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Tag Archive 'machine-learning'

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 [...]

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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 [...]

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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 [...]

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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 [...]

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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 [...]

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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 [...]

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A paper investigating the relevance of (pre-calculated) features for 3D point cloud classification using deep learning was just published in the ISPRS Annals of Photogrammetry and Remote Sensing.
The study presents a non-end-to-end deep learning classifier for 3D point clouds using multiple sets of input features and compares it with an implementation of the state-of-the-art [...]

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Global Platform 2019 in Geneva

Creating maps helps humanity. Drawing maps together with communities is crucial for effective risk reduction interventions, ensuring no one is left behind.
The progress of the implementation of the targets set by the Sendai Framework for Disaster Risk Reduction (DRR) have been key discussion points during this years Global Platform in Geneva. [...]

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We are always happy to support citizen science projects at the HeiGIT. HeiGIT/ GIScience efforts already range from tools that assess the data quality of citizen science projects (see, e.g., this blog post about “Plausible Parrots“) to approaches related to data creation, like MapSwipe Analytics (learn more here).
Currently, we are supporting citizen science approaches towards [...]

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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 be processed [...]

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