How Computer Vision Streamlines Risk Avoidance Workflows in Oil & Gas
Blog post from Roboflow
In a guest post on the Roboflow Blog, Douglas Long, a full stack developer, discusses how computer vision can streamline risk avoidance workflows in the oil and gas industry, particularly in relation to notifying landowners about new energy infrastructure projects. Traditionally, the process involved manually identifying and digitizing geographic points of interest using tools like ArcGIS, which was labor-intensive and prone to errors. Long demonstrates how deploying a computer vision model can automate and enhance this process by accurately identifying structures, such as homes, within a specified region, thereby improving efficiency and reducing human error. The article outlines the technical steps involved in creating and deploying a computer vision model, including sourcing and annotating images, training the model, and using it to automate structure detection. This approach not only accelerates the notification process but also allows for greater accuracy and scalability, ultimately transforming the way energy companies handle regulatory requirements and landowner consultations.