High-Quality Damaged Building Instance Segmentation Based on Improved Mask Transfiner Using Post-Earthquake UAS Imagery: A Case Study of the Luding Ms 6.8 Earthquake in China
Unmanned aerial systems (UASs) are increasingly playing a crucial role in earthquake emergency response and disaster assessment due to their ease of operation, mobility, and low cost. However, post-earthquake scenes are complex, with many forms of damaged buildings. UAS imagery has a high spatial re...
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MDPI AG
2024-11-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/16/22/4222 |
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| author | Kangsan Yu Shumin Wang Yitong Wang Ziying Gu |
| author_facet | Kangsan Yu Shumin Wang Yitong Wang Ziying Gu |
| author_sort | Kangsan Yu |
| collection | DOAJ |
| description | Unmanned aerial systems (UASs) are increasingly playing a crucial role in earthquake emergency response and disaster assessment due to their ease of operation, mobility, and low cost. However, post-earthquake scenes are complex, with many forms of damaged buildings. UAS imagery has a high spatial resolution, but the resolution is inconsistent between different flight missions. These factors make it challenging for existing methods to accurately identify individual damaged buildings in UAS images from different scenes, resulting in coarse segmentation masks that are insufficient for practical application needs. To address these issues, this paper proposed DB-Transfiner, a building damage instance segmentation method for post-earthquake UAS imagery based on the Mask Transfiner network. This method primarily employed deformable convolution in the backbone network to enhance adaptability to collapsed buildings of arbitrary shapes. Additionally, it used an enhanced bidirectional feature pyramid network (BiFPN) to integrate multi-scale features, improving the representation of targets of various sizes. Furthermore, a lightweight Transformer encoder has been used to process edge pixels, enhancing the efficiency of global feature extraction and the refinement of target edges. We conducted experiments on post-disaster UAS images collected from the 2022 Luding earthquake with a surface wave magnitude (Ms) of 6.8 in the Sichuan Province of China. The results demonstrated that the average precisions (AP) of DB-Transfiner, <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msub><mrow><mi>A</mi><mi>P</mi></mrow><mrow><mi>b</mi><mi>o</mi><mi>x</mi></mrow></msub></mrow></semantics></math></inline-formula> and <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msub><mrow><mi>A</mi><mi>P</mi></mrow><mrow><mi>s</mi><mi>e</mi><mi>g</mi></mrow></msub></mrow></semantics></math></inline-formula>, are 56.42% and 54.85%, respectively, outperforming all other comparative methods. Our model improved the original model by 5.00% and 4.07% in <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msub><mrow><mi>A</mi><mi>P</mi></mrow><mrow><mi>b</mi><mi>o</mi><mi>x</mi></mrow></msub></mrow></semantics></math></inline-formula> and <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msub><mrow><mi>A</mi><mi>P</mi></mrow><mrow><mi>s</mi><mi>e</mi><mi>g</mi></mrow></msub></mrow></semantics></math></inline-formula>, respectively. Importantly, the <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msub><mrow><mi>A</mi><mi>P</mi></mrow><mrow><mi>s</mi><mi>e</mi><mi>g</mi></mrow></msub></mrow></semantics></math></inline-formula> of our model was significantly higher than the state-of-the-art instance segmentation model Mask R-CNN, with an increase of 9.07%. In addition, we conducted applicability testing, and the model achieved an average correctness rate of 84.28% for identifying images from different scenes of the same earthquake. We also applied the model to the Yangbi earthquake scene and found that the model maintained good performance, demonstrating a certain level of generalization capability. This method has high accuracy in identifying and assessing damaged buildings after earthquakes and can provide critical data support for disaster loss assessment. |
| format | Article |
| id | doaj-art-c32ed9043f46465399a1f2ab32abc2e5 |
| institution | OA Journals |
| issn | 2072-4292 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj-art-c32ed9043f46465399a1f2ab32abc2e52025-08-20T02:27:38ZengMDPI AGRemote Sensing2072-42922024-11-011622422210.3390/rs16224222High-Quality Damaged Building Instance Segmentation Based on Improved Mask Transfiner Using Post-Earthquake UAS Imagery: A Case Study of the Luding Ms 6.8 Earthquake in ChinaKangsan Yu0Shumin Wang1Yitong Wang2Ziying Gu3Institute of Earthquake Forecasting, China Earthquake Administration, Beijing 100036, ChinaInstitute of Earthquake Forecasting, China Earthquake Administration, Beijing 100036, ChinaInstitute of Earthquake Forecasting, China Earthquake Administration, Beijing 100036, ChinaInstitute of Earthquake Forecasting, China Earthquake Administration, Beijing 100036, ChinaUnmanned aerial systems (UASs) are