An Interactive Prompt Based Network for Urban Floods Area Segmentation Using UAV Images
As climate change intensifies, extreme weather events like floods are occurring with increasing frequency. While data-driven deep learning methods are effective for extracting flood disaster information, their efficiency is constrained by the scarcity of postdisaster samples, the high cost of annota...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2025-01-01
|
| Series: | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10753337/ |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1846128609983463424 |
|---|---|
| author | Lingfei Shi Kun Yang Yunfang Chen Gong Chen |
| author_facet | Lingfei Shi Kun Yang Yunfang Chen Gong Chen |
| author_sort | Lingfei Shi |
| collection | DOAJ |
| description | As climate change intensifies, extreme weather events like floods are occurring with increasing frequency. While data-driven deep learning methods are effective for extracting flood disaster information, their efficiency is constrained by the scarcity of postdisaster samples, the high cost of annotations, and the models’ strong dependence on both the quantity and quality of data. This study introduces an interactive semantic segmentation model based on multisource UAV flood images, incorporating four types of prompts. By embedding expert knowledge into the prompt design, the model reduces annotation costs and enhances generalization capabilities. First, a prompt encoder is developed to map different types of prompt information into a three-channel space using convolutional techniques, thereby reducing sample labeling costs. Moreover, an image encoder that integrates Mamba and convolution is developed to effectively extract global spatial and channel features from flood images while minimizing computational load. Finally, a spatial and channel attention module with residual connections is introduced to enable multiscale fusion and filtering of prompt information and image features across both spatial and channel dimensions, improving the utilization of prompt information. To validate the model's performance, we conduct experiments using UAV flood imagery collected from diverse regions, backgrounds, and angles. The results demonstrate that, under consistent prompt conditions, our model extracts flood areas more efficiently, reducing misclassification and omission errors. Compared with the next best benchmark model, the intersection over union for the flood category improves by at least 3.75%. |
| format | Article |
| id | doaj-art-65cf23df1b7b4cf0952d4a4416a5a37e |
| institution | Kabale University |
| issn | 1939-1404 2151-1535 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
| spelling | doaj-art-65cf23df1b7b4cf0952d4a4416a5a37e2024-12-11T00:00:41ZengIEEEIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing1939-14042151-15352025-01-011893594810.1109/JSTARS.2024.349886510753337An Interactive Prompt Based Network for Urban Floods Area Segmentation Using UAV ImagesLingfei Shi0https://orcid.org/0009-0009-5807-8365Kun Yang1https://orcid.org/0009-0002-6845-6381Yunfang Chen2Gong Chen3Faculty of Geography, Yunnan Normal University, Kunming, ChinaFaculty of Geography, Yunnan Normal University, Kunming, ChinaFaculty of Geography, Yunnan Normal University, Kunming, ChinaFaculty of Geography, Yunnan Normal University, Kunming, ChinaAs climate change intensifies, extreme weather events like floods are occurring with increasing frequency. While data-driven deep learning methods are effective for extracting flood disaster information, their efficiency is constrained by the scarcity of postdisaster samples, the high cost of annotations, and the models’ strong dependence on both the quantity and quality of data. This study introduces an interactive semantic segmentation model based on multisource UAV flood images, incorporating four types of prompts. By embedding expert knowledge into the prompt design, the model reduces annotation costs and enhances generalization capabilities. First, a prompt encoder is developed to map different types of prompt information into a three-channel space using convolutional techniques, thereby reducing sample labeling costs. Moreover, an image encoder that integrates Mamba and convolution is developed to effectively extract global spatial and channel features from flood images while minimizing computational load. Finally, a spatial and channel attention module with residual connections is introduced to enable multiscale fusion and filtering of prompt information and image features across both spatial and channel dimensions, improving the utilization of prompt information. To validate the model's performance, we conduct experiments using UAV flood imagery collected from diverse regions, backgrounds, and angles. The results demonstrate that, under consistent prompt conditions, our model extracts flood areas more efficiently, reducing misclassification and omission errors. Compared with the next best benchmark model, the intersection over union for the flood category improves by at least 3.75%.https://ieeexplore.ieee.org/document/10753337/Flood detectionprompt learningsemantic segmentationUAV |
| spellingShingle | Lingfei Shi Kun Yang Yunfang Chen Gong Chen An Interactive Prompt Based Network for Urban Floods Area Segmentation Using UAV Images IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Flood detection prompt learning semantic segmentation UAV |
| title | An Interactive Prompt Based Network for Urban Floods Area Segmentation Using UAV Images |
| title_full | An Interactive Prompt Based Network for Urban Floods Area Segmentation Using UAV Images |
| title_fullStr | An Interactive Prompt Based Network for Urban Floods Area Segmentation Using UAV Images |
| title_full_unstemmed | An Interactive Prompt Based Network for Urban Floods Area Segmentation Using UAV Images |
| title_short | An Interactive Prompt Based Network for Urban Floods Area Segmentation Using UAV Images |
| title_sort | interactive prompt based network for urban floods area segmentation using uav images |
| topic | Flood detection prompt learning semantic segmentation UAV |
| url | https://ieeexplore.ieee.org/document/10753337/ |
| work_keys_str_mv | AT lingfeishi aninteractivepromptbasednetworkforurbanfloodsareasegmentationusinguavimages AT kunyang aninteractivepromptbasednetworkforurbanfloodsareasegmentationusinguavimages AT yunfangchen aninteractivepromptbasednetworkforurbanfloodsareasegmentationusinguavimages AT gongchen aninteractivepromptbasednetworkforurbanfloodsareasegmentationusinguavimages AT lingfeishi interactivepromptbasednetworkforurbanfloodsareasegmentationusinguavimages AT kunyang interactivepromptbasednetworkforurbanfloodsareasegmentationusinguavimages AT yunfangchen interactivepromptbasednetworkforurbanfloodsareasegmentationusinguavimages AT gongchen interactivepromptbasednetworkforurbanfloodsareasegmentationusinguavimages |