Web based methodologies and techniques to monitor electronic resources use in university libraries

The aim of this paper is to measure user satisfaction and the quality of the electronic resources consultation services offered by the Milano Bicocca University Library

Le indagini sull’utilizzo delle risorse elettroniche remote della Biblioteca dell’Universitá di Milano-Bicocca

Electronic resources are a growing property in academic library systems. The “hybrid” model is characterized by a strong presence of digital resources and services joining or substituting books, paper journals and the traditional methods of bibliographic research. The shift from a traditional to a hybrid library system requires important financial investments, so the analysis and the monitoring of the service supply has a particular meaning in this sense. The Library Management performed two surveys on electronic resource usage and evaluation after the experience of scholars and academic researchers. The article presents the results and analysis for the years 2001 and 2002

Web based methodologies and techniques to monitor electronic resources use in university libraries

The aim of this paper is to measure user satisfaction and the quality of the electronic resources consultation services offered by the Milano Bicocca University Library

DUG User Guide. Version 2.1

This user guide accompanies the DUG tool which is a public tool for applying the “Degree of urbanisation” (DEGURBA) model at one kilometer grid. DUG stands for Degree of Urbanisation Grid. It has been developed in the frame of the “Global Human Settlement Layer” (GHSL) project of the European Commission’s Joint Research Centre, with the overall objective to support the DEGURBA activities. The tool builds on the GHS SMOD model that implements settlement model classifier at 1 km grid. The tool uses population and built-up grids as input data, and optionally a water mask. It has been developed and tested using GHS P2016 datasets ; however other grids can be used on user responsibility. This user guide is a comprehensive guide to all aspects of using the DUG tool. It includes instructions for the set-up of the software, the use of the tool and the manipulation of the data. It presents briefly the basic principles and background information on the methodology and its implementation. Some guidelines on the parametrization are also provided.JRC.E.1-Disaster Risk Managemen

MASADA USER GUIDE

This user guide accompanies the MASADA tool which is a public tool for the detection of built-up areas from remote sensing data. MASADA stands for Massive Spatial Automatic Data Analytics. It has been developed in the frame of the “Global Human Settlement Layer” (GHSL) project of the European Commission’s Joint Research Centre, with the overall objective to support the production of settlement layers at regional scale, by processing high and very high resolution satellite imagery. The tool builds on the Symbolic Machine Learning (SML) classifier; a supervised classification method of remotely sensed data which allows extracting built-up information using a coarse resolution settlement map or a land cover information for learning the classifier. The image classification workflow incorporates radiometric, textural and morphological features as inputs for information extraction. Though being originally developed for built-up areas extraction, the SML classifier is a multi-purpose classifier that can be used for general land cover mapping provided there is an appropriate training data set. The tool supports several types of multispectral optical imagery. It includes ready-to-use workflows for specific sensors, but at the same time, it allows the parametrization and customization of the workflow by the user. Currently it includes predefined workflows for SPOT-5, SPOT-6/7, RapidEye and CBERS-4, but it was also tested with various high and very high resolution1 sensors like GeoEye-1, WorldView-2/3, Pléiades and Quickbird.JRC.E.1-Disaster Risk Managemen

Next Generation Mapping of Human Settlements from Copernicus Sentinel-2 data: leveraging cloud computing, machine learning and earth observation data

Since the advent of the openly accessible Sentinel satellite data as part of the Copernicus programme of the European Commission and ESA, massive amounts of satellite data have brought disruptive changes in Earth observation data management and analysis. In the context of the Global Human Settlement Layer activities, the Copernicus Sentinel-2 mission offers new opportunities for mapping human settlements over large areas and for the update and improvement of the Global Human Settlement Layer datasets and information layers. Concurrently, state-of-the-art machine learning algorithms and cloud computing infrastructures have become available with a great potential to revolutionize the image processing of satellite remote sensing. Within this context, this study explores the feasibility of refactoring the existing GHSL workflows and applications into the cloud computing paradigm by leveraging the functionalities offered by the Distributed Web Platform WASDI combined with advanced machine learning methods for image processing and classification. In this report, we summarize the lessons learnt using WASDI for mapping of built-up areas from Sentinel data. We present the advantages of both convenient and powerful workflow management and cloud scalability and the experiences gained and challenges using the WASDI platform. The experiments showed that porting of the GHSL workflows to DIAS can be facilitated by the WASDI interface. When testing two different cloud providers, large differences in the time for accessing the Sentinel-2 data and downloading it were observed and had the largest impact on the performances of the workflows.JRC.E.1-Disaster Risk Managemen

