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Christophe Mallet,1 Jules Desmeules,2 Rassa Pegahi,3 Alain Eschalier1 1Université Clermont Auvergne, INSERM, NEURO-DOL Basics & Clinical Pharmacology of Pain, Clermont-Ferrand, France; 2Faculty of Medicine and The School of Pharmaceutical Sciences, Faculty of Sciences, Geneva University, Geneva, Switzerland; 3Medical Affairs Department, UPSA, Paris, FranceCorrespondence: Alain Eschalier, Faculté de Médecine, UMR Neuro-Dol, 49 Bd François Mitterrand, Clermont-Ferrand, 63000, France, Email alain.eschalier@uca.frAbstract: Paracetamol remains the recommended first-line option for mild-to-moderate acute pain in general population and particularly in vulnerable populations. Despite its wide use, debate exists regarding the analgesic mechanism of action (MoA) of paracetamol. A growing body of evidence challenged the notion that paracetamol exerts its analgesic effect through cyclooxygenase (COX)-dependent inhibitory effect. It is now more evident that paracetamol analgesia has multiple pathways and is mediated by the formation of the bioactive AM404 metabolite in the central nervous system (CNS). AM404 is a potent activator of TRPV1, a major contributor to neuronal response to pain in the brain and dorsal horn. In the periaqueductal grey, the bioactive metabolite AM404 activated the TRPV1 channel‐mGlu5 receptor‐PLC‐DAGL‐CB1 receptor signaling cascade. The present article provides a comprehensive literature review of the centrally located, COX-independent, analgesic MoA of paracetamol and relates how the current experimental evidence can be translated into clinical practice. The evidence discussed in this review established paracetamol as a central, COX-independent, antinociceptive medication that has a distinct MoA from non-steroidal anti-inflammatory drugs (NSAIDs) and a more tolerable safety profile. With the establishment of the central MoA of paracetamol, we believe that paracetamol remains the preferred first-line option for mild-to-moderate acute pain for healthy adults, children, and patients with health concerns. However, safety concerns remain with the high dose of paracetamol due to the NAPQI-mediated liver necrosis. Centrally acting paracetamol/p-aminophenol derivatives could potentiate the analgesic effect of paracetamol without increasing the risk of hepatoxicity. Moreover, the specific central MoA of paracetamol allows its combination with other analgesics, including NSAIDs, with a different MoA. Future experiments to better explain the central actions of paracetamol could pave the way for discovering new central analgesics with a better benefit-to-risk ratio.Keywords: paracetamol, acetaminophen, AM404, analgesia, molecular targets

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. [ABSTRACT FROM AUTHOR]

We report key elements and figures related to the proceedings of the 2020 edition of the XXIVth ISPRS Congress. The COVID-19 pandemic causes global travel challenges and restrictions for the full year 2020. Consequently, the Congress planned in June 2020 in Nice (France) was postponed to July 2021. Papers were already submitted and the review process was almost complete. Thus, it has been decided to achieve the publication of the proceedings of these papers under the label "2020 Edition". The authors of published papers have the opportunity to present their work during a Virtual Event (31 August – 2 September 2020), while the papers of the 2021 edition will be presented during the (physical) Congress in July 2021. [ABSTRACT FROM AUTHOR]

Sentinel-2 satellites provide dense image time series exhibiting high spectral, spatial and temporal resolution. These images are in particular of utter interest to map Land-Cover (LC) at large scale. 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 national topographic geodatabase. The ι2 framework is adopted and tested for the specificity of the country. Here, experiments focus on generic 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, allowing to provide a first step towards finer-grained map retrieval. [ABSTRACT FROM AUTHOR]

