Deep Learning Segmentation of Satellite Imagery Identifies Aquatic Vegetation Associated with Snail Intermediate Hosts of Schistosomiasis in Senegal, Africa
Deep Learning Segmentation of Satellite Imagery Identifies Aquatic Vegetation Associated with Snail Intermediate Hosts of Schistosomiasis in Senegal, Africa
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DOI：
10.3390/rs14061345
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发表时间：
2022-03-01
期刊：
影响因子：
5
通讯作者：
De Leo, Giulio A.
中科院分区：
文献类型：
--
作者：
Liu, Zac Yung-Chun;Chamberlin, Andrew J.;De Leo, Giulio A.
血吸虫病是一种使人衰弱的贫穷寄生虫病，影响全世界2亿多人，主要在撒哈拉以南非洲，显然与热带和亚热带地区水坝和水资源管理基础设施的建设有关。与水基础设施建设相关的水文和盐度变化可能为水生植被创造有利条件，为血吸虫寄生虫的中间蜗牛宿主提供适宜的栖息地。非洲有成千上万的大小水库、灌溉渠和水坝正在开发或建设中，因此，在快速变化的生态系统中，准确评估作为血吸虫病淡水蜗牛中间宿主栖息地的高风险环境的空间分布至关重要。然而，监测蜗牛的标准技术是劳动密集型的，耗时的，并且提供的信息仅限于可以人工采样的小区域。因此，在最需要控制血吸虫病的低收入国家，在确定潜在传播热点以实施有针对性的医疗和环境干预措施方面面临巨大挑战。在这项研究中，我们开发了一个新的框架，通过整合卫星数据、高清、低成本无人机图像和人工智能（AI）驱动的计算机视觉技术（称为语义分割），绘制塞内加尔河流域大空间尺度上适宜蜗牛栖息地的空间分布。建立了一个深度学习模型（U-Net）来自动分析高分辨率卫星图像，以生成水生植被的分割图，具有快速和强大的广义预测，比更常用的随机森林方法更准确。对疾病传播风险最高地区的准确和最新知识可以通过瞄准携带疾病的蜗牛栖息地来提高控制干预措施的有效性。随着这一新框架的部署，地方政府或卫生行为体可以更好地将环境干预措施定位到最需要的地方和时间，以实现消除血吸虫病的目标。
Schistosomiasis is a debilitating parasitic disease of poverty that affects more than 200 million people worldwide, mostly in sub-Saharan Africa, and is clearly associated with the construction of dams and water resource management infrastructure in tropical and subtropical areas. Changes to hydrology and salinity linked to water infrastructure development may create conditions favorable to the aquatic vegetation that is suitable habitat for the intermediate snail hosts of schistosome parasites. With thousands of small and large water reservoirs, irrigation canals, and dams developed or under construction in Africa, it is crucial to accurately assess the spatial distribution of high-risk environments that are habitat for freshwater snail intermediate hosts of schistosomiasis in rapidly changing ecosystems. Yet, standard techniques for monitoring snails are labor-intensive, time-consuming, and provide information limited to the small areas that can be manually sampled. Consequently, in low-income countries where schistosomiasis control is most needed, there are formidable challenges to identifying potential transmission hotspots for targeted medical and environmental interventions. In this study, we developed a new framework to map the spatial distribution of suitable snail habitat across large spatial scales in the Senegal River Basin by integrating satellite data, high-definition, low-cost drone imagery, and an artificial intelligence (AI)-powered computer vision technique called semantic segmentation. A deep learning model (U-Net) was built to automatically analyze high-resolution satellite imagery to produce segmentation maps of aquatic vegetation, with a fast and robust generalized prediction that proved more accurate than a more commonly used random forest approach. Accurate and up-to-date knowledge of areas at highest risk for disease transmission can increase the effectiveness of control interventions by targeting habitat of disease-carrying snails. With the deployment of this new framework, local governments or health actors might better target environmental interventions to where and when they are most needed in an integrated effort to reach the goal of schistosomiasis elimination.