Towards an automatic generalized machine learning approach to map lava flows
- Resource Type
- Conference
- Authors
- Corradino, Claudia; Amato, Eleonora; Torrisi, Federica; Negro, Ciro Del
- Source
- 2021 17th International Workshop on Cellular Nanoscale Networks and their Applications (CNNA) Cellular Nanoscale Networks and their Applications (CNNA), 2021 17th International Workshop on. :1-4 Sep, 2021
- Subject
- Bioengineering
Components, Circuits, Devices and Systems
Computing and Processing
General Topics for Engineers
Geoscience
Nuclear Engineering
Power, Energy and Industry Applications
Robotics and Control Systems
Signal Processing and Analysis
Support vector machines
Image motion analysis
Computer vision
Laser radar
Satellites
Spaceborne radar
Machine learning
machine learning
remote sensing
volcano monitoring
lava flow mapping
- Language
Volcano-related resurfacing processes can be monitored by complementary using radar and optical sensors. Combining both data sources with machine learning (ML) approaches is fundamental to automatically extract volcano-related features. Here, a generalized ML approach is developed in Google Earth Engine (GEE) to map lava flows in both near-real time (NRT) and no-time critical (NTC) time scales. A first attempt towards a generalized classification to automatically map new lava flows is proposed.