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.
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Mahdy, Mostafa Y.M.
d70eb14b-7e5f-49ff-aaa5-3a87f0e81947
Alghamdi, Abdulsalam S.
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Bahaj, Abu Bakr S.
a64074cc-2b6e-43df-adac-a8437e7f1b37
18 November 2021
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).
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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
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Published date: 18 November 2021
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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.
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© Content from this work may be used under the terms of the Creative Commons Attribution 3.0 Licence.
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Copyright 2021 Elsevier B.V., All rights reserved.
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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
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Local EPrints ID: 456902
URI: http://eprints.soton.ac.uk/id/eprint/456902
PURE UUID: 44c1e253-85c1-4624-b594-5edd20f7595e
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Date deposited: 16 May 2022 16:42
Last modified: 18 Mar 2024 03:15
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Author:
Mostafa Y.M. Mahdy
Author:
Abdulsalam S. Alghamdi
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