LiDAR is an optical remote sensing method that uses laser beams to estimate the spatial coordinates of desired targets on earth. LiDAR has been used extensively for self-driving cars technology, urban planification, and forestry (climate change applications). Features include many physical properties such as intensity. These 3D point clouds include classes from a natural environment such as water, man-made structures, vegetation, and the ground. Our goal would be to classify the 3D point clouds using an ML pipeline. This short course will give participants an opportunity to gain understanding of the data, and learn how to clean data by using tools like Python or OpenRefine and feature engineering. The second session would include an overview of some dimensionality reduction methods such as principal component analysis. We will then introduce neural networks in the context of classification and discuss their implementation. Other classification techniques will be introduced. We also expect to share different data visualization options and practice storytelling through final 5 minute presentations.
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