The University of Southampton
University of Southampton Institutional Repository

Satellite imagery to select a sample of rooftops for a PV installation project in Jeddah, Saudi Arabia

Satellite imagery to select a sample of rooftops for a PV installation project in Jeddah, Saudi Arabia
Satellite imagery to select a sample of rooftops for a PV installation project in Jeddah, Saudi Arabia

A region-based convolutional neural network image segmentation approach (Mask R-CNN) was applied to identification of flat rooftops from satellite imagery in the city of Jeddah in Saudi Arabia. The model was trained on a small sample of rooftops (202) digitized from a 0.5 m resolution image (covering 0.21 km2) and then was applied to an independent area 4.5 km away. The precision and recall of the model were 0.98 and 0.96 respectively in terms of identifying rooftops in the independent area. A spatially stratified sample of rooftops was drawn from those identified by the model and the median roof area of the sample was not significantly different from the area as a whole. The results, although at a small scale, demonstrate the effectiveness of this approach for selecting buildings with appropriate rooftops for solar photovoltaic (PV) installation, in the context of closely spaced flat-roofed buildings, without requiring cadastral mapping or LIDAR datasets.

Blunden, Luke S.
28b4a5d4-16f8-4396-825b-4f65639d2903
Mahdy, Mostafa Y.M.
d70eb14b-7e5f-49ff-aaa5-3a87f0e81947
Alghamdi, Abdulsalam S.
c8473e87-8814-41ba-9352-63991e9a0dba
Bahaj, Abu Bakr S.
a64074cc-2b6e-43df-adac-a8437e7f1b37
Blunden, Luke S.
28b4a5d4-16f8-4396-825b-4f65639d2903
Mahdy, Mostafa Y.M.
d70eb14b-7e5f-49ff-aaa5-3a87f0e81947
Alghamdi, Abdulsalam S.
c8473e87-8814-41ba-9352-63991e9a0dba
Bahaj, Abu Bakr S.
a64074cc-2b6e-43df-adac-a8437e7f1b37

Blunden, Luke S., Mahdy, Mostafa Y.M., Alghamdi, Abdulsalam S. and Bahaj, Abu Bakr S. (2021) Satellite imagery to select a sample of rooftops for a PV installation project in Jeddah, Saudi Arabia. 2021 International Hybrid Conference on Carbon Neutral Cities - Energy Efficiency and Renewables in the Digital Era, CISBAT 2021, , Lausanne, Virtual, Switzerland. 08 - 10 Sep 2021. 7 pp . (doi:10.1088/1742-6596/2042/1/012014).

Record type: Conference or Workshop Item (Paper)

Abstract

A region-based convolutional neural network image segmentation approach (Mask R-CNN) was applied to identification of flat rooftops from satellite imagery in the city of Jeddah in Saudi Arabia. The model was trained on a small sample of rooftops (202) digitized from a 0.5 m resolution image (covering 0.21 km2) and then was applied to an independent area 4.5 km away. The precision and recall of the model were 0.98 and 0.96 respectively in terms of identifying rooftops in the independent area. A spatially stratified sample of rooftops was drawn from those identified by the model and the median roof area of the sample was not significantly different from the area as a whole. The results, although at a small scale, demonstrate the effectiveness of this approach for selecting buildings with appropriate rooftops for solar photovoltaic (PV) installation, in the context of closely spaced flat-roofed buildings, without requiring cadastral mapping or LIDAR datasets.

Text
Blunden_2021_J._Phys.__Conf._Ser._2042_012014 - Version of Record
Available under License Creative Commons Attribution.
Download (1MB)

More information

Published date: 18 November 2021
Additional Information: Funding Information: This work is part of the activities of the Energy and Climate Change Division and the Sustainable Energy Research Group in the Faculty of Engineering and Applied Sciences at the University of Southampton (www.energy.soton.ac.uk), UK and the Department of Electrical and Computer Engineering, King Abdulaziz University (KAU), Saudi Arabia. This work was funded by the Deputyship for Research & Innovation, Ministry of Education in the Kingdom of Saudi Arabia under project number 714. Publisher Copyright: © Content from this work may be used under the terms of the Creative Commons Attribution 3.0 Licence. Copyright: Copyright 2021 Elsevier B.V., All rights reserved.
Venue - Dates: 2021 International Hybrid Conference on Carbon Neutral Cities - Energy Efficiency and Renewables in the Digital Era, CISBAT 2021, , Lausanne, Virtual, Switzerland, 2021-09-08 - 2021-09-10

Identifiers

Local EPrints ID: 456902
URI: http://eprints.soton.ac.uk/id/eprint/456902
PURE UUID: 44c1e253-85c1-4624-b594-5edd20f7595e
ORCID for Luke S. Blunden: ORCID iD orcid.org/0000-0002-0046-5508
ORCID for Mostafa Y.M. Mahdy: ORCID iD orcid.org/0000-0003-2006-870X
ORCID for Abu Bakr S. Bahaj: ORCID iD orcid.org/0000-0002-0043-6045

Catalogue record

Date deposited: 16 May 2022 16:42
Last modified: 06 Jun 2024 01:48

Export record

Altmetrics

Contributors

Author: Luke S. Blunden ORCID iD
Author: Mostafa Y.M. Mahdy ORCID iD
Author: Abdulsalam S. Alghamdi

Download statistics

Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.

View more statistics

Atom RSS 1.0 RSS 2.0

Contact ePrints Soton: eprints@soton.ac.uk

ePrints Soton supports OAI 2.0 with a base URL of http://eprints.soton.ac.uk/cgi/oai2

This repository has been built using EPrints software, developed at the University of Southampton, but available to everyone to use.

We use cookies to ensure that we give you the best experience on our website. If you continue without changing your settings, we will assume that you are happy to receive cookies on the University of Southampton website.

×