Spatial-Temporal Feature Extraction     

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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.  

Preferred Background:
- Strong programming experience and skills in Python
- Experience and education in statistical and AI models applied to spatial-temporal data