Irregularly shaped man-made marine objects detected by small aeroplanes equipped with high-resolution photogrammetry sensor technologies

Tools

Kuru, Kaya and Clough, Stuart and Ansell, Darren and McCarthy, John and McGovern, Stephanie (2023) Irregularly shaped man-made marine objects detected by small aeroplanes equipped with high-resolution photogrammetry sensor technologies. [DataSet]

Original publication URL: https://doi.org/10.1016/j.ecoinf.2023.102285

Description

The marine economy has historically been highly diversified and prolific due to the fact that the Earth’s oceans comprise two-thirds of its total surface area. As technology advances, leading enterprises and ecological organisations are building and mobilising new devices supported by cutting-edge marine mechatronics solutions to explore and harness this challenging environment. Automated tracking of these types of industries and the marine life around them can help us figure out what’s causing the current changes in species numbers, predict what could happen in the future, and create the right policies to help reduce the environmental impact and make the planet more sustainable. The objective of this study is to create a new platform for the automated detection of irregularly shaped man-made marine objects (ISMMMOs) in large datasets derived from marine aerial survey imagery. In this context, a novel nonparametric methodology, which harbours several hybrid statistical Machine Learning (ML) methods, was developed to automatically segment ISMMMOs on the sea surface in large surveys. This methodology was validated on a wide range of marine domains, providing robust empirical proof of concept. The data was collected using small aeroplanes equipped with a wide range of advanced, high-resolution photogrammetry sensor technologies, including 35 mm and medium format sensors from a variety of manufacturers. In this data publication, we would like to share the high-resolution ISMMMO images and their processed outputs using the above-mentioned techniques that were previously published.

Research / Data Type:

Dataset

DOI:

10.17030/uclan.data.00000426

Depositing User:

Kaya Kuru

Date Deposited:

20 Nov 2023 12:12

Revision:

URI:

https://uclandata.uclan.ac.uk/id/eprint/426

Additional details

Creators:	Creators Email ORCID Kuru, Kaya KKuru@uclan.ac.uk orcid.org/0000-0002-4279-4166 Clough, Stuart s.clough@apemltd.co.uk UNSPECIFIED Ansell, Darren DAnsell@uclan.ac.uk UNSPECIFIED McCarthy, John j.mccarthy@apemltd.co.uk UNSPECIFIED McGovern, Stephanie s.mcgovern@apemltd.co.uk UNSPECIFIED
Schools:	School of Engineering and Computing* > Engineering, Construction, Maths and Physics*
Collection period:	From To 1 January 2010 1 January 2016
Date:	16 September 2023
Date type:	Publication
Data collection method:	The data was collected using small aeroplanes equipped with a wide range of advanced, high-resolution photogrammetry sensor technologies, including 35 mm and medium format sensors from a variety of manufacturers.
Status:	Published
Publisher:	Ecological Informatics
Copyright holders:	APEM Ltd.
Contact email address:	rdmsupport@uclan.ac.uk
Last Modified:	20 Nov 2023 12:13