Recognition of Oil Storage Tanks in satellite images using the Yolov3 object detection model from scratch using Tensorflow 2.x and calculation of the volume occupied by the Floating Head Tanks with the help of shadow made by them.

Before 1957, our planet Earth only had a natural satellite: the moon. On October 4, 1957, the Soviet Union launched the world’s first artificial satellite. Since then, about 8,900 satellites from more than 40 countries have been launched.

Photo by NASA on Unsplash

These satellites help us in monitoring, communication, navigation, and much more. The nations also use the satellite to keep an eye on another nation’s land and its movements, to estimate their economy and power. However, all the nations hide their information from one another.

Likewise, the global oil market is not entirely transparent. Almost all oil-producing nations make an effort to hide their total production, consumption, and storage. Nations do this to indirectly conceal their actual economy from outside and empower their defense system. This practice might lead to a threat to other nations.

For this reason, many startups companies like Planet and Orbital Insight came out to keep eyes on such kind of activities of the nations by satellite imagery. Thye collects satellite imagery of oil storage tanks and estimates reserve volumes.

But the question is, how can one estimate the volume of a tank by just a satellite image? Well, this will only be possible when oil is stored in the floating roof/head tank. This particular type of tank is specially designed to store large quantities of petroleum products such as crude oil or condensate. It consists of the top head that sits directly on the top of the oil, which rises or falls with the volume of oil in the tank and makes two shadows around it. As you can see the below image, the shadow on the north side

source

(exterior shadow) of the tank refers to the total height of the tank while the shadow within the tank (interior shadow) shows the depth of the floating head/roof(i.e how much empty tank is). And the volume will be estimated as 1-(Area of Interior Shadow/Area of Exterior Shadow).

In this blog, we are going to implement the complete model to estimate the occupied volumes of tanks with the help of satellite images in python language using Tensorflow2.x framework, from scratch.

### GitHub Repo

Everything on this article and the entire code is available in this github repository

Here is the list of contents that are followed in this blog. We would explore each one by one.

#computer-vision #deep-learning #yolov3 #data-science #object-detection

1.60 GEEK