Feed on
Posts
Comments

Tag Archive 'deep learning'

After more than a decade of rapid development of volunteered geographic information (VGI), VGI has already become one of the most important research topics in the GIScience community. Almost in the meantime, we have witnessed the ever-fast growth of geospatial deep learning technologies to develop smart GIServices and to address remote sensing tasks, for instance, [...]

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 »

We are pleased to share that because of the response to our work, ISPRS IJGI selected our paper on Detecting Graffiti with Street View Images and Deep Learning to be highlighted as a title story through some graphics on the journals main page.

Novack T, Vorbeck L, Lorei H, Zipf A. (2020): Towards Detecting Building Facades with [...]

Read Full Post »

As a recognized type of art, graffiti is a cultural asset and an important aspect of a city’s aesthetics. As such, graffiti is associated with social and commercial vibrancy and is known to attract tourists. However, positional uncertainty and incompleteness are current issues of open geo-datasets containing graffiti data. In a newly published paper, we [...]

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 »

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 Volunteered [...]

Read Full Post »

In a new publication, we show how deep neural networks can be used in an end-to-end manner for the classification of 3D point clouds from airborne laser scan data. The research, based on the award-winning diploma thesis of Lukas Winiwarter at TU Wien, has now been published in “PFG - Photogrammetrie, Fernerkundung, Geoinformation“, the Journal [...]

Read Full Post »

Our new paper on Machine Learning and Humanitarian Mapping
Nowadays, Machine Learning and Deep Learning approaches are steadily gaining popularity within the humanitarian (mapping) community. New tools such as the ML Enabler or the rapId editor might change the way crowdsourced data is produced in the future. Hence, at the Heidelberg Institute for Geoinformation Technology and [...]

Read Full Post »

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 the [...]

Read Full Post »

Older Posts »