A Streamlined Solution to Efficiently Reduce Errors and Label Geographical Images

Abstract

Given the recent rise in global temperatures, the impact of global warming is becoming increasingly significant. To gain a better understanding of global warming’s effects, scientists analyze images that show changes over time with climate – such as Arctic ice. At the status quo, there is a lack of Arctic ice training data, and analyzing images is a tedious and time-consuming task, requiring scientists to manually segment and label their images. To solve this issue, Class X was developed to provide an alternative solution that could complete the same tasks in a simpler and time-efficient manner. Class X uses Python and depends heavily on initial data to train off of as well as manually segmented and labeled data, allowing Class X to autonomously segment and label images. The machine learning model uses datasets such as NASA’s IceBridge data portal to train the machine learning model. As a result, the image classification model for Class X uses aerial Arctic ice images to train along with object-based analysis (OBIA) methods and high spatial resolution data. Additionally, a crucial step in ensuring the software works is creating training data testing model results. As a result, each amenity of Class X enables the software to be a vital tool for scientists around the globe. Class X helps save time and money for companies and allows for data to be easily dissected and used for further studies.