Spatial-Temporal Feature Extraction - overview
Spatial-Temporal Feature Extraction
Description: The availability of spatial-temporal data from satellite, aircraft and drone mounted sensors is readily increasing and being used to solve a broad range of problems. These data are used as input to multiple machine learning and AI models for classification and forecasting purposes. A key component of improving those models is the identification and extraction of features within spatial-temporal data that contribute significantly to model performance. This project is to develop and test statistical and/or deep learning techniques to extract features from spatial-temporal data to improve classification and predictive models. Application areas include renewable energy generation, inferring loads in time and space within electrical networks.
- Strong programming experience and skills in Python
- Experience and education in statistical and AI models applied to spatial-temporal data