Efficient Building Roof Type Classification: A Domain-Specific Self-Supervised Approach
Accurate classification of building roof types from aerial imagery is crucial for various remote sensing applications, including urban planning, disaster management, and infrastructure monitoring. However, this task is often hindered by the limited availability of labeled data for supervised learnin...
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Copernicus Publications
2025-07-01
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| Series: | ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| Online Access: | https://isprs-annals.copernicus.org/articles/X-G-2025/591/2025/isprs-annals-X-G-2025-591-2025.pdf |
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| author | G. Mutreja K. Bittner |
| author_facet | G. Mutreja K. Bittner |
| author_sort | G. Mutreja |
| collection | DOAJ |
| description | Accurate classification of building roof types from aerial imagery is crucial for various remote sensing applications, including urban planning, disaster management, and infrastructure monitoring. However, this task is often hindered by the limited availability of labeled data for supervised learning approaches. To address this challenge, this paper investigates the effectiveness of selfsupervised learning with EfficientNet architectures, known for their computational efficiency, for building roof type classification. We propose a novel framework that incorporates a Convolutional Block Attention Module (CBAM) to enhance the feature extraction capabilities of EfficientNet. Furthermore, we explore the benefits of pretraining on a domain-specific dataset, the Aerial Image Dataset (AID), compared to ImageNet pretraining. Our experimental results demonstrate the superiority of our approach. Employing Simple Framework for Contrastive Learning of Visual Representations (SimCLR) with EfficientNet-B3 and CBAM achieves a 95.5% accuracy on our validation set, matching the performance of state-of-the-art transformer-based models while utilizing significantly fewer parameters. We also provide a comprehensive evaluation on two challenging test sets, demonstrating the generalization capability of our method. Notably, our findings highlight the effectiveness of domain-specific pretraining, consistently leading to higher accuracy compared to models pretrained on the generic ImageNet dataset. Our work establishes EfficientNetbased self-supervised learning as a computationally efficient and highly effective approach for building roof type classification, particularly beneficial in scenarios with limited labeled data. |
| format | Article |
| id | doaj-art-3b352ee209164a3c8ff5e2e7dbb054b3 |
| institution | Kabale University |
| issn | 2194-9042 2194-9050 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Copernicus Publications |
| record_format | Article |
| series | ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| spelling | doaj-art-3b352ee209164a3c8ff5e2e7dbb054b32025-08-20T03:28:34ZengCopernicus PublicationsISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences2194-90422194-90502025-07-01X-G-202559159710.5194/isprs-annals-X-G-2025-591-2025Efficient Building Roof Type Classification: A Domain-Specific Self-Supervised ApproachG. Mutreja0K. Bittner1Remote Sensing Technology Institute, German Aerospace Center (DLR), Weßling, GermanyRemote Sensing Technology Institute, German Aerospace Center (DLR), Weßling, GermanyAccurate classification of building roof types from aerial imagery is crucial for various remote sensing applications, including urban planning, disaster management, and infrastructure monitoring. However, this task is often hindered by the limited availability of labeled data for supervised learning approaches. To address this challenge, this paper investigates the effectiveness of selfsupervised learning with EfficientNet architectures, known for their computational efficiency, for building roof type classification. We propose a novel framework that incorporates a Convolutional Block Attention Module (CBAM) to enhance the feature extraction capabilities of EfficientNet. Furthermore, we explore the benefits of pretraining on a domain-specific dataset, the Aerial Image Dataset (AID), compared to ImageNet pretraining. Our experimental results demonstrate the superiority of our approach. Employing Simple Framework for Contrastive Learning of Visual Representations (SimCLR) with EfficientNet-B3 and CBAM achieves a 95.5% accuracy on our validation set, matching the performance of state-of-the-art transformer-based models while utilizing significantly fewer parameters. We also provide a comprehensive evaluation on two challenging test sets, demonstrating the generalization capability of our method. Notably, our findings highlight the effectiveness of domain-specific pretraining, consistently leading to higher accuracy compared to models pretrained on the generic ImageNet dataset. Our work establishes EfficientNetbased self-supervised learning as a computationally efficient and highly effective approach for building roof type classification, particularly beneficial in scenarios with limited labeled data.https://isprs-annals.copernicus.org/articles/X-G-2025/591/2025/isprs-annals-X-G-2025-591-2025.pdf |
| spellingShingle | G. Mutreja K. Bittner Efficient Building Roof Type Classification: A Domain-Specific Self-Supervised Approach ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| title | Efficient Building Roof Type Classification: A Domain-Specific Self-Supervised Approach |
| title_full | Efficient Building Roof Type Classification: A Domain-Specific Self-Supervised Approach |
| title_fullStr | Efficient Building Roof Type Classification: A Domain-Specific Self-Supervised Approach |
| title_full_unstemmed | Efficient Building Roof Type Classification: A Domain-Specific Self-Supervised Approach |
| title_short | Efficient Building Roof Type Classification: A Domain-Specific Self-Supervised Approach |
| title_sort | efficient building roof type classification a domain specific self supervised approach |
| url | https://isprs-annals.copernicus.org/articles/X-G-2025/591/2025/isprs-annals-X-G-2025-591-2025.pdf |
| work_keys_str_mv | AT gmutreja efficientbuildingrooftypeclassificationadomainspecificselfsupervisedapproach AT kbittner efficientbuildingrooftypeclassificationadomainspecificselfsupervisedapproach |