Feed on
Posts
Comments

Tag Archive 'machine-learning'

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 »

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 [...]

Read Full Post »

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. [...]

Read Full Post »

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 [...]

Read Full Post »

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 [...]

Read Full Post »

Last week our team member Dr. Michael Schultz successfully defended his PhD Defence about
Tropical deforestation monitoring using Landsat time series and breakpoint detection
at Wageningen University and Research (WUR). It was supervised by Prof. Dr M. Herold (Professor of Geo-information Science and Remote Sensing at Wageningen University & Research). We congratulate cordially! Keep up the good [...]

Read Full Post »

Our paper about Deep Learning from Multiple Crowds: A Case Study of Humanitarian Mapping is available online now.
Satellite images are widely applied in humanitarian mapping which labels buildings, roads and so on for humanitarian aid and economic development. However, the labeling now is mostly done by volunteers. In a recently accepted study, we utilize deep learning [...]

Read Full Post »

Heidelberg Alumni USA (HAUS) is the alumni initiative for all former students, lecturers, professors or staff of Heidelberg University who live or work in the US.
HAUS was born in New York City, and that’s where we will celebrate the tenth anniversary the weekend of September 14-16, 2018. We hope you will be there for this [...]

Read Full Post »

Another chapter in machine human fusion land use device narrative: new Sentinel 2 osmlanduse.org product results based on OpenStreetMap plus Sentinel 2 data plus Machine Learning were presented at ToulouseSpaceShow 2018 during a European Space Agency (ESA) Research and User Support (RUS) event. Stay tuned: The new product will soon be available for all [...]

Read Full Post »

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 remote [...]

Read Full Post »

Older Posts »