High Definition image classification in Geoscience using Machine Learning
This addresses a time-consuming data cleaning bottleneck for geoscientists, but it is incremental as it applies standard machine learning methods to a specific domain.
The paper tackles the problem of automatically distinguishing clear from blurry high-definition drone images in geoscience to automate data cleaning, achieving classification results using SVM and neural networks.
High Definition (HD) digital photos taken with drones are widely used in the study of Geoscience. However, blurry images are often taken in collected data, and it takes a lot of time and effort to distinguish clear images from blurry ones. In this work, we apply Machine learning techniques, such as Support Vector Machine (SVM) and Neural Network (NN) to classify HD images in Geoscience as clear and blurry, and therefore automate data cleaning in Geoscience. We compare the results of classification based on features abstracted from several mathematical models. Some of the implementation of our machine learning tool is freely available at: https://github.com/zachgolden/geoai.