I graduated from Texas A&M with a degree in mechanical engineering in 2010, and I graduated from KAUST in 2011 with a degree in Earth Science and Engineering. I worked for Chesapeake Energy as a geophysicist for 2012-2019. At Chesapeake my work focused on seismic interpretation, inversion, anisotropy analysis, and attribute analysis. I began work for Rosen in 2020 as a data scientist. I am currently working at Rosen, and my focus is on deep learning for industrial image processing.
The impact of human development on our planet's climate and environment is a key concern for many scientists and policy makers. The abundance of satellite imagery provides us with a unique opportunity to study the global impact of human activity. Machine learning is an extremely useful tool for this analysis, as it provides a means to automatically extract and process huge amounts of data. In this study, Google Earth Engine was used to detect deforested areas within the Amazon. An image processing workflow was developed, and a simple pixel-wise classifier was trained. This classifier was applied to a small area of the Amazon for the years 2015-2019. Active areas of deforestation are identified using this technique.