The other week Yingjie Hu and myself co-organized a session entitled "Spatial Data Science for Disaster Resilience" as part for the 4th Spatial Data Science Symposium (SDSS 2023).
Session Abstract:
Natural disasters, such as hurricanes, floods, tornados, wildfires, earthquakes, and blizzards, pose significant threats to people and society. The availability of various geospatial data sources (e.g., drone-collected images, mobile phone location data, social media data, and sensor network data) combined with the advancement of statistical and machine learning models provide great opportunities for understanding human-environment interactions during these catastrophic events. This session aims to bring together researchers interested in using spatial data science to answer questions and address issues in any aspect related to disaster management.
Talks in the session:
- Lei Zou (keynote):
- Achieving a Smart and Resilient Future with Spatial Data Science.
- Qunying Huang:
- Wildfire Burnt Area Detection with Deep Learning and Sentinel2 Imagery.
- Manzhu Yu:
- Deciphering Wildfire Dynamics: Spatiotemporal Attention-Based Sequence-to-Sequence Models Using ConvLSTM Networks.
- Md Zakaria Salim:
- Socio-economic Disparities of Property Damage in Hurricane Ian.
- Qingqing Chen:
- Community Resilience to Wildfire: A Network Analysis Approach by Utilizing Human Mobility Data.
- Kai Sun:
- GALLOC: a GeoAnnotator for Labeling LOCation Descriptions from Disaster-related Text Messages.
If these talks sound of interest and as this was a online and distributed event, the main organizers of the Symposium have made all the talks available online. The talks from our session can be seen below and all the other talks and seasons from the symposium at large can be found here.
No comments:
Post a Comment