Locos por la Geología

• Short Name: ICDDG
• Event Type: Conference
• Website URL: https://waset.org/data-driven-geoscience-conference-in-march-2023-in-london
• Contact URL: https://waset.org
• Location: London, United Kingdom
• Date: March 15-16, 2023
• Organization: World Academy of Science, Engineering and Technology

Data-driven geoscience
Machine learning and analytics in geoscience
Machine learning in geosciences and remote sensing
Computational methods for geoscience
Large-scale machine learning in earth sciences
Architectural considerations and data life cycle

Integration of data, computing/HPC/cloud, and algorithms
Cloud computing, software as a service
New technologies for scaling the architecture
Semantic technologies, linked data, and efficient semantic query, reasoning, and integration techniques
Capturing well-architected and curated data repositories
Data and semantic information architectures
Architecting automated pipelines for data capture
Enabling analytics on data pipelines for computation, data discovery, event detection, reduction, etc
Open source data science frameworks

Onboard/sensor-based computing
Embedded and real-time data reduction and triage/analytics methods
Managing bandwidth constraints for high volume instruments
Approaches for scaling observing systems for satellite, airborne and ground-based sensors
New concepts for data intensive missions
Integration of computational methods with observing systems

Scalable data analytics for massively distributed data
Access and integration of highly distributed, heterogeneous data
Search and retrieval across massive, heterogeneous data stores
Novel statistical approaches for data integration and fusion
Sampling strategies from massive data repositories
Uncertainty in scientific inferences
In situ analysis for high performance computing
Computation applied at the data sources
Automated machine learning methods for identifying and extracting interesting features and patterns
Methods for visualizing massive observational and model data
Visualization of big data from observations and/or models