[HOT] Machine Learning, Deep Learning and Humanitarian Mapping

[Benjamin Herfort]

Hey all,

We've been discussing machine learning and deep learning approaches 
quite often in the last couple weeks. New tools such as the ML Enabler 
<https://medium.com/devseed/ml-enabler-completing-the-machine-learning-pipeline-for-mapping-3aae94fa9e94> 
or the rapId editor <https://github.com/facebookincubator/RapiD> might 
change the way crowdsourced data is produced in the future. I guess, 
that these will also be heavily discussed during this year's HOT summit 
and state of the map conference (come to Heidelberg! ;)).

Since I'm working at a research institution, I wanted to share another 
piece of (scientific) work on that topic. Together with colleagues from 
the Heidelberg Institute for Geoinformation Technology 
<https://heigit.org/> and the GIScience Research Group 
<https://www.geog.uni-heidelberg.de/gis/index_en.html> we investigated 
the potential of Deep Learning in combination with MapSwipe’s 
<http://mapswipe.org/> crowdsourcing approach. If this sounds 
interesting to you, you can find out more at the blogpost 
<http://k1z.blog.uni-heidelberg.de/2019/07/31/mapping-human-settlements-with-higher-accuracy-and-less-volunteer-efforts-by-combining-crowdsourcing-and-deep-learning/>I 
wrote or directly look at our paper:

Herfort, B.; Li, H.; Fendrich, S.; Lautenbach, S.; Zipf, A. Mapping 
Human Settlements with Higher Accuracy and Less Volunteer Efforts by 
Combining Crowdsourcing and Deep Learning 
<https://www.mdpi.com/2072-4292/11/15/1799>. /Remote Sens./ *2019*, 
/11/, 1799.

Together with the broader MapSwipe community we are currently working on 
new project types for MapSwipe as well. This will also allow us to 
integrate machine learning results better into our existing 
crowdsourcing workflows. Sounds Interesting?Check out MapSwipe’s Github 
repositories <https://github.com/mapswipe> or join the MapSwipe working 
group.

Have a nice day,

Benni


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