Machine Learning, Deep Learning and Humanitarian Mapping

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Machine Learning, Deep Learning and Humanitarian Mapping

Benjamin Herfort-2

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 or the rapId editor 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 and the GIScience Research Group we investigated the potential of Deep Learning in combination with MapSwipe’s crowdsourcing approach. If this sounds interesting to you, you can find out more at the blogpost 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. 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 or join the MapSwipe working group.

Have a nice day,


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