Closing the Domain Gap: Can Pseudo-Labels from Synthetic UAV Data Enable Real-World Flood Segmentation?
We present a novel methodology for generating and filtering synthetic Unmanned Aerial Vehicle (UAV) flood imagery to enhance the generalization capabilities of segmentation models. Our framework combines text-to-image synthesis and image inpainting, using curated prompts and real-world segmentation...
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MDPI AG
2025-06-01
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| author | Georgios Simantiris Konstantinos Bacharidis Costas Panagiotakis |
| author_facet | Georgios Simantiris Konstantinos Bacharidis Costas Panagiotakis |
| author_sort | Georgios Simantiris |
| collection | DOAJ |
| description | We present a novel methodology for generating and filtering synthetic Unmanned Aerial Vehicle (UAV) flood imagery to enhance the generalization capabilities of segmentation models. Our framework combines text-to-image synthesis and image inpainting, using curated prompts and real-world segmentation masks to produce diverse and realistic flood scenes. To overcome the lack of human annotations, we employ an unsupervised pseudo-labeling method that generates segmentation masks based on floodwater appearance characteristics. We further introduce a filtering stage based on outlier detection in feature space to improve the realism of the synthetic dataset. Experimental results on five state-of-the-art flood segmentation models show that synthetic data can closely match real data in training performance, and combining both sources improves model robustness by 1–7%. Finally, we investigate the impact of prompt design on the visual fidelity of generated images and provide qualitative and quantitative evidence of distributional similarity between real and synthetic data. |
| format | Article |
| id | doaj-art-642c22d62a6847b98a26e8a6a1ef6c06 |
| institution | Kabale University |
| issn | 1424-8220 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj-art-642c22d62a6847b98a26e8a6a1ef6c062025-08-20T03:29:35ZengMDPI AGSensors1424-82202025-06-012512358610.3390/s25123586Closing the Domain Gap: Can Pseudo-Labels from Synthetic UAV Data Enable Real-World Flood Segmentation?Georgios Simantiris0Konstantinos Bacharidis1Costas Panagiotakis2Department of Management Science and Technology, Hellenic Mediterranean University, 72100 Agios Nikolaos, GreeceDepartment of Management Science and Technology, Hellenic Mediterranean University, 72100 Agios Nikolaos, GreeceDepartment of Management Science and Technology, Hellenic Mediterranean University, 72100 Agios Nikolaos, GreeceWe present a novel methodology for generating and filtering synthetic Unmanned Aerial Vehicle (UAV) flood imagery to enhance the generalization capabilities of segmentation models. Our framework combines text-to-image synthesis and image inpainting, using curated prompts and real-world segmentation masks to produce diverse and realistic flood scenes. To overcome the lack of human annotations, we employ an unsupervised pseudo-labeling method that generates segmentation masks based on floodwater appearance characteristics. We further introduce a filtering stage based on outlier detection in feature space to improve the realism of the synthetic dataset. Experimental results on five state-of-the-art flood segmentation models show that synthetic data can closely match real data in training performance, and combining both sources improves model robustness by 1–7%. Finally, we investigate the impact of prompt design on the visual fidelity of generated images and provide qualitative and quantitative evidence of distributional similarity between real and synthetic data.https://www.mdpi.com/1424-8220/25/12/3586unsupervised image segmentationdeep learningpseudo-labelsflood segmentationunmanned aerial vehiclesimage inpainting |
| spellingShingle | Georgios Simantiris Konstantinos Bacharidis Costas Panagiotakis Closing the Domain Gap: Can Pseudo-Labels from Synthetic UAV Data Enable Real-World Flood Segmentation? Sensors unsupervised image segmentation deep learning pseudo-labels flood segmentation unmanned aerial vehicles image inpainting |
| title | Closing the Domain Gap: Can Pseudo-Labels from Synthetic UAV Data Enable Real-World Flood Segmentation? |
| title_full | Closing the Domain Gap: Can Pseudo-Labels from Synthetic UAV Data Enable Real-World Flood Segmentation? |
| title_fullStr | Closing the Domain Gap: Can Pseudo-Labels from Synthetic UAV Data Enable Real-World Flood Segmentation? |
| title_full_unstemmed | Closing the Domain Gap: Can Pseudo-Labels from Synthetic UAV Data Enable Real-World Flood Segmentation? |
| title_short | Closing the Domain Gap: Can Pseudo-Labels from Synthetic UAV Data Enable Real-World Flood Segmentation? |
| title_sort | closing the domain gap can pseudo labels from synthetic uav data enable real world flood segmentation |
| topic | unsupervised image segmentation deep learning pseudo-labels flood segmentation unmanned aerial vehicles image inpainting |
| url | https://www.mdpi.com/1424-8220/25/12/3586 |
| work_keys_str_mv | AT georgiossimantiris closingthedomaingapcanpseudolabelsfromsyntheticuavdataenablerealworldfloodsegmentation AT konstantinosbacharidis closingthedomaingapcanpseudolabelsfromsyntheticuavdataenablerealworldfloodsegmentation AT costaspanagiotakis closingthedomaingapcanpseudolabelsfromsyntheticuavdataenablerealworldfloodsegmentation |