Individual Tree Detection Based on High-Resolution RGB Images for Urban Forestry Applications
Urban forests play an important role in urban ecosystems. They can not only beautify the urban environment but also help protect biodiversity and maintain ecological balance. Effective urban forest management is a basic requirement to ensure sustainable development. Traditional urban forest manageme...
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| Format: | Article |
| Language: | English |
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IEEE
2022-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9765982/ |
| _version_ | 1811307501258276864 |
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| author | Lishuo Zhang Hong Lin Feng Wang |
| author_facet | Lishuo Zhang Hong Lin Feng Wang |
| author_sort | Lishuo Zhang |
| collection | DOAJ |
| description | Urban forests play an important role in urban ecosystems. They can not only beautify the urban environment but also help protect biodiversity and maintain ecological balance. Effective urban forest management is a basic requirement to ensure sustainable development. Traditional urban forest management usually requires the investment of a lot of materials and labor to conduct field research. RGB high-resolution aerial images have emerged as an efficient source of data for use in the detection and mapping of individual trees in urban areas. In recent years, there has been impressive progress in the field of deep learning methods for use in object detection. Semi-supervised learning is an effective way to deal with the problem that deep learning requires a large amount of labeled data. In this paper, we proposed an improved faster region-based convolutional neural network (Faster R-CNN) with Swin transformer method. Based on existing datasets, the model was trained and then transferred to new datasets. The method was evaluated within three distinct urban areas: a green space, a residential area and a suburban area. The experimental results indicate that our method achieved higher performance than other Faster R-CNN models. This method provides a reference in automated individual tree detection based on high-resolution images in urban areas for urban forestry managers. |
| first_indexed | 2024-04-13T09:06:53Z |
| format | Article |
| id | doaj.art-6320d28ab67240bda5fa2586960452b7 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-04-13T09:06:53Z |
| publishDate | 2022-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-6320d28ab67240bda5fa2586960452b72022-12-22T02:52:59ZengIEEEIEEE Access2169-35362022-01-0110465894659810.1109/ACCESS.2022.31715859765982Individual Tree Detection Based on High-Resolution RGB Images for Urban Forestry ApplicationsLishuo Zhang0https://orcid.org/0000-0003-1551-4066Hong Lin1Feng Wang2School of Geography and Planning, Sun Yat-sen University, Guangzhou, ChinaGuangzhou Urban Planning and Design Survey Research Institute, Guangzhou, ChinaGuangzhou Urban Planning and Design Survey Research Institute, Guangzhou, ChinaUrban forests play an important role in urban ecosystems. They can not only beautify the urban environment but also help protect biodiversity and maintain ecological balance. Effective urban forest management is a basic requirement to ensure sustainable development. Traditional urban forest management usually requires the investment of a lot of materials and labor to conduct field research. RGB high-resolution aerial images have emerged as an efficient source of data for use in the detection and mapping of individual trees in urban areas. In recent years, there has been impressive progress in the field of deep learning methods for use in object detection. Semi-supervised learning is an effective way to deal with the problem that deep learning requires a large amount of labeled data. In this paper, we proposed an improved faster region-based convolutional neural network (Faster R-CNN) with Swin transformer method. Based on existing datasets, the model was trained and then transferred to new datasets. The method was evaluated within three distinct urban areas: a green space, a residential area and a suburban area. The experimental results indicate that our method achieved higher performance than other Faster R-CNN models. This method provides a reference in automated individual tree detection based on high-resolution images in urban areas for urban forestry managers.https://ieeexplore.ieee.org/document/9765982/Individual tree detectionswin transformerfaster R-CNNurban forestry |
| spellingShingle | Lishuo Zhang Hong Lin Feng Wang Individual Tree Detection Based on High-Resolution RGB Images for Urban Forestry Applications IEEE Access Individual tree detection swin transformer faster R-CNN urban forestry |
| title | Individual Tree Detection Based on High-Resolution RGB Images for Urban Forestry Applications |
| title_full | Individual Tree Detection Based on High-Resolution RGB Images for Urban Forestry Applications |
| title_fullStr | Individual Tree Detection Based on High-Resolution RGB Images for Urban Forestry Applications |
| title_full_unstemmed | Individual Tree Detection Based on High-Resolution RGB Images for Urban Forestry Applications |
| title_short | Individual Tree Detection Based on High-Resolution RGB Images for Urban Forestry Applications |
| title_sort | individual tree detection based on high resolution rgb images for urban forestry applications |
| topic | Individual tree detection swin transformer faster R-CNN urban forestry |
| url | https://ieeexplore.ieee.org/document/9765982/ |
| work_keys_str_mv | AT lishuozhang individualtreedetectionbasedonhighresolutionrgbimagesforurbanforestryapplications AT honglin individualtreedetectionbasedonhighresolutionrgbimagesforurbanforestryapplications AT fengwang individualtreedetectionbasedonhighresolutionrgbimagesforurbanforestryapplications |