An open source virtual globe rendering engine for 3D applications: NASA World Wind

Background NASA World Wind is an open source application-programming interface for developing geographic information systems based on a virtual globe rendering engine representing a planet. NASA World Wind provides the ideal environment for scientific data, their analysis, visual representation and interaction with users, in a single platform, which can be deployed both as a Java desktop application (NASA World Wind) or a JavaScript web application (ESA-NASA Web World Wind). Results We give here an overview of the project, reporting details regarding current development direction, with state of the art examples. The European Space Agency is now partnering with NASA on development of the "ESA-NASA Web World Wind"; this high degree of interest from other agencies will boost future project productivity. Conclusions With this contribution, we want to increase awareness of NASA World Wind as a unique opportunity to foster collaboration between scientists, developers and other stakeholders, enriching knowledge of our Earth’s complexity

Location-aware recommendation systems: Where we are and where we recommend to go

Recommendation systems have been successfully used to provide items of interest to the users (e.g., movies, music, books, news, images). However, traditional recommenda- tion systems do not take into account the location as a relevant factor when providing suggestions. On the other hand, nowadays, there exist an increasing amount of geo- referenced data and users are usually interested only in nearby items (e.g., restaurants, museums, cinemas). Hence, the emergence of location-aware recommendation systems have acquired a great attention by the research community in the last decade. In this paper, we provide a survey of location-aware rec- ommendation systems in mobile computing scenarios. Firstly, we describe briefly the fundamentals of recommendation sys- tems. Then, we introduce some of the most relevant existing approaches for location-aware recommendation. Moreover, we present the main applications of this type of systems in several recommendation scenarios, such as music, news, restaurants, etc. Finally, we discuss new avenues and open issues in the area

Danube Region data projects

The Danube Reference Data and Services Infrastructure (DRDSI) project currently provides access to more than 6,500 datasets, relevant for one or more Priority Areas of the EU Strategy for the Danube Region (EUSDR). These datasets can act as a solid foundation for integration of scientific knowledge into the policy making process on different levels (local, regional and international). From the perspective of macro-regional strategies, this would only be possible if data can be used across borders and domains, and put in the right context. Projects at regional, national, cross-border and macro-regional levels present a useful container to uncover stakeholders, expertise and data creation/sharing capacity for policy-making and research. This JRC technical report investigates the existing project databases and similar resources related to the EUSDR that describe such projects, as well as how this information may be presented in the DRDSI platform.JRC.H.6-Digital Earth and Reference Dat

Spatial Object Recommendation with Hints: When Spatial Granularity Matters

Existing spatial object recommendation algorithms generally treat objects identically when ranking them. However, spatial objects often cover different levels of spatial granularity and thereby are heterogeneous. For example, one user may prefer to be recommended a region (say Manhattan), while another user might prefer a venue (say a restaurant). Even for the same user, preferences can change at different stages of data exploration. In this paper, we study how to support top-k spatial object recommendations at varying levels of spatial granularity, enabling spatial objects at varying granularity, such as a city, suburb, or building, as a Point of Interest (POI). To solve this problem, we propose the use of a POI tree, which captures spatial containment relationships between POIs. We design a novel multi-task learning model called MPR (short for Multi-level POI Recommendation), where each task aims to return the top-k POIs at a certain spatial granularity level. Each task consists of two subtasks: (i) attribute-based representation learning; (ii) interaction-based representation learning. The first subtask learns the feature representations for both users and POIs, capturing attributes directly from their profiles. The second subtask incorporates user-POI interactions into the model. Additionally, MPR can provide insights into why certain recommendations are being made to a user based on three types of hints: user-aspect, POI-aspect, and interaction-aspect. We empirically validate our approach using two real-life datasets, and show promising performance improvements over several state-of-the-art methods

Citizen Science and Geospatial Capacity Building

This book is a collection of the articles published the Special Issue of ISPRS International Journal of Geo-Information on “Citizen Science and Geospatial Capacity Building”. The articles cover a wide range of topics regarding the applications of citizen science from a geospatial technology perspective. Several applications show the importance of Citizen Science (CitSci) and volunteered geographic information (VGI) in various stages of geodata collection, processing, analysis and visualization; and for demonstrating the capabilities, which are covered in the book. Particular emphasis is given to various problems encountered in the CitSci and VGI projects with a geospatial aspect, such as platform, tool and interface design, ontology development, spatial analysis and data quality assessment. The book also points out the needs and future research directions in these subjects, such as; (a) data quality issues especially in the light of big data; (b) ontology studies for geospatial data suited for diverse user backgrounds, data integration, and sharing; (c) development of machine learning and artificial intelligence based online tools for pattern recognition and object identification using existing repositories of CitSci and VGI projects; and (d) open science and open data practices for increasing the efficiency, decreasing the redundancy, and acknowledgement of all stakeholders

Farm 2.0 Using Wordpress to Manage Geocontent and Promote Regional Food Products

Dissertation submitted in partial fulfilment of the requirements for the Degree of Master of Science in Geospatial Technologies.Recent innovations in geospatial technology have dramatically increased the utility and ubiquity of cartographic interfaces and spatially-referenced content on the web. Capitalizing on these developments, the Farm2.0 system demonstrates an approach to manage user-generated geocontent pertaining to European protected designation of origin (PDO) food products.Wordpress, a popular open-source publishing platform, supplies the framework for a geographic content management system, or GeoCMS, to promote PDO products in the Spanish province of Valencia. The Wordpress platform is modified through a suite of plug-ins and customizations to create an extensible application that could be easily deployed in other regions and administrated cooperatively by distributed regulatory councils. Content, either regional recipes or map locations for vendors and farms, is available for syndication as a GeoRSS feed and aggregated with outside feeds in a dynamic web map

Earth Observation Open Science and Innovation

geospatial analytics; social observatory; big earth data; open data; citizen science; open innovation; earth system science; crowdsourced geospatial data; citizen science; science in society; data scienc

New Generation Platforms for Exploration of Crowdsourced Geo-Data

This chapter addresses two recent topics in the field of geo-information, the former more technological and the latter more scientific. On one side, there is an emerging trend of visualizing data and their changes in space and time through multidimensional geospatial clients and/or virtual globes. In the most advanced cases, these are not simply plain viewers but also allow analysis of the data by acting as “multidimensional intelligent geo-viewers”. On the other side, citizen science is providing a great momentum to the possibility of lay people taking part in scientific development. It is a new, citizen-centred paradigm which, in most cases, takes advantage of the individual and collective augmented capability of sensing the surrounding world through the sensors that we wear. The “citizen sensors” will consciously contribute to this development, either through volunteered geographic information or by being themselves an unconscious part of the data analytics, which makes use of geo-crowdsourced data to extract information in order to create a higher level understanding of natural and manmade phenomena. This chapter seeks to outline the Web technological solutions for visualizing and analyzing such data, through a summary of the current state of the art and the original applications developed by the authors