We invite to the upcoming online seminar at the Urban Analytics Lab seminar series at the National University of Singapore (NUS):
‘Deep learning from Volunteered Geographical Information: a case study of humanitarian mapping with OpenStreetMap’
on 29 April (9am German time, 3pm Singapore time)
By Hao Li, GIScience Research Group, Heidelberg University @GIScienceHD
As an emerging topic, OpenStreetMap (OSM) has been greatly improved via recent humanitarian mapping campaigns, and intensively used to support humanitarian aid activities, especially in sub-Saharan Africa. Considering its time-crucial nature, how to create timely and accurate maps of OSM missing features (e.g., buildings and roads) become a vital challenge. In our project DeepVGI, we study predictive analytics methods with remote sensing images, VGI, and social media data (e.g., Twitter) via advanced deep learning algorithms. The experiments results, especially in sub-Saharan Africa countries, demonstrate the capability of GeoAI in improving the OSM data completeness, and more importantly show great potential in supporting better and faster humanitarian mapping from a machine-assisted mapping perspective.