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Remotely sensed above-ground storage tank dataset for object detection and infrastructure assessment.

Publication ,  Journal Article
Robinson, C; Bradbury, K; Borsuk, ME
Published in: Scientific data
January 2024

Remotely sensed imagery has increased dramatically in quantity and public availability. However, automated, large-scale analysis of such imagery is hindered by a lack of the annotations necessary to train and test machine learning algorithms. In this study, we address this shortcoming with respect to above-ground storage tanks (ASTs) that are used in a wide variety of industries. We annotated available high-resolution, remotely sensed imagery to develop an original, publicly available multi-class dataset of ASTs. This dataset includes geospatial coordinates, border vertices, diameters, and orthorectified imagery for over 130,000 ASTs from five labeled classes (external floating roof tanks, closed roof tanks, spherical pressure tanks, sedimentation tanks, and water towers) across the contiguous United States. This dataset can be used directly or to train machine learning algorithms for large-scale risk and hazard assessment, production and capacity estimation, and infrastructure evaluation.

Duke Scholars

Published In

Scientific data

DOI

EISSN

2052-4463

ISSN

2052-4463

Publication Date

January 2024

Volume

11

Issue

1

Start / End Page

67
 

Citation

APA
Chicago
ICMJE
MLA
NLM
Robinson, C., Bradbury, K., & Borsuk, M. E. (2024). Remotely sensed above-ground storage tank dataset for object detection and infrastructure assessment. Scientific Data, 11(1), 67. https://doi.org/10.1038/s41597-023-02780-1
Robinson, Celine, Kyle Bradbury, and Mark E. Borsuk. “Remotely sensed above-ground storage tank dataset for object detection and infrastructure assessment.Scientific Data 11, no. 1 (January 2024): 67. https://doi.org/10.1038/s41597-023-02780-1.
Robinson C, Bradbury K, Borsuk ME. Remotely sensed above-ground storage tank dataset for object detection and infrastructure assessment. Scientific data. 2024 Jan;11(1):67.
Robinson, Celine, et al. “Remotely sensed above-ground storage tank dataset for object detection and infrastructure assessment.Scientific Data, vol. 11, no. 1, Jan. 2024, p. 67. Epmc, doi:10.1038/s41597-023-02780-1.
Robinson C, Bradbury K, Borsuk ME. Remotely sensed above-ground storage tank dataset for object detection and infrastructure assessment. Scientific data. 2024 Jan;11(1):67.

Published In

Scientific data

DOI

EISSN

2052-4463

ISSN

2052-4463

Publication Date

January 2024

Volume

11

Issue

1

Start / End Page

67