Automating offshore infrastructure extractions using synthetic aperture radar &amp; Google Earth Engine

Wong, Brian A.; Thomas, Christian J.; Halpin, Patrick N.

doi:10.1016/j.rse.2019.111412

Cited by 30 publications

(35 citation statements)

References 41 publications

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“…Therefore, their number was not enough to have a separate category and, thus, were assigned to the Others applications category. These studies are mainly related to archaeology [169]- [171], 3D printing [172], wildlife [173], [174], oil platform detection [175], and crashed airplane detection [176]. For example, 300 Landsat-8 images between 2013 and 2018 were processed in GEE to detect possible crashed airplane in the Cambodian jungle [176].…”

Section: J Othersmentioning

confidence: 99%

See 1 more Smart Citation

Google Earth Engine Cloud Computing Platform for Remote Sensing Big Data Applications: A Comprehensive Review

Amani¹,

Ghorbanian

Ahmadi

et al. 2020

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

662

216

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Remote Sensing (RS) systems have been collecting massive volumes of datasets for decades, managing and analyzing of which are not practical using common software packages and desktop computing resources. In this regard, Google has developed a cloud computing platform, called Google Earth Engine (GEE), to effectively address the challenges of big data analysis. In particular, this platform facilitates processing big geo data over large areas and monitoring the environment for long periods of time. Although this platform was launched in 2010 and has proved its high potential for different applications, it has not been fully investigated and utilized for RS applications until recent years. Therefore, this study aims to comprehensively explore different aspects of the GEE platform, including its datasets, functions, advantages/limitations, and various applications. For this purpose, 450 journal articles published in 150 journals between January 2010 and May 2020 were studied. It was observed that Landsat and Sentinel datasets were extensively utilized by GEE users. Moreover, supervised machine learning algorithms, such as Random Forest (RF), were more widely applied to image classification tasks. GEE has also been employed in a broad range of applications, such as Land Cover/land Use (LCLU) classification, hydrology, urban planning, natural disaster, climate analyses, and image processing. It was generally observed that the number of GEE publications has significantly increased during the past few years, and it is expected that GEE will be utilized by more users from different fields to resolve their big data processing challenges.

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Section: J Othersmentioning

confidence: 99%

“…NDVI, albedo, thermal bands, spectral information, and panchromatic features were utilized in this study. Moreover, Sentinel-1 SAR data were used to automatically identify and delineate offshore oil platforms [175]. The proposed method was evaluated by 1,577 reference samples and obtained an overall accuracy of 96.09% over the Gulf of Mexico.…”

Section: J Othersmentioning

confidence: 99%

Google Earth Engine Cloud Computing Platform for Remote Sensing Big Data Applications: A Comprehensive Review

Amani¹,

Ghorbanian

Ahmadi

et al. 2020

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

662

216

View full text Add to dashboard Cite

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“…Wong et al [17] extracted the offshore drilling rigs using Sentinel-1 SAR data with GEE. The method may be divided into five steps: (1) Generating median composite image;…”

Section: Comparison With the Methods Based On Sar Imagesmentioning

confidence: 99%

“…The size of filtering window and the shape and size of morphological operation structure elements will affect the results. In [17], 460 parameter combinations were tested and the optimal parameter combination was selected. However, the portability of the optimal parameter combination in other regions is uncertain.…”

Section: Comparison With the Methods Based On Sar Imagesmentioning

confidence: 99%

“…Wan et al [16] compared the shape differences between ships and offshore drilling rigs in SAR data to improve detection accuracy. Wong et al [17] developed a cloud-native geoprocessing algorithm to automatically detect and extract offshore oil platforms in the Gulf of Mexico using synthetic aperture radar and the Google Earth Engine. Zhang et al [18] proposed an automatic extraction method for offshore platforms in single SAR images based on a dual-step-modified model.…”

Section: Introductionmentioning

confidence: 99%

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Detecting Offshore Drilling Rigs with Multitemporal NDWI: A Case Study in the Caspian Sea

et al. 2021

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Offshore drilling rigs are the foundation of oil and gas exploitation in water areas. Their spatial and temporal distribution, state attributes and other information directly reflect the exploitation level of regional oil and gas resources. Therefore, it is very important to build an automatic detecting method for offshore drilling rigs with good performance to accurately capture the temporal and spatial distribution and state of oil and gas exploitation activities. At present, there are two main groups of methods for offshore drilling rigs: invariant feature-based methods and nighttime firelight-based methods. Methods based on invariant location are more subjective in terms of their parameter settings and require intensive computation. Nighttime light-based methods are largely unable to identify offshore drilling rigs without associated waste gas ignition. Furthermore, multiple offshore drilling rigs in close proximity to one another cannot be effectively distinguished with low spatial resolution imagery. To address these shortcomings, we propose a new method for the automatic identification of offshore drilling rigs based on Landsat-7 ETM+ images from 2018 to 2019, taking the Caspian Sea as the research area. We build a nominal annual cloud and cloud shadow-free Normalized Difference Water Index (NDWI) composite by designing an optimal NDWI compositing method based of the influence of cloud and cloud shadow on the NDWI values of water, bare land (island) and offshore drilling rigs. The classification of these objects is simultaneously done during the compositing process, with the following rules: water body (Max_NDWI > 0.55), bare land (island) (Min_NDWI < −0.05) and offshore drilling rig (0 < Mean_NDWI < 0.4). A threshold segmentation and postprocessing were carried out to further refine the results. Using this method, 497 offshore platforms were automatically identified using a nominal annual cloud and cloud shadow-free NDWI composite image and Google Earth Engine. Validation using Sentinel-2 Multispectral Imager (MSI) and Google Earth images demonstrated that the correct rate of offshore drilling rig detection in the Caspian Sea is 90.2%, the missing judgment rate is 5.3% and the wrong judgment rate is 4.5%, proving the performance of the proposed method. This method can be used to identify offshore drilling rigs within a large water surface area relatively quickly, which is of great significance for exploring the exploitation status of offshore oil and gas resources. It can also be extended to finer spatial resolution optical remote sensing images; thus small-size drilling rigs can be effectively detected.

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Concept of Sustainable Energy System for Smart Cities

Kumar¹

2023

Advances in Geographical and Environmental Sciences

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Automating offshore infrastructure extractions using synthetic aperture radar & Google Earth Engine

Cited by 30 publications

References 41 publications

Google Earth Engine Cloud Computing Platform for Remote Sensing Big Data Applications: A Comprehensive Review

Google Earth Engine Cloud Computing Platform for Remote Sensing Big Data Applications: A Comprehensive Review

Detecting Offshore Drilling Rigs with Multitemporal NDWI: A Case Study in the Caspian Sea

Concept of Sustainable Energy System for Smart Cities

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