Ethnic Architectural Heritage Identification Using Low-Altitude UAV Remote Sensing and Improved Deep Learning Algorithms
Ethnic minority architecture is a vital carrier of the cultural heritage of ethnic minorities in China, and its quick and accurate extraction from remote sensing images is highly important for promoting the application of remote sensing information in urban management and architectural heritage prot...
Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2024-12-01
|
| Series: | Buildings |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2075-5309/15/1/15 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1841549358436188160 |
|---|---|
| author | Ting Luo Xiaoqiong Sun Weiquan Zhao Wei Li Linjiang Yin Dongdong Xie |
| author_facet | Ting Luo Xiaoqiong Sun Weiquan Zhao Wei Li Linjiang Yin Dongdong Xie |
| author_sort | Ting Luo |
| collection | DOAJ |
| description | Ethnic minority architecture is a vital carrier of the cultural heritage of ethnic minorities in China, and its quick and accurate extraction from remote sensing images is highly important for promoting the application of remote sensing information in urban management and architectural heritage protection. Taking Buyi architecture in China as an example, this paper proposes a minority architectural heritage identification method that combines low-altitude unmanned aerial vehicle (UAV) remote sensing technology and an improved deep learning algorithm. First, UAV images are used as the data source to provide high-resolution images for research on ethnic architecture recognition and to solve the problems associated with the high costs, time consumption, and destructiveness of traditional methods for ethnic architecture recognition. Second, to address the lack of edge pixel features in the sample images and reduce repeated labeling of the same sample, the ethnic architecture in entire remote sensing images is labeled on the Arcgis platform, and the sliding window method is used to cut the image data and the corresponding label file with a 10% overlap rate. Finally, an attention mechanism SE module is introduced to improve the DeepLabV3+ network model structure and achieve superior ethnic building recognition results. The experimental data fully show that the model’s accuracy reaches as high as 0.9831, with an excellent recall rate of 0.9743. Moreover, the F1 score is stable at a high level of 0.9787, which highlights the excellent performance of the model in terms of comprehensive evaluation indicators. Additionally, the intersection/union ratio (IoU) of the model is 0.9582, which further verifies its high precision in pixel-level recognition tasks. According to an in-depth comparative analysis, the innovative method proposed in this paper solves the problem of insufficient feature extraction of sample edge pixels and substantially reduces interference from complex environmental factors such as roads, building shadows, and vegetation with the recognition results for ethnic architecture. This breakthrough greatly improves the accuracy and robustness of the identification of architecture in low-altitude remote sensing images and provides strong technical support for the protection and intelligent analysis of architectural heritage. |
| format | Article |
| id | doaj-art-807a0a46c177423ab58c5856b5156443 |
| institution | Kabale University |
| issn | 2075-5309 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Buildings |
| spelling | doaj-art-807a0a46c177423ab58c5856b51564432025-01-10T13:15:47ZengMDPI AGBuildings2075-53092024-12-011511510.3390/buildings15010015Ethnic Architectural Heritage Identification Using Low-Altitude UAV Remote Sensing and Improved Deep Learning AlgorithmsTing Luo0Xiaoqiong Sun1Weiquan Zhao2Wei Li3Linjiang Yin4Dongdong Xie5Guizhou Institute of Mountain Resources, Guizhou Academy of Sciences, Guiyang 550001, ChinaGuizhou Institute of Mountain Resources, Guizhou Academy of Sciences, Guiyang 550001, ChinaGuizhou Institute of Mountain Resources, Guizhou Academy of Sciences, Guiyang 550001, ChinaGuizhou Institute of Mountain Resources, Guizhou Academy of Sciences, Guiyang 550001, ChinaGuizhou Institute of Mountain Resources, Guizhou Academy of Sciences, Guiyang 550001, ChinaGuizhou Institute of Mountain Resources, Guizhou Academy of Sciences, Guiyang 550001, ChinaEthnic minority architecture is a vital carrier of the cultural heritage of ethnic minorities in China, and its quick and accurate extraction