07-15, 14:00–14:30 (Europe/Dublin), Liffey Hall 2
Leveraging geospatial Python libraries to understand and predict High-risk houses during cyclone-induced floods in urban areas considering historical openly available satellite images and urban morphological data.
Assigning a flood risk score to each individual house near the coastal regions is a challenge. Also, as the land characteristics vary based on different geographical locations, prepare for emergencies on demand.
We will demonstrate an end-to-end methodology using geospatial Python libraries to understand the use of Multi-Criteria Decision making methods taking into account driving variables. This talk will also throw light upon:
- Getting the large imagery datasets into DL friendly format
- Resampling of Satellite image data in python
- Conducting overlay analysis with weights
- Calculation of zonal statistics at house level
- Future Scope
We'll also showcase the geo-visualization portal we created and the technologies used, how you can use Python to convert large GeoJSON output to light vector tiles, and create a seamless experience for the user through an intuitive front-end.
Leveraging geospatial Python libraries to understand and predict High-risk houses during cyclone-induced floods in urban areas considering historical openly available satellite images and urban morphological data.
Expected audience expertise: Domain –none
Expected audience expertise: Python –some
Sumedh Ghatage is an Associate Lead Data Scientist (Geospatial) at Gramener. He has worked on various smart city initiatives including sectors such as environmental resource management, location intelligence, and disaster management projects.
He drives a community called “Geospatial Awareness Hub” which helps enable Education, Employment, and Business to foster the growth and awareness of the Geospatial Industry