LakeNet: Water Quality Monitoring with Satellite Images and CNNs

Monitoring the water quality of lakes is a challenging task that can provide significant benefits and insights to environmental conservationists, policy-makers, and educators alike. While current methods utilize in-situ measurements to project water quality parameters, such methods are expensive and time consuming. This project proposes a Convolutional Neural Network regressor to predict various water quality metrics from multi-spectral images. Testing results show that this method far outperforms conventional methods of remotely estimating these metrics. In addition, this project provides a new dataset of Minnesota lakes used to train, test, and evaluate this network.

Resources: [Paper]