increasingly playing a crucial role in earthquake emergency response and disaster assessment due to their ease of operation, mobility, and low cost. However, post-earthquake scenes are complex, with many forms of damaged buildings. UAS imagery has a high spatial resolution, but the resolution is inconsistent between different flight missions. These factors make it challenging for existing methods to accurately identify individual damaged buildings in UAS images from different scenes, resulting in coarse segmentation masks that are insufficient for practical application needs. To address these issues, this paper proposed DB-Transfiner, a building damage instance segmentation method for post-earthquake UAS imagery based on the Mask Transfiner network. This method primarily employed deformable convolution in the backbone network to enhance adaptability to collapsed buildings of arbitrary shapes. Additionally, it used an enhanced bidirectional feature pyramid network (BiFPN) to integrate multi-scale features, improving the representation of targets of various sizes. Furthermore, a lightweight Transformer encoder has been used to process edge pixels, enhancing the efficiency of global feature extraction and the refinement of target edges. We conducted experiments on post-disaster UAS images collected from the 2022 Luding earthquake with a surface wave magnitude (Ms) of 6.8 in the Sichuan Province of China. The results demonstrated that the average precisions (AP) of DB-Transfiner, <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msub><mrow><mi>A</mi><mi>P</mi></mrow><mrow><mi>b</mi><mi>o</mi><mi>x</mi></mrow></msub></mrow></semantics></math></inline-formula> and <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msub><mrow><mi>A</mi><mi>P</mi></mrow><mrow><mi>s</mi><mi>e</mi><mi>g</mi></mrow></msub></mrow></semantics></math></inline-formula>, are 56.42% and 54.85%, respectively, outperforming all other comparative methods. Our model improved the original model by 5.00% and 4.07% in <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msub><mrow><mi>A</mi><mi>P</mi></mrow><mrow><mi>b</mi><mi>o</mi><mi>x</mi></mrow></msub></mrow></semantics></math></inline-formula> and <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msub><mrow><mi>A</mi><mi>P</mi></mrow><mrow><mi>s</mi><mi>e</mi><mi>g</mi></mrow></msub></mrow></semantics></math></inline-formula>, respectively. Importantly, the <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msub><mrow><mi>A</mi><mi>P</mi></mrow><mrow><mi>s</mi><mi>e</mi><mi>g</mi></mrow></msub></mrow></semantics></math></inline-formula> of our model was significantly higher than the state-of-the-art instance segmentation model Mask R-CNN, with an increase of 9.07%. In addition, we conducted applicability testing, and the model achieved an average correctness rate of 84.28% for identifying images from different scenes of the same earthquake. We also applied the model to the Yangbi earthquake scene and found that the model maintained good performance, demonstrating a certain level of generalization capability. This method has high accuracy in identifying and assessing damaged buildings after earthquakes and can provide critical data support for disaster loss assessment.https://www.mdpi.com/2072-4292/16/22/4222damaged buildingsUASinstance segmentationMask TransfinerLuding Ms6.8 earthquake |
| spellingShingle | Kangsan Yu Shumin Wang Yitong Wang Ziying Gu High-Quality Damaged Building Instance Segmentation Based on Improved Mask Transfiner Using Post-Earthquake UAS Imagery: A Case Study of the Luding Ms 6.8 Earthquake in China Remote Sensing damaged buildings UAS instance segmentation Mask Transfiner Luding Ms6.8 earthquake |
| title | High-Quality Damaged Building Instance Segmentation Based on Improved Mask Transfiner Using Post-Earthquake UAS Imagery: A Case Study of the Luding Ms 6.8 Earthquake in China |
| title_full | High-Quality Damaged Building Instance Segmentation Based on Improved Mask Transfiner Using Post-Earthquake UAS Imagery: A Case Study of the Luding Ms 6.8 Earthquake in China |
| title_fullStr | High-Quality Damaged Building Instance Segmentation Based on Improved Mask Transfiner Using Post-Earthquake UAS Imagery: A Case Study of the Luding Ms 6.8 Earthquake in China |
| title_full_unstemmed | High-Quality Damaged Building Instance Segmentation Based on Improved Mask Transfiner Using Post-Earthquake UAS Imagery: A Case Study of the Luding Ms 6.8 Earthquake in China |
| title_short | High-Quality Damaged Building Instance Segmentation Based on Improved Mask Transfiner Using Post-Earthquake UAS Imagery: A Case Study of the Luding Ms 6.8 Earthquake in China |
| title_sort | high quality damaged building instance segmentation based on improved mask transfiner using post earthquake uas imagery a case study of the luding ms 6 8 earthquake in china |
| topic | damaged buildings UAS instance segmentation Mask Transfiner Luding Ms6.8 earthquake |
| url | https://www.mdpi.com/2072-4292/16/22/4222 |
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