High Definition image classification in Geoscience using Machine Learning

TL;DR

This paper presents machine learning methods, including SVM and neural networks, to automatically classify high-definition geoscience images as clear or blurry, streamlining data cleaning processes.

Contribution

It introduces a machine learning approach for automatic HD image classification in geoscience, comparing feature-based models to improve accuracy.

Findings

SVM and NN effectively classify images as clear or blurry.

Feature extraction from mathematical models enhances classification accuracy.

Open-source implementation available for community use.

Abstract

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.