Lecture

Satellite Observations of Air Pollution

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Description

This lecture discusses how ground-based and satellite measurements complement each other, providing regional information and filling spatiotemporal data gaps. It covers topics such as Aerosol Optical Depth, validation through the AERONET network, and the use of satellites for air quality data. The lecture also explores the relationship between AOD and PM2.5, limitations of satellite data, and exercises using MODIS Aqua satellite data.

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Statistical model validation

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Training, validation, and test data sets

In machine learning, a common task is the study and construction of algorithms that can learn from and make predictions on data. Such algorithms function by making data-driven predictions or decisions, through building a mathematical model from input data. These input data used to build the model are usually divided into multiple data sets. In particular, three data sets are commonly used in different stages of the creation of the model: training, validation, and test sets.

Satellite imagery

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Satellite

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