Toward global rooftop PV detection with Deep Active Learning
It is crucial to know the location of rooftop PV systems to monitor the regional progress toward sustainable societies and to ensure the integration of decentralized energy resources into the electricity grid. However, locations of PV are often unknown, which is why a large number of studies have pr...
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| Language: | English |
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Elsevier
2024-12-01
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| Series: | Advances in Applied Energy |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2666792424000295 |
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| author | Matthias Zech Hendrik-Pieter Tetens Joseph Ranalli |
| author_facet | Matthias Zech Hendrik-Pieter Tetens Joseph Ranalli |
| author_sort | Matthias Zech |
| collection | DOAJ |
| description | It is crucial to know the location of rooftop PV systems to monitor the regional progress toward sustainable societies and to ensure the integration of decentralized energy resources into the electricity grid. However, locations of PV are often unknown, which is why a large number of studies have proposed variants of Deep Learning to detect PV panels in remote sensing data using supervised Deep Learning. However, these methods are based on annotating datasets and therefore often require relabeling when fine-tuned or extended to a different region. Recent advances in Deep Active Learning offer the opportunity to significantly reduce the number of required annotated images by intelligently selecting the images to label next based on their informative value for the model. In this study, we compare different Deep Active Learning algorithms using a variety of datasets from different regions and compare different model training variants. In the simulations, the entropy-based acquisition function shows the highest performance with only 3% of the data needed in case-imbalanced data, while remaining simple to implement. We believe that Deep Active Learning provides an elegant solution to maintain high model accuracy while reducing annotation effort substantially. This facilitates the development of generalizable models for worldwide rooftop PV detection. |
| format | Article |
| id | doaj-art-1eaec8d822934bf8b37e96ee2467516a |
| institution | Kabale University |
| issn | 2666-7924 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Advances in Applied Energy |
| spelling | doaj-art-1eaec8d822934bf8b37e96ee2467516a2024-12-12T05:23:39ZengElsevierAdvances in Applied Energy2666-79242024-12-0116100191Toward global rooftop PV detection with Deep Active LearningMatthias Zech0Hendrik-Pieter Tetens1Joseph Ranalli2German Aerospace Center (DLR), Institute of Networked Energy Systems, Carl-von-Ossietzky-Straße 15, Oldenburg, 26129, Germany; Corresponding author.German Aerospace Center (DLR), Institute of Networked Energy Systems, Carl-von-Ossietzky-Straße 15, Oldenburg, 26129, GermanyPenn State Hazleton, 76 University Drive, Hazleton, 18202, PA, USAIt is crucial to know the location of rooftop PV systems to monitor the regional progress toward sustainable societies and to ensure the integration of decentralized energy resources into the electricity grid. However, locations of PV are often unknown, which is why a large number of studies have proposed variants of Deep Learning to detect PV panels in remote sensing data using supervised Deep Learning. However, these methods are based on annotating datasets and therefore often require relabeling when fine-tuned or extended to a different region. Recent advances in Deep Active Learning offer the opportunity to significantly reduce the number of required annotated images by intelligently selecting the images to label next based on their informative value for the model. In this study, we compare different Deep Active Learning algorithms using a variety of datasets from different regions and compare different model training variants. In the simulations, the entropy-based acquisition function shows the highest performance with only 3% of the data needed in case-imbalanced data, while remaining simple to implement. We believe that Deep Active Learning provides an elegant solution to maintain high model accuracy while reducing annotation effort substantially. This facilitates the development of generalizable models for worldwide rooftop PV detection.http://www.sciencedirect.com/science/article/pii/S2666792424000295Deep Active LearningPV panel detectionMachine LearningSemantic segmentationRemote sensing |
| spellingShingle | Matthias Zech Hendrik-Pieter Tetens Joseph Ranalli Toward global rooftop PV detection with Deep Active Learning Advances in Applied Energy Deep Active Learning PV panel detection Machine Learning Semantic segmentation Remote sensing |
| title | Toward global rooftop PV detection with Deep Active Learning |
| title_full | Toward global rooftop PV detection with Deep Active Learning |
| title_fullStr | Toward global rooftop PV detection with Deep Active Learning |
| title_full_unstemmed | Toward global rooftop PV detection with Deep Active Learning |
| title_short | Toward global rooftop PV detection with Deep Active Learning |
| title_sort | toward global rooftop pv detection with deep active learning |
| topic | Deep Active Learning PV panel detection Machine Learning Semantic segmentation Remote sensing |
| url | http://www.sciencedirect.com/science/article/pii/S2666792424000295 |
| work_keys_str_mv | AT matthiaszech towardglobalrooftoppvdetectionwithdeepactivelearning AT hendrikpietertetens towardglobalrooftoppvdetectionwithdeepactivelearning AT josephranalli towardglobalrooftoppvdetectionwithdeepactivelearning |