# High Definition image classification in Geoscience using Machine Learning

Source: [High Definition image classification in Geoscience using Machine Learning](<https://www.sota2.com/research/paper/68fa68811e209131cea1c865>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-25T22:38:59.898Z

Authors: Yajun An, Zachary Golden, Tarka Wilcox, Renzhi Cao

Published: 2020-09-25T17:30:03.000Z

## 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.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2010.03965>)

[PDF](<https://arxiv.org/pdf/2010.03965.pdf>)