The detection of oil storage tanks is crucial for effective environmental monitoring, infrastructure management, and security operations, especially in industries where oil storage plays a significant role. Accurate detection allows for better resource management, risk mitigation, and early identification of potential hazards, making it an essential task for remote sensing technologies. This chapter explores the use of the Faster R-CNN object detection model to identify fuel tanks from remote sensing images captured by both UAVs and satellites. The model was trained and fine-tuned on a dataset containing labeled oil storage tanks and evaluated across various performance metrics. Key findings from the study show that Faster R-CNN achieves a precision of 0.871 and a recall of 0.802, indicating its strong capability in accurately detecting fuel tanks while maintaining a balance between true-positive rates and false positives. The model’s overall detection accuracy, reflected by an mAP of 0.844, demonstrates robust performance, particularly at an IoU threshold of 50%, where it reached an mAP@50 of 0.933. Additionally, the model’s inference speed of 29 FPS makes it suitable for real-time applications. However, challenges were observed in detecting tanks closely grouped together or tanks that blended in with their surroundings, as well as in inclined-angle images, where some tanks were incorrectly detected as a single object. Despite these limitations, the algorithm’s performance remained strong across different conditions.