from remote sensing images is highly important for promoting the application of remote sensing information in urban management and architectural heritage protection. Taking Buyi architecture in China as an example, this paper proposes a minority architectural heritage identification method that combines low-altitude unmanned aerial vehicle (UAV) remote sensing technology and an improved deep learning algorithm. First, UAV images are used as the data source to provide high-resolution images for research on ethnic architecture recognition and to solve the problems associated with the high costs, time consumption, and destructiveness of traditional methods for ethnic architecture recognition. Second, to address the lack of edge pixel features in the sample images and reduce repeated labeling of the same sample, the ethnic architecture in entire remote sensing images is labeled on the Arcgis platform, and the sliding window method is used to cut the image data and the corresponding label file with a 10% overlap rate. Finally, an attention mechanism SE module is introduced to improve the DeepLabV3+ network model structure and achieve superior ethnic building recognition results. The experimental data fully show that the model’s accuracy reaches as high as 0.9831, with an excellent recall rate of 0.9743. Moreover, the F1 score is stable at a high level of 0.9787, which highlights the excellent performance of the model in terms of comprehensive evaluation indicators. Additionally, the intersection/union ratio (IoU) of the model is 0.9582, which further verifies its high precision in pixel-level recognition tasks. According to an in-depth comparative analysis, the innovative method proposed in this paper solves the problem of insufficient feature extraction of sample edge pixels and substantially reduces interference from complex environmental factors such as roads, building shadows, and vegetation with the recognition results for ethnic architecture. This breakthrough greatly improves the accuracy and robustness of the identification of architecture in low-altitude remote sensing images and provides strong technical support for the protection and intelligent analysis of architectural heritage.https://www.mdpi.com/2075-5309/15/1/15ethnic architectural heritagelow-altitude remote sensing identificationdeep learningattention mechanismDeepLabV3+ |
| spellingShingle | Ting Luo Xiaoqiong Sun Weiquan Zhao Wei Li Linjiang Yin Dongdong Xie Ethnic Architectural Heritage Identification Using Low-Altitude UAV Remote Sensing and Improved Deep Learning Algorithms Buildings ethnic architectural heritage low-altitude remote sensing identification deep learning attention mechanism DeepLabV3+ |
| title | Ethnic Architectural Heritage Identification Using Low-Altitude UAV Remote Sensing and Improved Deep Learning Algorithms |
| title_full | Ethnic Architectural Heritage Identification Using Low-Altitude UAV Remote Sensing and Improved Deep Learning Algorithms |
| title_fullStr | Ethnic Architectural Heritage Identification Using Low-Altitude UAV Remote Sensing and Improved Deep Learning Algorithms |
| title_full_unstemmed | Ethnic Architectural Heritage Identification Using Low-Altitude UAV Remote Sensing and Improved Deep Learning Algorithms |
| title_short | Ethnic Architectural Heritage Identification Using Low-Altitude UAV Remote Sensing and Improved Deep Learning Algorithms |
| title_sort | ethnic architectural heritage identification using low altitude uav remote sensing and improved deep learning algorithms |
| topic | ethnic architectural heritage low-altitude remote sensing identification deep learning attention mechanism DeepLabV3+ |
| url | https://www.mdpi.com/2075-5309/15/1/15 |
| work_keys_str_mv | AT tingluo ethnicarchitecturalheritageidentificationusinglowaltitudeuavremotesensingandimproveddeeplearningalgorithms AT xiaoqiongsun ethnicarchitecturalheritageidentificationusinglowaltitudeuavremotesensingandimproveddeeplearningalgorithms AT weiquanzhao ethnicarchitecturalheritageidentificationusinglowaltitudeuavremotesensingandimproveddeeplearningalgorithms AT weili ethnicarchitecturalheritageidentificationusinglowaltitudeuavremotesensingandimproveddeeplearningalgorithms AT linjiangyin ethnicarchitecturalheritageidentificationusinglowaltitudeuavremotesensingandimproveddeeplearningalgorithms AT dongdongxie ethnicarchitecturalheritageidentificationusinglowaltitudeuavremotesensingandimproveddeeplearningalgorithms |