MASADA Sentinel 1 & 2 User Guide

MASADA stands for Massive Spatial Automatic Data Analytics. It has been developed in the frame of the “Global Human Settlement Layer” (GHSL) project of the European Commission’s Joint Research Centre, with the overall objective to support the production of settlement layers, by automatic classification of high and very high resolution satellite imagery. The tool builds on the Symbolic Machine Learning (SML) classifier; a supervised classification method of remotely sensed data which allows extracting built-up information using a coarse resolution settlement map or a land cover information for learning the classifier. The first version of MASADA (v1.3) supports Very High Resolution satellite data and includes pre-defined workflows for a variety of sensors (e.g. SPOT-5, SPOT-6/7, RapidEye, CBERS-4). The second version of MASADA (v2.0) is tailored to the processing of Copernicus Sentinel-1 and Sentinel-2 data. Two workflows building on the SML but adapted to the characteristics of each of the two sensors have been implemented in a stand-alone software. The tool is designed for the processing of single scenes, for batch processing of a series of scenes and for parallel processing of large datasets thanks to a dedicated command-line interface. This user guide is a comprehensive guide to all aspects of using the MASADA tool. It includes instructions for the installation of the software, the use of the tool and the manipulation of the data. It presents briefly the basic principles and background information on the two main modules integrated in this new version: S1 module and S2 module. Some guidelines on the parametrization of the modules are also provided together with test datasets.JRC.E.1-Disaster Risk Managemen

GHS-DU-TUC User Guide: Degree of Urbanisation Territorial Units Classifier User Guide Version 1

The Degree of Urbanisation Territorial Units Classifier (GHS-DU-TUC) Tool (– version 1) is an information system developed in the framework of the Global Human Settlement Layer (GHSL) to produce the classification of territorial units based on the Degree of Urbanisation and extract related statistics. The tool classifies territorial units by Degree of Urbanisation at Level 1 (3 classes) and Level 2 (7 classes) based on population majority by settlement classes derived from the “Degree of Urbanisation” method and ported to the GHSL environment through the GHSL Settlement Model (GHSL SMOD). The GHS-DU-TUC 1 is designed as an operational tool to perform the second step required to apply the Degree of Urbanisation released as standalone tool and as ArcGIS Toolbox. Once the first step produces the settlement classification grid (i.e. with the GHS-DUG Tool), the user runs the GHS-DU-TUC that requires this settlement classification grid, the population grid used to produce the settlement classification grid (i.e. produced with the GHS-POP2G Tool) and a geometry of territorial units to be classified by Degree of Urbanisation. This tool is conceptualised to be deployed after the application of the GHSL tools GHS-POP2G and GHS-DUG but it accepts in input population grids produced by means of any other procedure respecting the described constrains. This document contains the description of the GHS-DU-TUC Tool use, the rationale for the second step to apply the Degree of Urbanisation (the classification of territorial units) and the comprehensive description of the outputs. The tool is a capacity enhancement asset in the framework of the multi-stakeholder effort for the uptake of the Degree of Urbanisation, the people-based harmonised definition of cities and settlements recommended by the 51st Session of the United Nations Statistical Commission as the method to delineate cities and rural areas for international statistical comparison. The GHS-DU-TUC, as all GHSL Tools, is issued with an end-user licence agreement, included in the download package.JRC.E.1-Disaster Risk Managemen

GHSL-OECD Functional Urban Areas

Function Urban Areas (FUAs), as defined by the OECD and the European Union, are sets of contiguous local (administrative) units composed of a ‘city’ and its surrounding, less densely populated local units that are part of the city’s labour market (‘commuting zone’). To be included in the commuting zone, local units should at least 15% of their working population to the city. This definition is limited to the OECD countries and it is subject to both availability of commuting flows data at local level and to the definition of administrative unit boundaries. In the context of international comparability of urban-related statistics and indicators the aim of this task is to propose a FUA definition that does not depend on arbitrary and not harmonized administrative units and scale it to the globe. To pursue this goal it is proposed an automated classification procedure of FUAs based on objective characteristics (distance from the Urban Centre, area and population of the Urban Centre, local population and GDP per capita at national level), to classify areas within and outside FUAs. The automated classification of FUA is done in collaboration with the OECD and supported by DG REGIO. This document describes the public release of the GHSL-OECD Functional Urban Areas 2019 (GHS-FUA).JRC.E.1-Disaster Risk Managemen

DUG User Guide

The Degree of Urbanisation Grid (DUG) Tool (– version 3.0) is an information system developed in the framework of the Global Human Settlement Layer (GHSL) to produce geospatial grids to map settlement classes and extract related statistics. The settlement classes are derived from the “Degree of Urbanisation” method and ported to the GHSL environment through the GHSL Settlement Mode (GHSL SMOD)l. The DUG 3.0 is designed as a scalable tool allowing the application of the GHSL Settlement Model to the input data available to the user or to data made available in the GHSL Data Package 2019. This document contains the description of the DUG Tool use, the rationale of the differentiation between settlement classes and the comprehensive description of the outputs. The tool is a capacity enhancement asset in the framework of the multi-stakeholder effort to develop a people-based harmonised definition of cities and settlements that helps the assessment of the feasibility of applying a global definition of cities/urban areas in support of global monitoring of SDGs and the New Urban Agenda urban targetsJRC.E.1-Disaster Risk Managemen