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Learning and Recognizing Archeological Features from LiDAR Data
We present a remote sensing pipeline that processes LiDAR (Light Detection And Ranging) data through machine & deep learning for the application of archeological feature detection on big geo-spatial data platforms such as e. g. IBM PAIRS Geoscope.
An Unsupervised Clustering-Based Short-Term Solar Forecasting Methodology Using Multi-Model Machine Learning Blending
This methodology consists of three parts: GHI time series unsupervised clustering, pattern recognition, and UC-based forecasting.
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