GEOSPATIAL TECHNIQUES USE FOR ASSESSMENT OF VULNERABILITY TO URBAN FLOODING IN BUJUMBURA CITY, BURUNDI

The rate of urbanization growth in tropical areas, particularly in African cities, coupled with a gap in the knowledge of vulnerability and coping capacities increases the flood-related risk in diverse communities. This study aims to evaluate the factors of vulnerability to flooding and to develop a vulnerability index in Bujumbura city, Burundi. To this end, both physical and socio-economic parameters accountable for flood vulnerability have been integrated with a geospatial analysis process based on the Analytic Hierarchy Process (AHP) and Weighted Linear Combination (WLC) methods. The resulting vulnerability index shows that low-income households and their local infrastructures are the most vulnerable to flooding. Another finding reveals that higher vulnerability is due to settlements located in flood-prone areas with unplanned land use and ill-structured development planning

OBJECT BASED “DAYAS” CLASSIFICATION USING SENTINEL A-2 SATELLITE IMAGERY CASE STUDY CITY OF BENSLIMANE

The management of “DAYAS” is a major issue in the preservation and maintain of biodiversity and environmental balance, especially in a context where this fragile ecosystems face many degradation factors. The extraction of Dayas is a key component in the management process of this type of wetlands, and has been the subject of many researches related to remote sensing. The methods and instrumentation for optical remote sensing are used to improve the mapping of Dayas, based on the radiometric characteristics of local hydrosystems. The present paper studies the inputs of different methods for the delimitation and extraction of Dayas in the realm of Benslimane city, using Sentinel A-2 imagery for the mapping. The methodology for the application of the pixel-based and the object-oriented approaches requires many steps, starting from an image pre-processing with Sentinel-2 calibration, the calculation of NDWI index, to proceed to the extraction of Dayas from the very high resolution image segmentation, then the application of the object-oriented classification to validate the results. The cartographic results demonstrate the input of the applied methodology in the Dayas extraction in different situations and timing (winter/summer), and allow to measure the cartographic accuracy for each approach, finding 65% of accuracy for the pixel-based approach with Kappa index = 0.40 versus 75% of accuracy for the object-oriented approach with Kappa index = 0.72. The results achieved inform and orient about optimisation measures and regulations of the Kappa index to improve the Dayas extraction and mapping

BIG HEALTH DATA: A SYSTEMATIC MAPPING STUDY

The Big Data, a result of the digital revolution, offers several opportunities in the field of health. Indeed, appliances and applications permanently connected to humans and the global digitalization of medical documents produce a vast health data: "Big Health Data". This data is the subject of several projects in the world given the opportunities offered to optimize this area. This paper focuses on quantifying the production of scientific articles about Big Health Data research and the most investigated Big Health Data topics. It also presents a mapping of countries producing articles about this subject. In remote sensing using real time categories, we aimed to quantify articles dealing with “big data architectures”, technologies and data sources used. A systematic mapping study was conducted with a set of seven research questions by investigating articles from two digital libraries: Scopus and Springer. The study concern articles published in 2017 and the first half of 2018. The results are illustrated by diagrams answering each question from which a set of recommendations are concluded in this area of research. The study shows that this Data is used the most in studies of oncology. Statistics show that while remote sensing and monitoring is a hot topic, real-time use is not as interesting. It was found that there’s a lack in studies interested in big data technologies used in real time remote sensing in the field of health. In conclusion, we recommend more focus on research area treating architecture in remote sensing real time Big Health Data systems combined with geolocation

USING GIS AND PHOTOGRAMMETRY FOR ASSESSING SOLAR PHOTOVOLTAIC POTENTIAL ON FLAT ROOFS IN URBAN AREA CASE OF THE CITY OF BEN GUERIR / MOROCCO

