Deep Learning Techniques for Oil Tank Detection in Remote Sensing Images

The detection of oil storage tanks from remotely sensed imagery has witnessed significant advances with the advent of deep learning. Convolutional neural networks now underpin most object detection frameworks, exploiting hierarchical feature extraction to recognise the characteristic shapes and textures of above-ground storage tanks. Modern pipelines often integrate feature pyramid networks to capture multiscale information, while attention modules and Transformer-based encoders further refine focus on tank perimeters and contextual cues. Data augmentation, synthetic data generation and multimodal fusion—combining optical, radar and geospatial sources—address the challenges of variable illumination, occlusion and geometric distortion. Large-scale annotated datasets enable robust training, and architectures such as Faster R-CNN, YOLO variants and anchor-free detectors strike a balance between accuracy and computational efficiency. These approaches support applications that span environmental risk assessment, infrastructure monitoring and energy capacity estimation at regional and global scales.

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Deep Learning Techniques for Oil Tank Detection in Remote Sensing Images publication trend

The graph below shows the total number of articles in deep learning techniques for oil tank detection in remote sensing images across all publications each year (not limited to Nature Index journals).

Technical terms

Convolutional Neural Network (CNN): A deep learning model using convolutional layers to extract hierarchical spatial features from images.

Object Detection: The process of identifying and locating instances of predefined classes within an image, typically via bounding boxes and labels.

Feature Pyramid Network (FPN): An architecture that merges feature maps at multiple scales to improve detection of objects of varying sizes.

Synthetic Aperture Radar (SAR): An active remote sensing modality that uses microwave backscatter to generate imagery regardless of weather or illumination.

Transformer Encoder: A self-attention-based module that captures global contextual relationships within feature representations.

References

1. Remotely sensed above-ground storage tank dataset for object detection and infrastructure assessment. Scientific Data (2024).
2. City-scale industrial tank detection using multi-source spatial data fusion. International Journal of Digital Earth (2024).
3. Dense Oil Tank Detection and Classification via YOLOX-TR Network in Large-Scale SAR Images. Remote Sensing (2022).