Feed on
Posts
Comments

Tag Archive 'machine-learning'

This week 19.-23.10. the autumn school Urban Data Science takes place as a online course set up together by GIScience Heidelberg and the Institute for Transport Studies (IfV), KIT. It is part of an ongoing application for a HeiKA (Heidelberg Karlsruhe Strategic Partnership) project that would foster joint teaching modules between GIScience HD and IfV [...]

Read Full Post »

We launched a validation campaign of our new 10meter resolution OSMlanduse product for the member states of the European Union. Please contribute to the validation here. A technique where contributions are checked against each other is implemented to promote quality of information. The mapathon comes in four themes: nature, urban, agriculture or expert.
While the expert campaign [...]

Read Full Post »

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 [...]

Read Full Post »

Semantically rich maps are the foundation of indoor location‐based services. Many map providers such as OpenStreetMap and automatic mapping solutions focus on the representation and detection of geometric information (e.g., shape of room) and a few semantics (e.g., stairs and furniture) but often still neglect room usage. To mitigate the issue, a new published paper [...]

Read Full Post »

Multi-sensor remote sensing image classification has been considerably improved by deep learning feature extraction and classification networks. In this recent paper, we propose a novel multi-sensor fusion framework (CResNet-AUX) for the fusion of diverse remote sensing data sources. The novelty of this paper is grounded in three important design innovations:

A unique adaptation of the [...]

Read Full Post »

Accurate and detailed geographical information digitizing human activity patterns plays an essential role in response to natural disasters. Volunteered geographical information, in particular OpenStreetMap (OSM), shows great potential in providing the knowledge of human settlements to support humanitarian aid, while often the availability and quality of OSM remains a major concern. The majority of existing [...]

Read Full Post »

Recap: Keynote on Smart Cities

Already in October 2019 Prof. Zipf was invited to give a keynote on “User Generated Geoinformation for Smart Cities” at the “Smart Cities, Smart Data, Smart Governance” ISPRS Conference at CEPT University in Ahmedabad (known for the Gandhi-Ashram), where he also participated as speaker in the inaugural session and acted as session chair for a [...]

Read Full Post »

The Center for Spatial Studies, Department of Geography at the University of California, Santa Barbara is hosting the Spatial Data Science Symposium 2019 this coming week with the title
“Setting the Spatial Data Science Agenda”
Over 40 selected participants will gather to discuss the future of Spatial Data Science at this expert meeting. Instead of being [...]

Read Full Post »

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.
Mit an Board [...]

Read Full Post »

Seit einiger Zeit findet sich das gemeinsame Exponat des HeiGIT und des Alfred-Wegener-Instituts Helmholtz-Zentrum für Polar- und Meeresforschung für die Ausstellung “Künstliche Intelligenz” auf der “MS Wissenschaft” auch auf dem Webportal zum Wissenschaftsjahr 2019.
Das Thema “Künstliche Intelligenz” des Wissenschaftjahres 2019 wird dabei an zwei Beispielen aufgegriffen. Diese zeigen wie jedermann durch das Erzeugen von Trainingsdaten [...]

Read Full Post »

Older Posts »