Big Geospatial Data processing in the IQmulus Cloud

Remote sensing instruments are continuously evolving in terms of spatial, spectral and temporal resolutions and hence provide exponentially increasing amounts of raw data. These volumes increase significantly faster than computing speeds. All these techniques record lots of data, yet in different data models and representations; therefore, resulting datasets require harmonization and integration prior to deriving meaningful information from them. All in all, huge datasets are available but raw data is almost of no value if not processed, semantically enriched and quality checked. The derived information need to be transferred and published to all level of possible users (from decision makers to citizens). Up to now, there are only limited automatic procedures for this; thus, a wealth of information is latent in many datasets. This paper presents the first achievements of the IQmulus EU FP7 research and development project with respect to processing and analysis of big geospatial data in the context of flood and waterlogging detection

Developing a spatial data infrastructure for Rwanda : case study of land administration sector.

Thesis (M.Env.Dev.)-University of KwaZulu-Natal, Pietermaritzburg, 2007.Governments all over the world are being challenged to develop Spatial Data Infrastructure (SDI), to improve the access and use of spatial data for decision support and sustainable development. Therefore, SDI is part of the basic infrastructure that needs to be efficiently implemented and managed in the interest of any nation. The aim of this study, therefore, is to assess the feasibility of SDI implementation in Rwanda, using the Land administration geospatial data sector as a case study, given time constraints of the research. To achieve this, the concept of SDI is firstly explained in order to provide a common understanding of the concept. The new trends in the new Land Administration System of Rwanda, with emphasis on spatial data management are also presented. This information is generated from various written materials. Field work was also conducted by means of questionnaires, interviews and observation in attempt to assess Land Administration geospatial data, related assets and gaps with reference to SDI framework requirements. A situational analysis is carried out from the field work results. The research sets the scene providing the major findings. The main spatial data providers are public and based on national level. Land use and cadastral related spatial data are the least developed, and Land Administration application data are quiet non-existent. Various users, mainly decision makers, exist but lack effective access to data. A number of challenges, such as a high duplication of data collection and maintenance, lack of appropriate ways of data sharing, a shortage of human resources in Geo-information, absence of policies and regulations, are also found in the Land Administration spatial data sector. Nevertheless, the new Land Administration System orientations and national priorities in terms of information technology, offer a favourable environment for the implementation of SDI. Having considered this, the research proceeds to propose a Land Administration SDI prototype with its main application of Spatial Data discovery Facility, and then highlights its benefits. The research ends with a conclusion and recommendations for future research

Dynamic, interactive and visual analysis of population distribution and mobility dynamics in an urban environment using the mobility explorer framework

© 2017 by the authors. This paper investigates the extent to which a mobile data source can be utilised to generate new information intelligence for decision-making in smart city planning processes. In this regard, the Mobility Explorer framework is introduced and applied to the City of Vienna (Austria) by using anonymised mobile phone data from a mobile phone service provider. This framework identifies five necessary elements that are needed to develop complex planning applications. As part of the investigation and experiments a new dynamic software tool, called Mobility Explorer, has been designed and developed based on the requirements of the planning department of the City of Vienna. As a result, the Mobility Explorer enables city stakeholders to interactively visualise the dynamic diurnal population distribution, mobility patterns and various other complex outputs for planning needs. Based on the experiences during the development phase, this paper discusses mobile data issues, presents the visual interface, performs various user-defined analyses, demonstrates the application's usefulness and critically reflects on the evaluation results of the citizens' motion exploration that reveal the great potential of mobile phone data in smart city planning but also depict its limitations. These experiences and lessons learned from the Mobility Explorer application development provide useful insights for other cities and planners who want to make informed decisions using mobile phone data in their city planning processes through dynamic visualisation of Call Data Record (CDR) data

Adaptive main-memory indexing for high-performance point-polygon joins

Connected mobility applications rely heavily on geospatial joins that associate point data, such as locations of Uber cars, to static polygonal regions, such as city neighborhoods. These joins typically involve expensive geometric computations, which makes it hard to provide an interactive user experience. In this paper, we propose an adaptive polygon index that leverages true hit fltering to avoid expensive geometric computations in most cases. In particular, our approach closely approximates polygons by combining quadtrees with true hit filtering, and stores these approximations in a query-effcient radix tree. Based on this index, we introduce two geospatial join algorithms: an approximate one that guarantees a user-defined precision, and an exact one that adapts to the expected point distribution. In summary, our technique outperforms existing CPU-based joins by up to two orders of magnitude and is competitive with state-of-the-art GPU implementations

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

Training of Crisis Mappers and Map Production from Multi-sensor Data: Vernazza Case Study (Cinque Terre National Park, Italy)

This aim of paper is to presents the development of a multidisciplinary project carried out by the cooperation between Politecnico di Torino and ITHACA (Information Technology for Humanitarian Assistance, Cooperation and Action). The goal of the project was the training in geospatial data acquiring and processing for students attending Architecture and Engineering Courses, in order to start up a team of "volunteer mappers". Indeed, the project is aimed to document the environmental and built heritage subject to disaster; the purpose is to improve the capabilities of the actors involved in the activities connected in geospatial data collection, integration and sharing. The proposed area for testing the training activities is the Cinque Terre National Park, registered in the World Heritage List since 1997. The area was affected by flood on the 25th of October 2011. According to other international experiences, the group is expected to be active after emergencies in order to upgrade maps, using data acquired by typical geomatic methods and techniques such as terrestrial and aerial Lidar, close-range and aerial photogrammetry, topographic and GNSS instruments etc.; or by non conventional systems and instruments such us UAV, mobile mapping etc. The ultimate goal is to implement a WebGIS platform to share all the data collected with local authorities and the Civil Protectio