Search results for: “machine learning”
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Job Offer: “Lead: Geo Machine Learning for Good” Senior Spatial Data Science Expert (m, f, d) 100% permanent, HeiGIT gGmbH
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…
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Job Offer: “Lead: Geo Machine Learning for Good”, Senior Spatial Data Science Expert (m, f, d), 100%, permanent, HeiGIT gGmbH
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…
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Stellenausschreibung Geoinformatik Heidelberg – Project GeCO: Generating high-resolution CO2 maps by Machine Learning-based geodata fusion
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…
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New Project: GeCO: Generating high-resolution CO2 maps by Machine Learning-based geodata fusion and atmospheric transport modelling
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…
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Machine Learning for Space and Earth Observation Data (ML-SEOD) 2019 at ICCSA
Machine Learning for Space and Earth Observation Data (ML-SEOD) 2019 Call for Papers The Earth and Space environments are being monitored by an unprecedented amount of sensors: Earth observation satellites, sensor networks, telescopes working in different wavelengths, human records of Earth and Space events, etc. This generates a huge amount of raw data that must…
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Mapping cultural ecosystem services 2.0 – using machine learning annotated photos to learn how humans perceive landscapes and ecosystems
Ecosystems provide many different services to mankind. Services provided depend on how the land is used – land use decisions lead to trade-offs that with respect to the ecosystem service provided by ecosystems and landscapes. This trade-offs vary in many situations in space and time. Therefore, it is essential to quantify and map service provisioning…
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OSMlanduse.org + Remote Sensing = New conterminous land use data set for Germany released – filling gaps in OSM through machine learning
Land use data created by humans (OSM) was fused with satellite remote sensing data, resulting in a conterminous land use data set without gaps. The first version is now available for all Germany at OSMlanduse.org. When human input (OSM data) was absent a machine generated missing land use information learning from human inputs and using…
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GeoAI4Water 2 Applies Deep Learning to Detect Critical Infrastructure for Disaster Planning
Context During natural disasters and other catastrophic events, quickly identifying and distributing resources is essential. Even a brief delay in evacuating residents or sourcing water can significantly hamper relief efforts and prevent effective disaster management. The HeiGIT and GIScience teams have been actively designing tools to assist in disaster preparation and response, from working with partners to build community resilience to activating our disaster…
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Job Offer: Deep Learning Engineer (m/f/d, up to 100%)
Are you a highly motivated individual who loves designing and developing machine learning and deep learning systems? 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…
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Online seminar ‘Deep learning from Volunteered Geographical Information: a case study of humanitarian mapping with OpenStreetMap’
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,…
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Automatic mapping of national surface water with OpenStreetMap and Sentinel-2 MSI data using deep learning
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…
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E-TRAINEE project: E-learning course on Time Series Analysis in Remote Sensing
The E-TRAINEE project is a new collaboration project for developing an “E-learning course on Time Series Analysis in Remote Sensing for Understanding Human-Environment Interactions” with Markéta Potůčková (Department of Applied Geoinformatics and Cartography, Charles University Prague) as PI of the project and Heidelberg University, University of Innsbruck and University of Warsaw as project partners. The…