Time series of optical and Synthetic Aperture RADAR (SAR) images provide complementary knowledge about the cover and use of the Earth surface since they exhibit information of distinct physical nature. They have proved to be particularly relevant for monitoring large areas with high temporal dynamics and related to significant ecosystem services. Grasslands are such crucial surfaces, both in terms of economic and environmental issues and the automatic and frequent monitoring of their agricultural practices is required for many purposes. To address this problem, the deep-based SenDVI framework is presented. SenDVI proposes an object-based methodology to estimate NDVI values from Sentinel-1 SAR observations and contextual knowledge (weather, terrain). Values are regressed every 6 days for compliance with monitoring purposes. Very satisfactory results are obtained with this low-level multimodal fusion strategy (R2 = 0.84 on a Sentinel-2 tile). Finer analysis is however required to fully assess the relevance of each modality (Sentinel-1, Sentinel-2, weather, terrain) and feature sets and to propose the simplest conceivable framework. Results show that not all features are necessary and can be discarded while others have a mandatory contribution to the regression task. Moreover, experiments prove that accuracy can be improved by not saturating the network with non-essential information (among contextual knowledge in particular). This allows to move towards more operational solution. [ABSTRACT FROM AUTHOR]

Many land-cover products have been made available for a large range of end-users over the last ten years, even at global scales. In particular, remote sensing data analysis has proved to be the most feasible solution for automation purposes, at multiple spatial scales. However, current solutions are not sufficient for designing better products, adapted to real-case applications, operational constraints, and the generation of services, built upon these core layers. In this paper, we review the main requirements and the recent changes in remote sensing for the specific case of very high resolution land-cover mapping. We also comment current and evaluate challenges for the optimal exploitation of Earth Observation images with the aim of automatically generating maps tailored to specific end-users' needs. We advocate for more challenging large-scale benchmarks and for human-in-the-loop solutions. [ABSTRACT FROM AUTHOR]

In order to reach the goal of reliably solving Earth monitoring tasks, automated and efficient machine learning methods are necessary for large-scale scene analysis and interpretation. A typical bottleneck of supervised learning approaches is the availability of accurate (manually) labeled training data, which is particularly important to train state-of-the-art (deep) learning methods. We present SemCity Toulouse, a publicly available, very high resolution, multi-spectral benchmark data set for training and evaluation of sophisticated machine learning models. The benchmark acts as test bed for single building instance segmentation which has been rarely considered before in densely built urban areas. Additional information is provided in the form of a multi-class semantic segmentation annotation covering the same area plus an adjacent area 3 times larger. The data set addresses interested researchers from various communities such as photogrammetry and remote sensing, but also computer vision and machine learning. [ABSTRACT FROM AUTHOR]

Images from archival aerial photogrammetric surveys are a unique and relatively unexplored means to chronicle 3D land-cover changes occurred since the mid 20th century. They provide a relatively dense temporal sampling of the territories with a very high spatial resolution. Thus, they offer time series data which can answer a large variety of long-term environmental monitoring studies. Besides, they are generally stereoscopic surveys, making it possible to derive 3D information (Digital Surface Models). In recent years, they have often been digitized, making them more suitable to be considered in automatic analyses processes. Some photogrammetric softwares make it possible to retrieve their geometry (pose and camera calibration) and to generate corresponding DSM and orthophotomosaic. Thus, archival aerial photogrammetric surveys appear as being a powerful remote sensing data source to study land use/cover evolution over the last century. However, several difficulties have to be faced to be able to use them in automatic analysis processes. Indeed, surveys available on a study area can exhibit very different characteristics: survey pattern, focal, spatial resolution, modality (panchromatic, colour, infrared…). Planimetric and altimetric accuracies of derived products strongly depend on these characteristics. Thus, analysis processes have to cope with these uncertainties. Another important gap states in the lack of training data. Deep learning methods and especially Convolutional Neural Networks (CNN) are at present the most efficient semantic segmentation methods as long as a sufficient training dataset is available. However, temporal gaps can be very important between existing available databases and archival data. In this study, two custom variants of simple yet effective U-net - Deconv-Net inspired DL architectures are developed to process ortho-image and DSM based information. They are then trained out of a groundtruth derived out of a recent database to process archival datasets. [ABSTRACT FROM AUTHOR]