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Tag Archive 'deep learning'

Humans rely on clean water for their health, well-being, and various socio-economic activities. During the past few years, the COVID-19 pandemic has been a constant reminder of about the importance of hygiene and sanitation for public health. The most common approach to securing clean water supplies for this purpose is via wastewater treatment. To date, [...]

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We invite to the upcoming online seminar at the Urban Analytics Lab seminar series at the National University of Singapore (NUS):
‘Deep learning from Volunteered Geographical Information: a case study of humanitarian mapping with OpenStreetMap’
on 29 April (9am German time, 3pm Singapore time)
By Hao Li, GIScience Research Group, Heidelberg University @GIScienceHD
As an emerging topic, OpenStreetMap [...]

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OpenStreetMap (OSM) has been intensively used to support humanitarian aid activities, especially in the Global South. Its data availability in the Global South has been greatly improved via recent humanitarian mapping campaigns and due to the efforts of local communities. However, large rural areas are still incompletely mapped. The timely provision of map data is [...]

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Job advertisement HeiGIT gGmbH
Do you want to use your machine learning expertise for the benefit of society and the environment? Do you want to improve the availability and quality of geospatial data and further develop geoinformatics methods used for open, non-profit applications in the field of sustainability, mobility and humanitarian aid? That’s our mission too!
HeiGIT [...]

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Du willst Deine Machine Learning Kompetenz zum Wohle der Gesellschaft und Umwelt einsetzen? Du willst die Verfügbarkeit und Qualität von Geodaten verbessern und geoinformatische Methoden weiterentwickeln, die für offene, gemeinnützige Anwendungen im Bereich Nachhaltigkeit, Mobilität und humanitäre Hilfe eingesetzt werden? Das ist auch unsere Mission!
Die HeiGIT gGmbH ist ein forschungsorientiertes, gemeinnütziges Start-up mit den Zielen [...]

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Stellenausschreibung Universität Heidelberg – GIScience
Wissenschaftliche Mitarbeiter:in Geoinformatik - Projekt GeCO

GeCO: Generating high-resolution CO2 maps by Machine Learning-based geodata fusion
Du hast Interesse an Klimawandel, Treibhausgasemissionen und innovativen Geoinformatik-Methoden?
Im Rahmen des vom Heidelberg Center for the Environment (HCE) durch die Exzellenzstrategie geförderten Kooperationsprojektes GeCO suchen wir baldmöglichst nach einer wissenschaftlichen Mitarbeiter:in (m/f/d). Die Abteilung Geoinformatik entwickelt [...]

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Recently a new project has been starting in the context of Climate Change Action research:
GeCO: Generating high-resolution CO2 maps by Machine Learning-based geodata fusion and atmospheric transport modelling
The spatiotemporal distribution of greenhouse gases and their sources on Earth has so far been considered mainly at relatively coarse resolutions. There is a lack of sound [...]

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Large-scale mapping activities can benefit from the vastly increasing availability of earth observation (EO) data, especially when combined with volunteered geographical information (VGI) using machine learning (ML). High-resolution maps of inland surface water bodies are important for water supply and natural disaster mitigation as well as for monitoring, managing, and preserving landscapes and ecosystems.
In a [...]

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The AGILE 2021 conference is taking place this week. It is the the 24rd AGILE conference on GIScience. AGILE is the Association of Geographic Information Laboratories in Europe and the 2021 conference is for the first time held as a virtual conference. As in earlier years GIScience Heidelberg and HeiGIT are contributing to the conference with several [...]

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Accurate and complete geographic data of human settlement is crucial for humanitarian aid and disaster response. OpenStreetMap (OSM) can serve as a valuable source, especially for global south countries where buildings are largely unmapped. In a previous blog, we introduced our recent work in detecting OpenStreetMap missing buildings, so this time we will show you [...]

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