The session will contain two presentations, first we'll look at versatile RS tools to monitor the development of new wind projects in the GOM, and second we'll look at a case study for geothermal prospectivity in S America and the constraints used for evaluation.
2023 Proceedings
Remote Sensing and Deep Learning in support of Renewable Energy Development
Presentations
- Using ArcGIS and Machine Learning to Map Geothermal ProspectivityGeothermal energy exploitation involves great diversity in customer, usage and constraints, so to locate resource potential has many challenges. ArcGIS-based favourability mapping for ranking of prospective areas is emerging as a powerful tool. Geological risk lies principally in heat flow, permeability, and volume, but in Geothermal projects, key constraints also include commercial and social factors, and early matching of customer demand to resource supply is critical. We present a case study using ArcGIS Pro and Machine Learning that maps geothermal prospectivity in South America.
, Exprodat
- SAR data and Satellite Imagery: The Key to Offshore Wind Energy MonitoringTurbineHub, in partnership with Planet Labs and L3 Harris Geospatial, is creating a cutting-edge software that utilizes Synthetic-aperture radar (SAR), satellite imagery, and deep learning to track offshore wind energy development. The company has mapped all FAA boat routes which allows for the use of targeted SAR data to measure vessel traffic during non-daylight hours when optical imagery is ineffective. The software will provide analytics for each offshore wind project to support best practices and ensure the long-term sustainability of the offshore wind industry.
, turbinehub.com, llc
Session Type: User Presentations
Session Level: All Attendees