Renewable energy sources are at the forefront of political discussions around the world because of the scarcity of fossil fuels and climate change caused by the accumulation of greenhouse gases. By 2030, Morocco will cover 52% of these energy needs through renewable energies, in order to preserve the environment (COP 22). This paper aims to estimate the potential of photovoltaic solar energy from flat roofs in the city of Ben Guerir, Morocco using remote sensing and GIS data. To achieve this goal, vector orthophoto resulting from the photogrammetric restitution acquired in 2015 were used to generate a 3D model (DSM). The annual solar irradiation is calculated by the analyser of the solar tool. Each roof is calculated based on algorithms for the most common solar panel technologies (mono-si and poly-si). The applicability of this methodology has been demonstrated in the urban area of Benguerir, Morocco, and can be widespread in any other region of the world. The results obtained for a total roofing surface of 135 Ha, i.e. more than 345 Gwh of electricity annually generate. For an average roof of 60 m2 that could supply 5 to 6 households; A planned investment between 118,218 and 167,296 DH, and an annual maintenance charge of 2%. This study may be an initial assessment of solar potential in the city, which can be used to support the management decision regarding investment in the urban solar system

FUSING OF OPTICAL AND SYNTHETIC APERTURE RADAR (SAR) REMOTE SENSING DATA: A SYSTEMATIC LITERATURE REVIEW (SLR)

Remote sensing and image fusion have recognized many important improvements throughout the recent years, especially fusion of optical and synthetic aperture radar (SAR), there are so many published papers that worked on fusing optical and SAR data which used in many application fields in remote sensing such as Land use Mapping and monitoring. The goal of this survey paper is to summarize and synthesize the published articles from 2013 to 2018 which focused on the fusion of Optical and synthetic aperture radar (SAR) remote sensing data in a systematic literature review (SLR), based on the pre-published articles on indexed database related to this subject and outlining the latest techniques as well as the most used methods. In addition this paper highlights the most popular image fusion methods in this blending type. After conducting many researches in the indexed databases by using different key words related to the topic “fusion Optical and SAR in remote sensing”, among 705 articles, chosen 83 articles, which match our inclusion criteria and research questions as results ,all the systematic study ‘ questions have been answered and discussed

THE CONTRIBUTION OF GIS TO DISPLAY AND ANALYZE THE WATER QUALITY DATA COLLECTED BY A WIRELESS SENSOR NETWORK: CASE OF BOUREGREG CATCHMENT, MOROCCO

The monitoring of water quality is, in most cases, managed in the laboratory and not on real time bases. Besides this process being lengthy, it doesn’t provide the required specifications to describe the evolution of the quality parameters that are of interest. This study presents the integration of Geographic Information Systems (GIS) with wireless sensor networks (WSN) aiming to create a system able to detect the parameters like temperature, salinity and conductivity in a Moroccan catchment scale and transmit information to the support station. This Information is displayed and evaluated in a GIS using maps and spatial dashboard to monitor the water quality in real time

MACHINE LEARNING (AI) FOR IDENTIFYING SMART CITIES

Cities worldwide are attempting to be claimed as smart, but truly classifying as such remains a great challenge. This paper aims to use artificial intelligence AI to classify the smart city's performance as well as the factors linked to it. This is based on the perceptions of residents on issues related to structures and technology applications available in their cities. To achieve this goal, the study included 200 cities worldwide. For 147 cities we captured the perceptions of 120 residents in each city, by answering a survey of 39 questions evolving around two main Pillars: ‘Structures’ that refers to the existing infrastructure of the city and the ‘Technology’ pillar that describes the technological provisions and services available to the inhabitants. And each one is evaluated under five key areas: health and safety, mobility, activities, opportunities, and governance. The final score of the other 53 cities, was measured by using the data openly available on the internet. And this by means of different algorithms of machine learning such as Random Forest RF, Artificial Neural Network ANN, Support Vector Machine (SVM), and Gradient Boost (XGB). These algorithms have been compared and evaluated in order to select the best one. The tests showed that Random Forest RF alongside with Artificial Neural Network ANN, with the highest level of accuracy, are the best trained model. This study will enable other researches to use machine learning in the identification process of smart cities