GeoAI-Driven mapping of urban rooftop Photovoltaics: A sustainable energy framework for Karachi, Pakistan
Rooftop Photovoltaics (RPV) offer a sustainable solution for renewable energy integration, leveraging urban landscapes to address environmental challenges such as carbon emissions and resource management. This study introduces a novel methodology for mapping and estimating RPV potential in Karachi,...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-09-01
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| Series: | International Journal of Applied Earth Observations and Geoinformation |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S1569843225004339 |
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| author | Muhammad Kamran Lodhi Yumin Tan Agus Suprijanto Shahid Naeem Yang Li |
| author_facet | Muhammad Kamran Lodhi Yumin Tan Agus Suprijanto Shahid Naeem Yang Li |
| author_sort | Muhammad Kamran Lodhi |
| collection | DOAJ |
| description | Rooftop Photovoltaics (RPV) offer a sustainable solution for renewable energy integration, leveraging urban landscapes to address environmental challenges such as carbon emissions and resource management. This study introduces a novel methodology for mapping and estimating RPV potential in Karachi, Pakistan, using earth observation data derived from satellite imagery. We developed a custom dataset of high-resolution satellite images capturing diverse solar panel configurations and employed an ensemble deep learning approach—combining UNet-ResNet50, DeepLabv3-ResNet50, Mask2Former-SwinTransformer, SamLoRA-vit_b, and PSPNet-ResNet50—to detect RPV installations with high precision. Through weighted majority voting, the ensemble model achieved superior accuracy, precision, recall, and F1-score compared to individual models, enhancing the reliability of geospatial mapping under variable urban and weather conditions. To estimate photovoltaic yield, we integrated spatiotemporal solar irradiance data with surface meteorological measurements, including ambient temperature, wind speed, and humidity, yielding an annual RPV potential of 602.83 GWh for Karachi, with a potential reduction of 0.37 megatons of CO2 equivalent. This GeoAI-driven framework serves as a foundational layer for an Urban Digital Twin, enabling dynamic modelling of RPV potential to support sustainable urban energy transitions. Additionally, we evaluated the economic feasibility of RPV deployment using levelized cost of electricity (LCOE) analysis. By advancing image processing and big spatiotemporal data analytics with GeoAI, this study provides a scalable framework for inventorying urban renewable energy resources, offering actionable insights for governance and sustainable land use planning. |
| format | Article |
| id | doaj-art-62eaddb3842c4ac9b9a6ddaddd3fb5e9 |
| institution | Kabale University |
| issn | 1569-8432 |
| language | English |
| publishDate | 2025-09-01 |
| publisher | Elsevier |
| record_format | Article |
| series | International Journal of Applied Earth Observations and Geoinformation |
| spelling | doaj-art-62eaddb3842c4ac9b9a6ddaddd3fb5e92025-08-20T03:41:31ZengElsevierInternational Journal of Applied Earth Observations and Geoinformation1569-84322025-09-0114310478610.1016/j.jag.2025.104786GeoAI-Driven mapping of urban rooftop Photovoltaics: A sustainable energy framework for Karachi, PakistanMuhammad Kamran Lodhi0Yumin Tan1Agus Suprijanto2Shahid Naeem3Yang Li4Hangzhou International Innovation Institute, Beihang University, Hangzhou, China; School of Transportation Science and Engineering, Beihang University, Beijing 100191, ChinaHangzhou International Innovation Institute, Beihang University, Hangzhou, China; School of Transportation Science and Engineering, Beihang University, Beijing 100191, ChinaResearch Center for remote sensing, National Research and Innovation Agency (BRIN), Jakarta, IndonesiaHangzhou International Innovation Institute, Beihang University, Hangzhou, China; College of Resources and Environment, Yangtze University, Wuhan, ChinaHangzhou International Innovation Institute, Beihang University, Hangzhou, China; Corresponding author at: Hangzhou International Innovation Institute, Beihang University, Hangzhou, China.Rooftop Photovoltaics (RPV) offer a sustainable solution for renewable energy integration, leveraging urban landscapes to address environmental challenges such as carbon emissions and resource management. This study introduces a novel methodology for mapping and estimating RPV potential in Karachi, Pakistan, using earth observation data derived from satellite imagery. We developed a custom dataset of high-resolution satellite images capturing diverse solar panel configurations and employed an ensemble deep learning approach—combining UNet-ResNet50, DeepLabv3-ResNet50, Mask2Former-SwinTransformer, SamLoRA-vit_b, and PSPNet-ResNet50—to detect RPV installations with high precision. Through weighted majority voting, the ensemble model achieved superior accuracy, precision, recall, and F1-score compared to individual models, enhancing the reliability of geospatial mapping under variable urban and weather conditions. To estimate photovoltaic yield, we integrated spatiotemporal solar irradiance data with surface meteorological measurements, including ambient temperature, wind speed, and humidity, yielding an annual RPV potential of 602.83 GWh for Karachi, with a potential reduction of 0.37 megatons of CO2 equivalent. This GeoAI-driven framework serves as a foundational layer for an Urban Digital Twin, enabling dynamic modelling of RPV potential to support sustainable urban energy transitions. Additionally, we evaluated the economic feasibility of RPV deployment using levelized cost of electricity (LCOE) analysis. By advancing image processing and big spatiotemporal data analytics with GeoAI, this study provides a scalable framework for inventorying urban renewable energy resources, offering actionable insights for governance and sustainable land use planning.http://www.sciencedirect.com/science/article/pii/S1569843225004339Rooftop Photovoltaics (RPV)Sustainable EnergyEarth ObservationGeoAI |
| spellingShingle | Muhammad Kamran Lodhi Yumin Tan Agus Suprijanto Shahid Naeem Yang Li GeoAI-Driven mapping of urban rooftop Photovoltaics: A sustainable energy framework for Karachi, Pakistan International Journal of Applied Earth Observations and Geoinformation Rooftop Photovoltaics (RPV) Sustainable Energy Earth Observation GeoAI |
| title | GeoAI-Driven mapping of urban rooftop Photovoltaics: A sustainable energy framework for Karachi, Pakistan |
| title_full | GeoAI-Driven mapping of urban rooftop Photovoltaics: A sustainable energy framework for Karachi, Pakistan |
| title_fullStr | GeoAI-Driven mapping of urban rooftop Photovoltaics: A sustainable energy framework for Karachi, Pakistan |
| title_full_unstemmed | GeoAI-Driven mapping of urban rooftop Photovoltaics: A sustainable energy framework for Karachi, Pakistan |
| title_short | GeoAI-Driven mapping of urban rooftop Photovoltaics: A sustainable energy framework for Karachi, Pakistan |
| title_sort | geoai driven mapping of urban rooftop photovoltaics a sustainable energy framework for karachi pakistan |
| topic | Rooftop Photovoltaics (RPV) Sustainable Energy Earth Observation GeoAI |
| url | http://www.sciencedirect.com/science/article/pii/S1569843225004339 |
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