Urban Greening Analysis: A Multimodal Large Language Model for Pinpointing Vegetation Areas in Adverse Weather Conditions
Urban green spaces are an important part of the urban ecosystem and hold significant ecological value. To effectively protect these green spaces, urban managers urgently need to identify them and monitor their changes. Common urban vegetation positioning methods use deep learning segmentation models...
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MDPI AG
2025-06-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/17/12/2058 |
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| author | Hanzhang Liu Shijie Yang Chengwu Long Jiateng Yuan Qirui Yang Jiahua Fan Bingnan Meng Zhibo Chen Fu Xu Chao Mou |
| author_facet | Hanzhang Liu Shijie Yang Chengwu Long Jiateng Yuan Qirui Yang Jiahua Fan Bingnan Meng Zhibo Chen Fu Xu Chao Mou |
| author_sort | Hanzhang Liu |
| collection | DOAJ |
| description | Urban green spaces are an important part of the urban ecosystem and hold significant ecological value. To effectively protect these green spaces, urban managers urgently need to identify them and monitor their changes. Common urban vegetation positioning methods use deep learning segmentation models to process street view data in urban areas, but this is usually inefficient and inaccurate. The main reason is that they are not applicable to the variable climate of urban scenarios, especially performing poorly in adverse weather conditions such as heavy fog that are common in cities. Additionally, these algorithms also have performance limitations such as inaccurate boundary area positioning. To address these challenges, we propose the UGSAM method that utilizes the high-performance multimodal large language model, the Segment Anything Model (i.e., SAM). In the UGSAM, a dual-branch defogging network WRPM is incorporated, which consists of the dense fog network FFA-Net, the light fog network LS-UNet, and the feature fusion network FIM, achieving precise identification of vegetation areas in adverse urban weather conditions. Moreover, we have designed a micro-correction network SCP-Net suitable for specific urban scenarios to further improve the accuracy of urban vegetation positioning. The UGSAM was compared with three classic deep learning algorithms and the SAM. Experimental results show that under adverse weather conditions, the UGSAM performs best in OA (0.8615), mIoU (0.8490), recall (0.9345), and precision (0.9027), surpassing the baseline model FCN (OA improvement 28.19%) and PointNet++ (OA improvement 30.02%). Compared with the SAM, the UGSAM improves the segmentation accuracy by 16.29% under adverse weather conditions and by 1.03% under good weather conditions. This method is expected to play a key role in the analysis of urban green spaces under adverse weather conditions and provide innovative insights for urban development. |
| format | Article |
| id | doaj-art-72d778b7a47b487f970f9c1b46f1cdef |
| institution | DOAJ |
| issn | 2072-4292 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj-art-72d778b7a47b487f970f9c1b46f1cdef2025-08-20T03:16:39ZengMDPI AGRemote Sensing2072-42922025-06-011712205810.3390/rs17122058Urban Greening Analysis: A Multimodal Large Language Model for Pinpointing Vegetation Areas in Adverse Weather ConditionsHanzhang Liu0Shijie Yang1Chengwu Long2Jiateng Yuan3Qirui Yang4Jiahua Fan5Bingnan Meng6Zhibo Chen7Fu Xu8Chao Mou9School of Information Science and Technology (School of Artificial Intelligence), Beijing Forestry University, Beijing 100083, ChinaSchool of Information Science and Technology (School of Artificial Intelligence), Beijing Forestry University, Beijing 100083, ChinaSchool of Computer Science and Technology, East China Normal University, Shanghai 200062, ChinaSchool of Information Science and Technology (School of Artificial Intelligence), Beijing Forestry University, Beijing 100083, ChinaSchool of Information Science and Technology (School of Artificial Intelligence), Beijing Forestry University, Beijing 100083, ChinaSchool of Information Science and Technology (School of Artificial Intelligence), Beijing Forestry University, Beijing 100083, ChinaGeneral Forestry Station of Beijing Municipality, Beijing 100029, ChinaSchool of Information Science and Technology (School of Artificial Intelligence), Beijing Forestry University, Beijing 100083, ChinaSchool of Information Science and Technology (School of Artificial Intelligence), Beijing Forestry University, Beijing 100083, ChinaSchool of Information Science and Technology (School of Artificial Intelligence), Beijing Forestry University, Beijing 100083, ChinaUrban green spaces are an important part of the urban ecosystem and hold significant ecological value. To effectively protect these green spaces, urban managers urgently need to identify them and monitor their changes. Common urban vegetation positioning methods use deep learning segmentation models to process street view data in urban areas, but this is usually inefficient and inaccurate. The main reason is that they are not applicable to the variable climate of urban scenarios, especially performing poorly in adverse weather conditions such as heavy fog that are common in cities. Additionally, these algorithms also have performance limitations such as inaccurate boundary area positioning. To address these challenges, we propose the UGSAM method that utilizes the high-performance multimodal large language model, the Segment Anything Model (i.e., SAM). In the UGSAM, a dual-branch defogging network WRPM is incorporated, which consists of the dense fog network FFA-Net, the light fog network LS-UNet, and the feature fusion network FIM, achieving precise identification of vegetation areas in adverse urban weather conditions. Moreover, we have designed a micro-correction network SCP-Net suitable for specific urban scenarios to further improve the accuracy of urban vegetation positioning. The UGSAM was compared with three classic deep learning algorithms and the SAM. Experimental results show that under adverse weather conditions, the UGSAM performs best in OA (0.8615), mIoU (0.8490), recall (0.9345), and precision (0.9027), surpassing the baseline model FCN (OA improvement 28.19%) and PointNet++ (OA improvement 30.02%). Compared with the SAM, the UGSAM improves the segmentation accuracy by 16.29% under adverse weather conditions and by 1.03% under good weather conditions. This method is expected to play a key role in the analysis of urban green spaces under adverse weather conditions and provide innovative insights for urban development.https://www.mdpi.com/2072-4292/17/12/2058urban green spacesstreetscape imagerymultimodal large language modeldeep learningSegment Anything Model |
| spellingShingle | Hanzhang Liu Shijie Yang Chengwu Long Jiateng Yuan Qirui Yang Jiahua Fan Bingnan Meng Zhibo Chen Fu Xu Chao Mou Urban Greening Analysis: A Multimodal Large Language Model for Pinpointing Vegetation Areas in Adverse Weather Conditions Remote Sensing urban green spaces streetscape imagery multimodal large language model deep learning Segment Anything Model |
| title | Urban Greening Analysis: A Multimodal Large Language Model for Pinpointing Vegetation Areas in Adverse Weather Conditions |
| title_full | Urban Greening Analysis: A Multimodal Large Language Model for Pinpointing Vegetation Areas in Adverse Weather Conditions |
| title_fullStr | Urban Greening Analysis: A Multimodal Large Language Model for Pinpointing Vegetation Areas in Adverse Weather Conditions |
| title_full_unstemmed | Urban Greening Analysis: A Multimodal Large Language Model for Pinpointing Vegetation Areas in Adverse Weather Conditions |
| title_short | Urban Greening Analysis: A Multimodal Large Language Model for Pinpointing Vegetation Areas in Adverse Weather Conditions |
| title_sort | urban greening analysis a multimodal large language model for pinpointing vegetation areas in adverse weather conditions |
| topic | urban green spaces streetscape imagery multimodal large language model deep learning Segment Anything Model |
| url | https://www.mdpi.com/2072-4292/17/12/2058 |
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