms the Most Suitable?

editorial reviewedMonitoring wildlife and livestock in protected areas is essential to reach natural ecosystem conservation goals. In large open areas, this is often carried out by direct counting from observers in manned aircrafts flying at low altitude. However, there are several biases associated with this method, resulting in a low accuracy of large groups counts. Unmanned Aerial Vehicles (UAVs) have experienced a significant growth in recent years and seem to be relatively well-suited systems for photographing animals. While UAVs allow for more accurate herd counts than traditional methods, identification and counting are usually indirectly done during a manual time-consuming photo-interpretation process. For several years, machine learning and deep learning techniques have been developed and now show encouraging results for automatic animal detection. Some of them use Convolutional Neural Networks (CNNs) through anchor-based object detectors. These algorithms automatically extract relevant features from images, produce thousands of anchors all over the image and eventually decide which ones actually contain an object. Counting and classification are then achieved by summing and classifying all the selected bounding boxes. While this approach worked well for isolated mammals or sparse herds, it showed limits in close-by individuals by generating too many false positives, resulting in overestimated counts in dense herds. This raises the question: are anchor-based algorithms the most suitable for counting large mammals in aerial imagery? In an attempt to answer this, we built a simple one stage point-based object detector on a dataset acquired over various African landscapes which contains six large mammal species: buffalo (Syncerus caffer), elephant (Loxodonta africana), kob (Kobus kob), topi (Damaliscus lunatus jimela), warthog (Phacochoerus africanus) and waterbuck (Kobus ellipsiprymnus). An adapted version of the CNN DLA-34 was trained on points only (center of the original bounding boxes), splat onto a Focal Inverse Distance Transform (FIDT) map regressed in a pixel-wise manner using the focal loss. During inference, local maxima were extracted from the predicted map to obtain the animals location. Binary model’s performances were then compared to those of the state-of-the-art model, Libra-RCNN. Although our model detected 5% fewer animals compared to the baseline, its precision doubled from 37% to 70%, reducing the number of false positives by one third without using any hard negative mining method. The results obtained also showed a clear increase in precision in close-by individuals areas, letting it appear that a point-based approach seems to be better adapted for animal detection in herds than anchor-based ones. Future work will apply this approach on other animal datasets with different acquisition conditions (e.g. oblique viewing angle, coarser resolution, denser herds) to evaluate its range of use

Surveying wildlife and livestock in Uganda with aerial cameras: Deep Learning reduces the workload of human interpretation by over 70%

peer reviewedAs the need to accurately monitor key-species populations grows amid increasing pressures on global biodiversity, the counting of large mammals in savannas has traditionally relied on the Systematic-Reconnaissance-Flight (SRF) technique using light aircrafts and human observers. However, this method has limitations, including non-systematic human errors. In recent years, the Oblique-Camera-Count (OCC) approach developed in East Africa has utilized cameras to capture high-resolution imagery replicating aircraft observers’ oblique view. Whilst demonstrating that human observers have missed many animals, OCC relies on labor-intensive human interpretation of thousands of images. This study explores the potential of Deep Learning (DL) to reduce the interpretation workload associated with OCC surveys. Using oblique aerial imagery of 2.1 hectares footprint collected during an SRF-OCC survey of Queen Elizabeth Protected Area in Uganda, a DL model (HerdNet) was trained and evaluated to detect and count 12 wildlife and livestock mammal species. The model’s performance was assessed both at the animal instance-based and image-based levels, achieving accurate detection performance (F1 score of 85%) in positive images (i.e. containing animals) and reducing manual interpretation workload by 74% on a realistic dataset showing less than 10% of positive images. However, it struggled to differentiate visually related species and overestimated animal counts due to false positives generated by landscape items resembling animals. These challenges may be addressed through improved training and verification processes. The results highlight DL’s potential to semi-automate processing of aerial survey wildlife imagery, reducing manual interpretation burden. By incorporating DL models into existing counting standards, future surveys may increase sampling efforts, improve accuracy, and enhance aerial survey safety

From crowd to herd counting: How to precisely detect and count African mammals using aerial imagery and deep learning?

