
Remote sensing is the method of collecting and analyzing data using mechanical or electronic sensors. Commonly the industry considers imagery to be the main remote sensing medium. A new age of understanding the world was ushered in when French photographer Gaspar Felix Tournachon (a.k.a. Nadar) first strapped a camera to the basket of a tethered balloon in 1858 - and when James Black's glass negative camera took panorama shots of Boston in 1850. They were pioneering photographers inventing a new way to collect data about the world around us in the form of aerial imagery. Since that time, new methods of flight (the Wright brothers did some of the first photography from an airplane) and improvements in technology led to greater

accuracy, resolution, clarity, and a useful resource for everything from natural resource planning, to fighting wars (US Civil War, to the Middle East conflicts of today). Today's remote sensing data consists of not only aerial imagery, but satellite imagery, RADAR, LIDAR, SONAR, and many other sensors are available. In fact, the evolution of operations research begins with sonar sensor operations. Questions of how to optimally deploy and interpret these data yielded the discipline of using applied mathematics to determine operational parameters of these systems and OR was born.
That legacy lives on today. Globally there are more than 500TB
(my swag estimate from company websites) of imagery collected each month from commercial imagery providers. Who knows how much the world's governments are collecting. It almost certainly dwarfs that number. Having people visually inspect all that data is a meaningless task. It's error prone, slow, and inefficient. That's where OR and specifically techniques in machine learning and computer vision come in. Some basic operations that can be performed on imagery that add value are edge detection, class segmentation, terrain models, watershed analysis, line of sight, slope, aspect, and impervious surface models.