SDSS 2023: Session 2: Spatial Data Science for Disaster Resilience
Channel: Spatial Data Science Symposium
Date: 20/09/2023

Recorded during the Spatial Data Science Symposium (SDSS 2023), September 5-6, 2023, distributed & online

Session 2: Spatial Data Science for Disaster Resilience
September 5

More information at https://www.acsu.buffalo.edu/~yhu42/files/2023_Disaster_Resilience_Session_Online.pdf
Symposium website: http://sdss2023.spatial-data-science.net/

Organizers: 
Yingjie Hu (Department of Geography, University at Buffalo)
Andrew Crooks (Department of Geography, University at Buffalo)

0:00 Introduction
1:01 Lei Zou, Achieving a Smart and Resilient Future with Spatial Data Science
Lightning Talks:
29:41 Qunying Huang, Wildfire Burnt Area Detection with Deep Learning and Sentinel-2 Imagery
33:31 Manzhu Yu,  Deciphering Wildfire Dynamics: Spatiotemporal Attention-Based Sequence-to-Sequence Models Using ConvLSTM Networks
38:24  Md Zakaria Salim, Socio-economic Disparities of Property Damage in Hurricane Ian
41:53 Qingqing Chen, Community resilience to wildfire: A network analysis approach by utilizing human mobility data
45:33 Kai Sun, GALLOC: a GeoAnnotator for Labeling LOCation descriptions from disaster-related text messages

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.