Category: VGI Group
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A discussion of crowdsourced geographic information initiatives and big Earth observation data architectures for land-use and land-cover monitoring
The effective monitoring of land-use and land-cover changes (LULCC) is a basic requirement for understanding socio-enviromental processes of local to global scales. Remote sensing data and methods have long been established as the most effective approach for monitoring LULCC. The potential for further increasing the effectiveness of this approach is proportional to the astonishingly large…
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HeiGIT/GIScience @ISCRAM 2018 Rochester- Exchange of latest crisis management practice and innovative ideas near a natural wonder of the world
Rochester NY, a small city located at Lake Ontario, became a gathering place for the international ISCRAM community last week. Researchers and practitioners from over 20 countries presented their latest work, ideas and needs related to crisis management at the 15th ISCRAM conference. Crisis and humanitarian management being one of the main focuses of the…
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Ohsome for Street Network Analysis and Disaster Activation Monitoring
Disaster mapping activations that are supported by many volunteers with various levels of experience raise questions related to the quality of the provided Volunteered Geographic Information. Learning about the data quality that can be expected in a disaster activation helps to evaluate the quality and fitness for purpose of the OSM data. At the ISCRAM…
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Identifying the Effects of Mobility Domains on Volunteered Geographical Information: Towards an Analytical Approach
The production of Volunteered Geographic Information (VGI) is a type of human behavior which emerges via direct and indirect interactions with the physical environments described by these data. The nature of these interactions and the extent to which they rely on physical presence in the mapped area may affect the quality of the resulting digital…
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Associating OpenStreetMap tags to CORINE land-cover classes using text and semantic similarity measures
With the aim of rapidly estimating the updated state of the CORINE land-cover map at the frequency with which the OpenStreetMap (OSM) dataset is edited and extended, we propose an approach for automatically associating widely used OSM tags to Level 1 and Level 2 CORINE land-cover classes. This association is probabilistic and is undertaken based…
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Enhancing Crowdsourced Classification on Human Settlements Utilizing Logistic Regression Aggregation and Intrinsic Context Factors
Among semi-automated methods and pre-processed data products, crowdsourcing is another tool which can help to collect information on human settlements and complement existing data, yet it’s accuracy is debated. Whereas the potential of crowdsourced datasets for training of machine learning algorithms has been explored recently, only few work has been done towards utilizing machine learning…
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Let’s meet at SOTM 2018 :: Paper on OSM History Analytics Accepted in Academic Track
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…
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Towards Using the Potential of OpenStreetMap History for Disaster Activation Monitoring
Over the last years, the growing OpenStreetMap (OSM) database repeatedly proved its potential for various use cases, including disaster management. Disaster mapping activations show increasing contributions, but oftentimes raise questions related to the quality of the provided Volunteered Geographic Information (VGI). In order to better monitor and understand OSM mapping and data quality, HeiGIT developed…
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Preview of the ohsome Nepal dashboard
The ohsome OpenStreetMap history analytics platform, which is currently developed at HeiGIT, will make OSM’s full-history data more easily accessible. We are pleased to announce that we are coming closer to reaching our objectives, hereby sharing a preview of the first ohsome web dashboard. Our dashboards will allow you to explore OSM full-history data using…
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Introducing the ohsome OSM History Analytics Platform
The big spatial data analytics team at HeiGIT is currently developing the ohsome OpenStreetMap history analytics platform. Our aim is to make OSM’s full-history data more easily accessible for various kinds of data analytics tasks on a global scale. OpenStreetMap (OSM) is a freely available map of the world to which everyone may contribute geographic…