首页|Researcher from Chongqing University of Posts and Telecommunications Discusses Findings in Machine Learning (Extracting Shrubland in Deserts from Medium-Resolution Remote-Sensing Data at Large Scale)
Researcher from Chongqing University of Posts and Telecommunications Discusses Findings in Machine Learning (Extracting Shrubland in Deserts from Medium-Resolution Remote-Sensing Data at Large Scale)
扫码查看
点击上方二维码区域，可以放大扫码查看
原文链接
NETL
NSTL
Mdpi
Investigators publish new report on artificial intelligence. According to news originating from Chongqing, People’s Republic of China, by NewsRx editors, the research stated, “Shrubs are important ecological barriers in desert regions and an important component of global carbon estimation.” Financial supporters for this research include National Key Research And Development Program of China; Science And Technology Fundamental Resources Investigation Program. Our news correspondents obtained a quote from the research from Chongqing University of Posts and Telecommunications: “However, the shrubland in deserts has been hardly presented, although many high-quality land cover datasets with a 10 m scale based on remote-sensing data have been publicly released products. Therefore, the underestimation of carbon storage is inevitable with the absence of desert shrublands. The existing land-cover datasets have been analyzed and compared, and it has been found that the reason for missing the shrubland in deserts is mainly indued by the absence of shrubland samples, which are easy to neglect and difficult to retrieve. In this study, we developed a semi-automatic method to extract shrubland samples in deserts as the updated input for the machine-learning method. Firstly, the initial samples of desert shrublands were identified from the very high spatial-resolution (0.3 0.5 m) imagery on GEE, and the maximum NDVI from Sentinel-2 was used for double-checking. Secondly, a feature-based method was used to learn the feature from the initial samples and a similarity-based searching method was employed to automatically expand the samples.”
Chongqing University of Posts and TelecommunicationsChongqingPeople’s Republic of ChinaAsiaCyborgsEmerging TechnologiesMachine LearningRemote Sensing
NETL
NSTL
Mdpi
