Oil storage detector is a computer vision pipeline designed for detecting petroleum, oil and lubricant (POL) storage areas using satellite imagery. The system is built on Kedro framework and utilizes a convolutional neural network (CNN) to analyze the imagery data. The project was developed in the academic context and uses Airbus satellite imagery available on Kaggle. Although the code is not yet productionized, it can be considered as a proof-of-concept that showcases the potential of using computer vision in oil storage detection.

The oil storage detector system works by processing the satellite imagery through the CNN model, which has been trained to recognize the visual patterns associated with POL storage areas. The system can detect and highlight the locations of these storage areas on the imagery, providing valuable insights for decision-makers in the oil and gas industry. The use of computer vision in oil storage detection can save time and resources by automating the detection process and reducing the need for manual inspections.

Overall, the oil storage detector is a promising application of computer vision in the oil and gas industry. While it is still in the development stage and not yet ready for production use, it showcases the potential of using machine learning to automate the detection of important features in satellite imagery. As the technology continues to evolve, it is likely that we will see more advanced and sophisticated tools for oil storage detection emerge, helping to improve safety, efficiency, and sustainability in the industry.