Multimodal deep learning for identifying onshore natural gas facilities from GaoFen-2 imagery

With increasing demand for natural gas, the construction of natural gas extraction-related facilities has increased significantly. Accurate identification of these facilities is crucial for guiding spatial planning and evaluating environmental impacts. Existing research has primarily concentrated on offshore facilities, with limited attention to onshore facilities. This scarcity stems from identification challenges due to their dispersed distribution and complex environments. To address this gap, this study proposes a method combining a multimodal convolutional neural network (CNN) with object-based segmentation for onshore facility extraction. Experiments were conducted in northern Sichuan, China, with high-resolution Chinese satellite images, GF-2. Performance was compared between machine learning and CNN using sequentially cropped imageries. The proposed method achieved a precision of 59.97%, a recall of 94.87%, and an F1-score of 73.49%. The high recall indicates that most facilities were successfully detected, and the F1-score reflects the overall performance. These results suggest that the proposed method can effectively extract onshore facilities. Compared with machine learning and CNN using sequentially cropped imageries, the F1-score of the proposed method increased by 20.16% and 51.49%, respectively. The experimental results reveal that the proposed method can accurately identify onshore facilities, offering a scientific basis for assessing the environmental impact of greenhouse gases.