Petroleum and gas pipelines, comprising petroleum and gas pipes and related components, play an irreplaceable role in petroleum and gas transportation. For global economic growth, petroleum and gas are crucial natural resources. However, the pipelines often cross permafrost regions with challenging working conditions. Additionally, the potential for natural disasters raises concerns about pipeline accidents, posing a threat to pipeline operational safety. In response to the complexity of pipeline supervision and management, we choose to use remote sensing method combining deep learning-based algorithms. In this work, we build a petroleum and gas pipes dataset, which includes 1,388 remote sensing images and the study area is Russian polar regions. We trained FCN and U-Net deep learning models by using our self-built dataset for the detection of petroleum and gas pipes. Models’ performances were evaluated using MIoU (Mean Intersection over Union), mean precision, mean recall to evaluate the accuracy of the model’s prediction results and compared them visually with ground truth. Our results find that deep learning models can effectively learn the characteristics of pipelines and achieve ideal detection results on our dataset. The MIoU of the FCN model achieved 0.885 and the U-Net model achieved 0.894. The results demonstrate that our trained models can be used to accurately identify the petroleum and gas pipelines in remote sensing images.
The oil extraction process has cumulative detrimental impacts on the environment. However, in the process of oil mining, a large number of petroleum-based pollutants cause severe effect to soil and groundwater, which poses a serious risk to the ecological environment and human health. Understanding the distribution of oil well sites, is of vital importance to sustainable mining development. Efficient mapping these sites require automated identification and extraction of the oil well sites from satellite images. With the development of remote sensing satellite technology and the wide application of deep learning-based algorithms, it has become possible to automatically extract oil well sites from remote sensing images. However, there is lack of usage of Sentinel-2 satellite data to explore the efficacy in oil well sites detection. Therefore, we conducted this work to explore the feasibility of detecting the oil well sites with semantic segmentation from Sentinel-2 imagery. In this work, we established the Northeast Petroleum University Oil Well Sites Version 2.0 (NEPU-OWS V2.0) with spatial coverage spanning the Austin region of United States. We then validate the usability and effectiveness of the dataset using semantic segmentation models based on DANet and Swin-Unet, which are more capable of recognizing small targets. Our experimental results show that both models have great potential for remote sensing detection in the medium sized oil well sites and the Swin-Unet model achieved a better performance for the detection of oil well sites with a MIoU of 77.53%.
Industrial storage tanks are widely used in petroleum, chemical industry, metallurgy and other process industries. The use of storage tanks are important in ensuring industrial safety production and product storage. Recently, concerns are rising over leaks and spills which leads to potential environmental and public health risks, for example, air pollution. Thus, there is a need to detect and monitor the tanks, which has become a regulatory requirement in most countries. The monitoring of industrial storage tanks in a city is of great practical significance to the planning and construction of the city. With the recent development of remote sensing technology, computer vision and image processing, it is possible to automatically detect industrial storage tanks from optical remote sensing images. This paper mainly studies the automatic detection of urban industrial storage tanks using deep learning based algorithm, aiming to help people manage and monitor the urban environment and resources. In this paper, we collected optical satellite remote sensing images of the city as a unit and created a city-level dataset including three cities of Guangdong province in south China by utilising satellite image data obtained from Google Earth imagery. To explore the effect of the deep learning object detection algorithm for the industrial storage tanks detection at the city-level, a deep learning model built with SSD (Single-Shot Detector) was trained based on our collected dataset. To further improve the detection accuracy of industrial storage tanks, Hough transform was used to reduce the false alarm in the deep learning results. The experimental results show that the combination of deep learning target detection model and Hough transform is effective in detecting of industrial storage tank on the collected city-level dataset and can achieve promising detection results.
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