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| DOI | 10.1186/s12942-024-00371-w |
| Using image segmentation models to analyse high-resolution earth observation data: new tools to monitor disease risks in changing environments | |
| 发表日期 | 2024 |
| ISSN | 1476-072X |
| 起始页码 | 23 |
| 结束页码 | 1 |
| 卷号 | 23期号:1 |
| 英文摘要 | Background In the near future, the incidence of mosquito-borne diseases may expand to new sites due to changes in temperature and rainfall patterns caused by climate change. Therefore, there is a need to use recent technological advances to improve vector surveillance methodologies. Unoccupied Aerial Vehicles (UAVs), often called drones, have been used to collect high-resolution imagery to map detailed information on mosquito habitats and direct control measures to specific areas. Supervised classification approaches have been largely used to automatically detect vector habitats. However, manual data labelling for model training limits their use for rapid responses. Open-source foundation models such as the Meta AI Segment Anything Model (SAM) can facilitate the manual digitalization of high-resolution images. This pre-trained model can assist in extracting features of interest in a diverse range of images. Here, we evaluated the performance of SAM through the Samgeo package, a Python-based wrapper for geospatial data, as it has not been applied to analyse remote sensing images for epidemiological studies.Results We tested the identification of two land cover classes of interest: water bodies and human settlements, using different UAV acquired imagery across five malaria-endemic areas in Africa, South America, and Southeast Asia. We employed manually placed point prompts and text prompts associated with specific classes of interest to guide the image segmentation and assessed the performance in the different geographic contexts. An average Dice coefficient value of 0.67 was obtained for buildings segmentation and 0.73 for water bodies using point prompts. Regarding the use of text prompts, the highest Dice coefficient value reached 0.72 for buildings and 0.70 for water bodies. Nevertheless, the performance was closely dependent on each object, landscape characteristics and selected words, resulting in varying performance.Conclusions Recent models such as SAM can potentially assist manual digitalization of imagery by vector control programs, quickly identifying key features when surveying an area of interest. However, accurate segmentation still requires user-provided manual prompts and corrections to obtain precise segmentation. Further evaluations are necessary, especially for applications in rural areas. |
| 英文关键词 | Segment anything model; Mosquito-borne diseases; UAV; Drone; Remote sensing. |
| 语种 | 英语 |
| WOS研究方向 | Public, Environmental & Occupational Health |
| WOS类目 | Public, Environmental & Occupational Health |
| WOS记录号 | WOS:001227410300001 |
| 来源期刊 | INTERNATIONAL JOURNAL OF HEALTH GEOGRAPHICS
 |
| 文献类型 | 期刊论文 |
| 条目标识符 | http://gcip.llas.ac.cn/handle/2XKMVOVA/305037 |
| 作者单位 | University of Glasgow; University of Glasgow; Institut National de la Sante et de la Recherche Medicale (Inserm); Sorbonne Universite; Centre National de la Recherche Scientifique (CNRS); Assistance Publique Hopitaux Paris (APHP); Inria; Ifakara Health Institute; Universidad Peruana Cayetano Heredia; National University of Singapore; National University of Singapore |
| 推荐引用方式 GB/T 7714 | . Using image segmentation models to analyse high-resolution earth observation data: new tools to monitor disease risks in changing environments[J],2024,23(1). |
| APA | (2024).Using image segmentation models to analyse high-resolution earth observation data: new tools to monitor disease risks in changing environments.INTERNATIONAL JOURNAL OF HEALTH GEOGRAPHICS,23(1). |
| MLA | "Using image segmentation models to analyse high-resolution earth observation data: new tools to monitor disease risks in changing environments".INTERNATIONAL JOURNAL OF HEALTH GEOGRAPHICS 23.1(2024). |
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