With coastal erosion and the increased interest in beach monitoring, there is a greater need for evaluation of the shoreline detection methods. Some studies have been conducted to produce state of the art reviews on shoreline definition and detection. It should be noted that with the development of remote sensing, shoreline detection is mainly achieved by image processing. Thus, it is important to evaluate the different image processing approaches used for shoreline detection. This paper presents a state of the art review on image processing methods used for shoreline detection in remote sensing. It starts with a review of different key concepts that can be used for shoreline detection. Then, the applied fundamental image processing methods are shown before a comparative analysis of these methods. A significant outcome of this study will provide practical insights into shoreline detection.

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BackgroundPeople with disabilities represent approximately 6% of the Senegalese population. They face significant barriers to accessing health care. Although several initiatives have been implemented to improve access to health care for this vulnerable population, few studies have examined the effects of these initiatives. We conducted a mixed methods study in three neighborhoods in Saint-Louis City (Senegal) to assess the impact of health systems and social assistance programs aimed at improving access to health care for people with disabilities.MethodsData were collected from 105 people living with disabilities aged 1–49 years (or their caregivers). Interviews were also conducted with key stakeholders in the health and welfare sectors. Global Positioning System (GPS) coordinates of all the health and social services within the city were obtained. We also conducted observations in the main regional hospital, the district health center and three level-one health facilities to assess physical accessibility as well as interactions between patients living with disabilities and health and social workers. Descriptive and multivariate analyses were performed using Sphinx software. Spatial data were used to make cartographic representations of the proximity to basic social services using Arc GIS software.ResultsSeventy-nine percent of survey respondents reported difficulty obtaining treatment. Key barriers to care included the high cost of care, as well as ill-treatment by health workers. Limited human resources and low levels of financial support, combined with logistical challenges were reported to hamper the success of social welfare initiatives that aim to facilitate access to health care for people with disabilities.ConclusionOur results suggest that initiatives to increase access to health care among people with disability in Saint-Louis have had limited impact. Study findings underscore the importance of strengthening social assistance schemes within the health system and the need for social workers and health workers to collaborate to improve access to health care for people with disabilities.

<p><strong>Abstract.</strong> Coastline detection is a very challenging task in optical remote sensing. However the majority of commonly used methods have been developed for low to medium resolution without specification of the key indicator that is used. In this paper, we propose a new approach for very high resolution images using a specific indicator. First, a pre-processing step is carried out to convert images into the optimal colour space (HSV). Then, wavelet decomposition is used to extract different colour and texture features. These colour and texture features are then used for Fusion of Over Segmentation (FOOS) based clustering to have the distinctive natural classes of the littoral. Among these classes are waves, dry sand, wet sand, sea and land. We choose the mean level of high tide water, the interface between dry sand and wet sand, as a coastline indicator. To find this limit, we use a Distance Regularization Level Set Evolution (DRLSE), which automatically evolves towards the desired sea-land border. The result obtained is then compared with a ground truth. Experimental results prove that the proposed method is an efficient coastline detection process in terms of quantitative and visual performances.</p>

The outburst of the CoVid-19 pandemic has raised several questions leading to a complex system in terms of modeling. Indeed, the modeling of the epidemic, at the level of a country, needs considering each of the different sources of contamination as well as the public health authorities' strategy, in a specific way. With this in mind, in the present paper, we develop a mathematical model of the CoVid-19 epidemic in Senegal. In the model, the population is subdivided into five compartments: susceptible, infected but asymptomatic, symptomatic, quarantined, and recovered immune people. In addition, due to its important impact on the propagation of the disease, we add one more variable: the number of infected objects. %\vspace{1mm} Therefore, the model corresponds to a system of six non-linear ordinary differential equations we submit to an analytical study to prove the relevancy of the model, simulate the evolution of the epidemic, and retrieve epidemiological parameters, namely the infection rate and the basic reproduction number, ${\cal R}_0$. Based on the senegalese territory CoVid-19 data, we simulate various scenarios as for the evolution of the epidemic in the country, in order to predict the peak and its magnitude with regard to the application of barrier measures. We also explore the option of collective immunity with special protection for vulnerable people. In doing so, non-available parameters are identified using some mathematical identification technics.