Tag: remote sensing
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Contiguous high resolution OSMlanduse map of the European Union by combining Copernicus data and OpenStreetMap
Find here a new update of the OSMlanduse.org map. By injecting known tags provided by OpenStreetMap (OSM) into a remote sensing feature space using deep learning, tags were predicted when absent thus creating a contiguous map – initially for the member states of the EU. By design our method can be applied when- and wherever…
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Going green with MeinGrün – Today App launch in Heidelberg and Dresden
Today the time has come: The “meinGrün” web app for Dresden and Heidelberg is officially launched. With the mobile application you can (re-)discover known and unknown green spaces and find a pleasant route to those. Users can learn about the functions of the app via virtual scavenger hunt. The app is the result of the…
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GIScience Heidelberg at Annual Meeting of DGPF
Last week, members of GIScience Heidelberg participated in the 40th annual meeting of the DGPF (German association for photogrammetry and remote sensing) in Stuttgart, Germany. Presentations were contributed by PhD students Lukas Winiwarter and Katharina Anders (3DGeo Research Group), as well as former GIScience fellow Jun-Prof. Dr. René Westerholt. Read up on the presented topics…
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NASA uses Openrouteservice for study on disaster response times
According to a recent post by NASA, researchers at NASA’s Goddard Space Flight Center in Greenbelt, Maryland, calculated the time that could have been saved if ambulance drivers and other emergency responders had near-real-time information about flooded roads, using the 2011 Southeast Asian floods as a case study. This was the first NASA study to…
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New DFG project: IdealVGI – Deep Learning with OSM
Recently a new DFG project proposal was accepted to the GIScience Research Group Heidelberg within the DFG priority programme VisVGI (Volunteered Geographic Information: Interpretation, Visualisation and Social Computing” [SPP 1894]). It is joint collaboration project together with Prof. Begüm Demir from TU Berlin. IDEAL-VGI: Information Discovery from Big Earth Observation Data Archives by Learning from…
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MS Wissenschaft beendet Tour zur Künstlichen Intelligenz – aber weiter geht es im Web – auch mit unserem Exponat zu Trainingsdaten für Satellitenbilder
Gerade beendete die MS Wissenschaft ihre Tour durch 31 Städte zwischen Berlin und Wien in diesem Wissenschaftsjahr zum Thema “Künstliche Intelligenz“. 85.000 Menschen – Schulklassen, Familien und Interessierte aller Altersklassen – besuchten die Ausstellung zum Thema lernende Computersysteme an Bord des Wissenschaftsschiffs. Zu den Besonderheiten der Ausstellung zählten die zahlreiche Dialog- und Mitmachangebote an Bord.…
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Estimating OpenStreetMap Missing Built-up Areas using Pre-trained Deep Neural Networks
Recently a new paper about Estimating OpenStreetMap Missing Built-up Areas using Pre-trained Deep Neural Networks (DNNs) has been presented at the AGILE GIScience conference 2019 in Cyprus. Although built-up areas cover only a small proportion of the earth’s surface, these areas are closely tied to most of the world’s population and the economic output, which makes…
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Comparison of Three Algorithms for the Evaluation of TanDEM-X Data for Gully Detection in Krumhuk Farm (Namibia)
Namibia is a dry and low populated country highly dependent on agriculture, with many areas experiencing land degradation accelerated by climate change. One of the most obvious and damaging manifestations of these degradation processes are gullies, which lead to great economic losses while accelerating desertification. The development of standardized methods to detect and monitor the…
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Multisource and Multitemporal Data Fusion in Remote Sensing: A Comprehensive Review of the State of the Art
The recent, sharp increase in the availability of data captured by different sensors, combined with their considerable heterogeneity, poses a serious challenge for the effective and efficient processing of remotely sensed data. Such an increase in remote sensing and ancillary data sets, however, opens up the possibility of utilizing multimodal data sets in a joint…
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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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Dokumentation zur diesjährigen Fachtagung Katastrophenvorsorge ist online
Die Fachtagung Katastrophenvorsorge wird jedes Jahr vom Deutschen Roten Kreuz organisiert, mit dem Ziel eine Dialogplattform für verschiedene Akteuren der nationalen und internationalen Katastrophenvorsorge zu schaffen. In Kooperation mit dem Bundesamt für Bevölkerungsschutz und Katastrophenhilfe (BBK) und dem Zentrum für satellitengestützte Kriseninformation (ZKI), haben wir vom HeiGIT den Workshop „Geodaten in der Katastrophenvorsorge“ durchgeführt. Anhand…