peer reviewedRapid growth of human populations in sub-Saharan Africa has led to a simultaneous increase in the number of livestock, often leading to conflicts of use with wildlife in protected areas. To minimize these conflicts, and to meet both communities’ and conservation goals, it is therefore essential to monitor livestock density and their land use. This is usually done by conducting aerial surveys during which aerial images are taken for later counting. Although this approach appears to reduce counting bias, the manual processing of images is timeconsuming. The use of dense convolutional neural networks (CNNs) has emerged as a very promising avenue for processing such datasets. However, typical CNN architectures have detection limits for dense herds and closeby animals. To tackle this problem, this study introduces a new point-based CNN architecture, HerdNet, inspired by crowd counting. It was optimized on challenging oblique aerial images containing herds of camels (Camelus dromedarius), donkeys (Equus asinus), sheep (Ovis aries) and goats (Capra hircus), acquired over heterogeneous arid landscapes of the Ennedi reserve (Chad). This approach was compared to an anchor-based architecture, Faster-RCNN, and a density-based, adapted version of DLA-34 that is typically used in crowd counting. HerdNet achieved a global F1 score of 73.6 % on 24 megapixels images, with a root mean square error of 9.8 animals and at a processing speed of 3.6 s, outperforming the two baselines in terms of localization, counting and speed. It showed better proximity-invariant precision while maintaining equivalent recall to that of Faster-RCNN, thus demonstrating that it is the most suitable approach for detecting and counting large mammals at close range. The only limitation of HerdNet was the slightly weaker identification of species, with an average confusion rate approximately 4 % higher than that of Faster-RCNN. This study provides a new CNN architecture that could be used to develop an automatic livestock counting tool in aerial imagery. The reduced image analysis time could motivate more frequent flights, thus allowing a much finer monitoring of livestock and their land use

Le Méthane et le destin de la Terre : Les Hydrates de méthane : rêve ou cauchemar?

Les hydrates de méthane représentent une phase solide constituée de glace et de méthane: elles constituent sur terre plusieurs dizaines de milliers de milliards de tonnes de gaz, ce qui représente un trésor énergétique inouï, ... et dévoile un danger potentiel pour l'humanité, aussi bien sur le plan climatologique que géologique... Cet ouvrage scientifique, écrit par quatre experts, fait le point sur ce phénomène qui intéresse les plus hautes autorités mondiales. L'ouvrage présente ainsi: -définition et propriétés des clathrates -les HM en milieu océanique -les HM du Permafrost -Rappels sur l'effet de serre -Le cycle du méthane -les cycles glaciaires-interglaciaires -le rôle du Méthane dans l'histoire de la Terre -Les HD, source potentielle d'énergie

Multispecies detection and identification of African mammals in aerial imagery using convolutional neural networks

Survey and monitoring of wildlife populations are among the key elements in nature conservation. The use of unmanned aerial vehicles and light aircrafts as aerial image acquisition systems is growing, as they are cheaper alternatives to traditional census methods. However, the manual localization and identification of species within imagery can be time-consuming and complex. Object detection algorithms, based on convolutional neural networks (CNNs), have shown a good capacity for animal detection. Nevertheless, most of the work has focused on binary detection cases (animal vs. background). The main objective of this study is to compare three recent detection algorithms to detect and identify African mammal species based on high-resolution aerial images. We evaluated the performance of three multi-class CNN algorithms: Faster-RCNN, Libra-RCNN and RetinaNet. Six species were targeted: topis (Damaliscus lunatus jimela), buffalos (Syncerus caffer), elephants (Loxodonta africana), kobs (Kobus kob), warthogs (Phacochoerus africanus) and waterbucks (Kobus ellipsiprymnus). The best model was then applied to a case study using an independent dataset. The best model was the Libra-RCNN, with the best mean average precision (0.80 0.02), the lowest degree of interspecies confusion (3.5 1.4%) and the lowest false positive per true positive ratio (1.7 0.2) on the test set. This model was able to detect and correctly identify 73% of all individuals (1115), find 43 individuals of species other than those targeted and detect 84 missed individuals on our independent UAV dataset, with an average processing speed of 12 s/image. This model showed better detection performance than previous studies dealing with similar habitats. It was able to differentiate six animal species in nadir aerial images. Although limitations were observed with warthog identification and individual detection in herds, this model can save time and can perform precise surveys in open savanna

Impact of gasoline direct injection fuel films on exhaust soot production in a model experiment

International audienc

Pancreatic Ductal Adenocarcinoma: A Strong imbalance of Good and Bad immunological Cops in the Tumor Microenvironment

International audiencePancreatic ductal adenocarcinoma (PDAC) is one of the most aggressive and lethal cancers with very few available treatments. For many decades, gemcitabine was the only treatment for patients with PDAC. A recent attempt to improve patient survival by combining this chemotherapy with FOLFIRINOX and nab-paclitaxel failed and instead resulted in increased toxicity. Novel therapies are urgently required to improve PDAC patient survival. New treatments in other cancers such as melanoma, non-small-cell lung cancer, and renal cancer have emerged, based on immunotherapy targeting the immune checkpoints cytotoxic T-lymphocyte-associated antigen 4 or programmed death 1 ligand. However, the first clinical trials using such immune checkpoint inhibitors in PDAC have had limited success. Resistance to immunotherapy in PDAC remains unclear but could be due to tissue components (cancer-associated fibroblasts, desmoplasia, hypoxia) and to the imbalance between immunosuppressive and effector immune populations in the tumor microenvironment. In this review, we analyzed the presence of ``good and bad immunological cops'' in PDAC and discussed the significance of changes in their balance