Ziyu Yan
8 Papers
4 Citations
Ziyu Yan is an academic researcher. The author has contributed to research in topics: Computer science & Environmental science. The author has an hindex of 1, co-authored 1 publications.
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Papers
Investigating urban heat-related health risks based on local climate zones: A case study of Changzhou in China
Lei Ma,Guoan Huang,Brian Alan Johnson,Zhenjie Chen,Manchun Li,Ziyu Yan,Wenfeng Zhan,Heng Lu,Weiqiang He,Dongjie Lian +9 more
TL;DR: Li et al. as discussed by the authors proposed an LCZ-based risk assessment approach for assessing heat-related health risks, which can be used for informing and implementing area-level urban planning strategies.
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Comparing Object-Based and Pixel-Based Methods for Local Climate Zones Mapping with Multi-Source Data
TL;DR: Zhang et al. as mentioned in this paper compared object-based and pixel-based local climate zones (LCZs) mapping with multi-source data in detail, and concluded that the object based method is capable of LCZ mapping and performs better than the pixel based method under the same training condition unless in undersegmentation cases.
Patch-Based Local Climate Zones Mapping and Population Distribution Pattern in Provincial Capital Cities of China
Liang Zhou,Lei Ma,Brian Alan Johnson,Ziyu Yan,Feixue Li,Manchun Li +5 more
- 25 Jul 2022
TL;DR: Wang et al. as mentioned in this paper constructed a high-quality sample dataset from Chinese cities and presented a patch-based classification framework that employs chessboard segmentation and multi-seasonal images for LCZ mapping.
An Integrated Method for Estimating Forest-Canopy Closure Based on UAV LiDAR Data
TL;DR: In this paper , a method for estimating forest canopy closure (FCC) accurately using algorithm integration with an optimal window size for treetop detection and an optimal algorithm for crown-boundary extraction using UAV LiDAR data in various scenes was proposed.
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High Wind Speed Inversion Model of CYGNSS Sea Surface Data Based on Machine Learning
TL;DR: In this paper, the authors proposed a GNSS-R technique combined with a machine learning method to invert high wind speed at sea surface using L1-level satellite-based data from the Cyclone Global Navigation Satellite System (CYGNSS), together with the European Centre for Medium-Range Weather Forecasts (ECMWF) and the National Centers for Environmental Prediction (NCEP) data, constitute the original sample set, which is processed and trained with Support Vector Regression (SVR), the combination of Principal Component Analysis (PCA) and SVR
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