Automated identification of objects in aerial imagery using a CNN: oil/gas sites to Martian volcanoes

Abstract

The recent advancements in Deep Learning techniques have revolutionized the field of computer vision. Deep Learning has received massive attention in remote sensing. The availability of open-source satellite imagery has opened up lots of remote sensing applications, from object detection to disaster assessment. In this work, I explore the application of deep learning for automated identification of oil/gas sites in DJ Basin, Colorado and detection of volcanoes on Mars. Oil and gas production sites are one of the significant sources of methane emissions all over the world. Methane emission studies from oil/gas sites require a count of major equipment in a site. However, these counts are not properly documented, and manual annotation of each piece of equipment in a site takes a lot of time and effort. To solve this challenge, an end-to-end deep learning model is developed that finds the well sites from satellite imagery and returns a count of major equipment at each site. Second, an end-to-end deep learning approach is used to detect volcanoes on Mars. Volcanic constructs are fundamental in studying the potential for past and future habitable environment on Mars. Even though large volcanic constructs are well documented, there is no proper documentation for smaller volcanoes. Manually finding all the smaller volcanoes will be a tedious task. In the second part of my work, I explore the potential of deep learning approaches for Martian volcano detection.