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Tag Archive 'VGI'

A paper for the Academic Track of the State of the Map Conference, Milan, has been accepted. We are looking forward to discuss with you following aspects:
A growing number of studies analyzes OSM data, its contributors, usage, and quality.
Such studies were mostly limited to analyzing either small samples of the OSM database or to simple [...]

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We cordially invite everybody interested to our next open GIScience colloquium talk
The speaker is Dr. Nama Budhathoki
Executive Director of Kathmandu Living Labs
When: Monday 26.03.2018, 14:00 st
Where: INF 348, room 015 (Institute of Geography, Heidelberg University)
Abstract:
Production and use of information during emergencies: Experience from 2015 Nepal Earthquake
Information is one of the most critical infrastructures in times of [...]

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VGI-ALIVE - AnaLysis, Integration, Vision, Engagement
Tuesday 12th June 2018, Lund, Sweden, Workshop at AGILE 2018
Introduction to the VGI-ALIVE Workshop
The steady rise of data volume shared on already established and new Volunteered Geographic Information (VGI) and social media platforms calls for advanced analysis methods of user contribution patterns, leads to continued challenges in data fusion, and provides also new opportunities [...]

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Currently Heidelberg University is establishing a PhD Research Training Group (RTG)
on “Big Data Research“ together with several Indian Universities and Indian Institutes of Technology (IIT) in the context of the HGS MathComp at the IWR (Interdisciplinary Center for Scientific Computing). Partners from India include Allhabad University, Delhi University (MoU pending), Jawaharlal Nehru University, IIT Guwahati, [...]

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LandSense Innovation Challenge

The LandSense Team is thrilled to announce the first LandSense Innovation Challenge, which targets individuals, web-entrepreneurs, start-ups and SMEs coming from all participating H2020 countries, to present innovative IT solutions in addressing one of the three LandSense domains: Urban Landscape Dynamics, Agricultural Land Use, and Forest & Habitat Monitoring (learn more here).
The focus of this challenge [...]

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VGI-ALIVE - AnaLysis, Integration, Vision, Engagement
Tuesday 12th June 2018, Lund, Sweden, Workshop at AGILE 2018
Introduction to the VGI-ALIVE Workshop
The steady rise of data volume shared on already established and new Volunteered Geographic Information (VGI) and social media platforms calls for advanced analysis methods of user contribution patterns, leads to continued challenges in data fusion, and provides also new [...]

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The winter semester 2017/2018 just has ended last week at Heidelberg University. The final GIScience colloquium presentation was given by Ross Purves (University of Zurich). After his talk we had a good discussion with members of the GIScience Research Group about VGI research and beyond. Looking forward to continue this soon. Stay tuned for the [...]

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Call for Papers: ISPRS IJGI Special Issue “Volunteered Geographic Information: AnaLysis, Integration, Vision, Engagement (VGI-ALIVE)”
A special issue of ISPRS International Journal of Geo-Information (ISSN 2220-9964).
Deadline for manuscript submissions: 30 November 2018
The steady rise of data volume shared on already-established and new Volunteered Geographic Information (VGI) and social media platforms calls for advanced analysis methods of user contribution [...]

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VGI-ALIVE
- AnaLysis, Integration, Vision, Engagement
Tuesday 12th June 2018, Lund, Sweden, Workshop at AGILE 2018
Introduction to the VGI-ALIVE Workshop
The steady rise of data volume shared on already established and new Volunteered Geographic Information (VGI) and social media platforms calls for advanced analysis methods of user contribution patterns, leads to continued challenges in data fusion, and provides also new opportunities [...]

Read Full Post »

Recently, deep learning has been widely applied in pattern recognition with satellite images. Deep learning techniques like Convolutional Neural Network and Deep Belief Network have shown outstanding performance in detecting ground objects like buildings and roads, and the learnt deep features are further applied in some prediction tasks like poverty and population mapping. On the [...]

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