Advancing Oil and Gas Facility Detection: A Comparative Analysis of Deep Learning and Vision LLMs

Aerial imagery plays a critical role in identifying oil and gas (O&G) infrastructure for environmental monitoring and methane emission control. This study evaluates ten deep learning models and a multimodal large language model (GPT-4o-mini) for detecting O&G facilities using high-resolution images from the OGNet dataset. InceptionV3 achieved the highest F1 score (0.875), while GPT-4o-mini reached 0.857 through iterative prompt tuning. Though CNNs like ResNet50 and VGG16 were computationally efficient, GPT-4omini offered competitive accuracy with higher inference costs. These findings highlight the promise of combining CNN and vision-LLM approaches to support scalable and sustainable facility detection.