Novel Sources of Geographical Data and Old Planning Problems: New Challenges and Novel Approaches

A special issue of Future Internet (ISSN 1999-5903). This special issue belongs to the section "Internet of Things".

Deadline for manuscript submissions: closed (31 July 2022) | Viewed by 3337

Special Issue Editors


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Guest Editor
Consumer Data Research Centre, Department of Geography, University College London, UCL, London, UK
Interests: spatial modelling; spatial analysis; visualization; spatial data; geospatial science; computational social sciences; maritime big data; epidemiology; geodemographics
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Guest Editor
School of Engineering, University of Basilicata, Viale dell’Ateneo Lucano 10, 85100 Potenza, Italy
Interests: spatial planning; spatial simulation; geodemographics; geographic data analysis of socioeconomic and population data; planning 2.0; participation 2.0; e-democracy; e-participation
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Following the success of our Special Issue on “NeoGeography and WikiPlanning” (launched in 2011), we are calling for a new issue. Turner in 2006 wrote: “Neogeography means ‘new geography’ and consists of a set of techniques and tools that fall outside the realm of traditional GIS, Geographic Information Systems.” More than a decade passed and all the tools and techniques that were a novelty then, are now an integral part of spatial analysis and planning.

Turner’s definition of NeoGeography helps in defining a transition period in the history of GIS, a period where Desktop GIS lost its central role in favour of different architectures and platforms. Nowadays, Desktop GIS is part of a more complex technical infrastructure that leverages distributed computational and storage power. The techniques have increased their computational load and the data have increased in size and availability, demanding fast and scalable storage architectures.

With more analytical techniques, more data, and more power we are now able to answer old and new questions on the nature of geography, as well as to propose planning policies adequate to the times and consistent with the huge amount of data and tools to-date available. But as scholars we must not forget that the scientific credibility of such answers depends on the same techniques and data we used as inputs. While one can learn techniques, knowledge of the data is contextual, subjective, and anchored to the semantic domain of application and research. Knowing the data, however, it is not enough. The lack of official sources impacts the validation process and requires alternative datasets or data driven approaches. Now more than ever, we need new research, new tools, and new models to understand and organise the space around us.

This Special Issue welcomes contributions from scholars coming from different fields of expertise in the fields of the different sciences applied to space and ICT, willing to enhance the understanding and knowledge of the possibilities and opportunities given by coupling consolidated theories and disciplines with the potentials of technologies to solve spatial issues.

Dr. Maurizio Gibin
Prof. Dr. Beniamino Murgante
Prof. Dr. Giuseppe Borruso
Guest Editors

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Keywords

  • neogeography
  • volunteered geographic information
  • crowdsourcing
  • collaborative mapping
  • WikiCities
  • wikinomics
  • GeoDesign
  • planning 2.0
  • participation 2.0
  • urban social networks
  • participatory GIS
  • geography
  • SDI and planning
  • ontologies for urban planning
  • smart cities
  • resilient cities
  • VGI VS SDI
  • wikinomics
  • socialnomics
  • WikiCities
  • WikiPlanning
  • geospatial data science

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Published Papers (1 paper)

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20 pages, 42672 KiB  
Article
ReFuse: Generating Imperviousness Maps from Multi-Spectral Sentinel-2 Satellite Imagery
by Giovanni Giacco, Stefano Marrone, Giuliano Langella and Carlo Sansone
Future Internet 2022, 14(10), 278; https://doi.org/10.3390/fi14100278 - 28 Sep 2022
Cited by 3 | Viewed by 2347
Abstract
Continual mapping and monitoring of impervious surfaces are crucial activities to support sustainable urban management strategies and to plan effective actions for environmental changes. In this context, impervious surface coverage is increasingly becoming an essential indicator for assessing urbanization and environmental quality, with [...] Read more.
Continual mapping and monitoring of impervious surfaces are crucial activities to support sustainable urban management strategies and to plan effective actions for environmental changes. In this context, impervious surface coverage is increasingly becoming an essential indicator for assessing urbanization and environmental quality, with several works relying on satellite imagery to determine it. However, although satellite imagery is typically available with a frequency of 3–10 days worldwide, imperviousness maps are released at most annually as they require a huge human effort to be produced and validated. Attempts have been made to extract imperviousness maps from satellite images using machine learning, but (i) the scarcity of reliable and detailed ground truth (ii) together with the need to manage different spectral bands (iii) while making the resulting system easily accessible to the end users is limiting their diffusion. To tackle these problems, in this work we introduce a deep-learning-based approach to extract imperviousness maps from multi-spectral Sentinel-2 images leveraging a very detailed imperviousness map realised by the Italian department for environment protection as ground truth. We also propose a scalable and portable inference pipeline designed to easily scale the approach, integrating it into a web-based Geographic Information System (GIS) application. As a result, even non-expert GIS users can quickly and easily calculate impervious surfaces for any place on Earth (accuracy >95%), with a frequency limited only by the availability of new satellite images. Full article
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