当前位置: X-MOL 学术Environ. Model. Softw. › 论文详情
Deep Learning as a Wetland Classification Approach using LiDAR Topographic-Derived and NDVI Input Data
Environmental Modelling & Software ( IF 4.552 ) Pub Date : 2020-02-14 , DOI: 10.1016/j.envsoft.2020.104665
Gina L. O’Neil; Jonathan L. Goodall; Madhur Behl; Linnea Saby

Automated and accurate wetland identification algorithms are increasingly important for wetland conservation and environmental planning. Deep learning for wetland identification is an emerging field that shows promise for advancing these efforts. Deep learning is unique to traditional machine learning techniques for its ability to consider the spatial context of object characteristics within a landscape training the algorithms, which limits their application for many environmental applications including wetland identification. Using four study sites across Virginia with field delineated wetlands, we provide insight into the potential for deep learning for wetland detection from limited, but typical, wetland delineation training data. Our proposed workflow performs a wetland semantic segmentation using DeepNets, a deep learning architecture for remote sensing data, and an input dataset consisting of high-resolution topographic indices and the Normalized Difference Vegetation Index. Results show that models trained and evaluated for a single site were able to achieve high accuracy (up to 91% recall and 56% precision) and similar accuracy can be obtained for models trained across multiple sites (up to 91% recall and 57% precision). Through this analysis we found that, across all sites, input data configurations taking advantage of hydrologic properties derived from elevation data consistently outperformed models using the elevation data directly, showing the benefit of physically-informed inputs in deep learning training for wetland identification. By refining the wetland identification workflow presented in this paper and collecting additional training data across landscapes, there is potential for deep learning algorithms to support a range wetland conservation efforts.
更新日期:2020-02-20

 

全部期刊列表>>
智控未来
聚焦商业经济政治法律
跟Nature、Science文章学绘图
控制与机器人
招募海内外科研人才,上自然官网
隐藏1h前已浏览文章
课题组网站
新版X-MOL期刊搜索和高级搜索功能介绍
ACS材料视界
x-mol收录
湖南大学化学化工学院刘松
上海有机所
廖良生
南方科技大学
西湖大学
伊利诺伊大学香槟分校
徐明华
中山大学化学工程与技术学院
试剂库存
天合科研
down
wechat
bug