[Geo4All] DeepVGI – Deep Learning Volunteered Geographic Information - Combining OpenStreetMap, MapSwipe and Remote Sensing

[labrinos at eled.auth.gr]

Dear Alexander,

As the new issue of our Newsletter is about to be published, I would  
like to ask your permission to publish the small article you sent a  
few days ago (I don't know why I just received it!) about DeepVGI. I  
think that it is very interesting.
Please let me know as soon as possible.
The article will be published under "Articles" section of the newsletter.
In this case please let me know about your position and affiliation  
and/or any other contact information.

Thank you
Nikos Lambrinos
Chief Editor



Παραθέτοντας από Alexander Zipf <zipf at uni-heidelberg.de>:

> DEEPVGI – DEEP LEARNING VOLUNTEERED GEOGRAPHIC INFORMATION       -
> COMBINING OPENSTREETMAP, MAPSWIPE AND REMOTE SENSING
>
> Deep learning techniques,       esp. Convolutional Neural Networks (CNNs),
> are now widely studied       for predictive analytics with remote sensing
> images, which can be       further applied in different domains for ground
> object detection,       population mapping, etc. These methods usually
> train predicting       models with the supervision of a large set of
> training examples.       However, finding ground truths especially for
> developing and rural       areas is quite hard and manually labeling a
> large set of training       data is costly. On the other hand Volunteered
> Geographic       Information (VGI) (e.g., OpenStreetMap (OSM) and MapSwipe)
> which       is the geographic data provided voluntarily by
> individuals, provides a free approach for such big data.
>
> In our project "DeepVGI",       we study predictive analytics methods with
> remote sensing images,       VGI, deep neural networks as well as other
> learning algorithms. It       aims at deeply learning from satellite
> imageries with the       supervision of such Volunteered Geographic
> Information.
>
> VGI data from OpenStreetMap       (OSM) and the mobile crowdsourcing
> application MapSwipe which       allows volunteers to label images with
> buildings or roads for       humanitarian aids are utilized. We develop an
> active learning       framework with deep neural networks by incorporating
> both VGI data       with more complete supervision knowledge. Our
> experiments show       that DeepVGI can achieve high building detection
> performance for       humanitarian mapping in rural African areas.
>
> Figure 1 shows some initial       results of DeepVGI, where OpenStreetMap
> and MapSwipe data are       utilized for training together with multi-layer
> artificial neural       networks and a VGI-based active learning strategy
> proposed by us.       DeepVGI outperforms Deep-OSM (i.e. deep models
> trained with only       OpenStreetMap data), and achieves close accuracy to
> the       volunteers.
>
>  
> Figure 1: Initial Results of DeepVGI
>
> On the other hand, such       predictive analytics methods will be applied
> in geographic       applications like humanitarian mapping. It can help
> improve VGI       data quality, save volunteers’ time, etc. DeepVGI is
> also an       attempt to explore the interaction between human beings and
>     machines, between crowdsourcing and deep learning. Figure 2 shows
> the research framework of DeepVGI project, where we will first       focus
> on learning and prediction between deep neural networks and       big
> spatial data (including VGI data from our history OSM       project).
>
>  
> Figure 2 shows the overal Research Framework of DeepVGI
>
> Further details will be       made available soon. DeepVGI is a project of
> the HeiGIT         Big Spatial Data Analytics[1] in cooperation with
> the Humanitarian         VGI group[2] at       HeiGIT. The Heidelberg
> Institute for Geoinformation Technology       (HeiGIT) is currently being
> established with core funding by the       Klaus Tschira Stiftung (KTS)
> Heidelberg.
>
> http://www.geog.uni-heidelberg.de/gis/deepvgi_en.html
>
>   GIScience Research Group Heidelberg University
> http://uni-heidelberg.de/gis  https://www.facebook.com/GIScienceHeidelberg
>   twitter.com/GIScienceHD
>
> Σύνδεσμοι:
> ----------
> [1] http://www.geog.uni-heidelberg.de/gis/heigit_bigspatialdata_en.html
> [2] http://www.geog.uni-heidelberg.de/gis/heigit_disastermanagement_en.html



-- 
Δρ. Νίκος Λαμπρινός
Καθηγητής της Διδασκαλίας της Γεωγραφίας
Τμήμα Δημοτικής Εκπαίδευσης
Α.Π.Θ. 54124 Θεσσαλονίκη
Τηλ.: 2310 991201 / 991230
Email: labrinos at eled.auth.gr
Web Page:       http://users.auth.gr/labrinos/
		http://www.digital-earth.edu.gr/
		https://www.auth.gr/univUnits

---------------------------------------------------------------------
Dr. Nikos Lambrinos
Professor of Geography Teaching
Director of Digital Analysis and Educational Design Laboratory
President of the Hellenc digital earth Centre of Excellence
Faculty of Education
School of Primary Education
Dept. of Science and New Technologies
Aristotle University of Thessaloniki
GR-54124 Thessaloniki, Greece
Tel: +30 2310 991201
Email: labrinos at eled.auth.gr
Web Page:       http://users.auth.gr/labrinos/
		http://www.digital-earth.edu.gr/
		https://www.auth.gr/en/univUnits