Song Li
7 Papers
Song Li is an academic researcher. The author has contributed to research in topics: Computer science & Support vector machine. The author has an hindex of 1, co-authored 1 publications.
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Papers
Wildfire Risk Assessment in Liangshan Prefecture, China Based on An Integration Machine Learning Algorithm
Lingxiao Xie,Rui Zhang,Junyu Zhan,Song Li,Age Shama,Runqing Zhan,Ting Wang,Jichao Lv,Xin Bao,Renzhe Wu +9 more
TL;DR: Zhang et al. as discussed by the authors explored the potential of integration machine learning algorithms to build wildfire risk assessment models based on analyzing fire data's spatial and temporal distribution, and selected 10 triggering factors of topography, meteorology, vegetation, and human activities, using frequency ratio (FR) to provide uniform data representation of triggering factors.
Ground Deformation Pattern Analysis and Evolution Prediction of Shanghai Pudong International Airport Based on PSI Long Time Series Observations
TL;DR: Wang et al. as mentioned in this paper built a ground deformation prediction model using the Long Short Term Memory (LSTM) neural network for the short-term prediction of the Shanghai Pudong International Airport (SPIA) deformation severity area.
remote sensing GNSS-IR Snow Depth Retrieval Based on the Fusion of Multi-Satellite SNR Data by the BP Neural Network
TL;DR: In this article , the authors proposed a novel GNSS-IR signal-to-noise ratio (SNR) retrieving snow depth method for fusing the available GPS-IR observations to obtain an accurate and reliable result.
Automatic recognition of landslides based on change detection
Song Li,Houqianga Hua +1 more
TL;DR: This project analyzes the current methods for the recognition of landslide disasters, and their applicability to the practice of landslide monitoring, and develops the new approach to identify landslides, which uses change detection based on texture analysis in multi-temporal imageries.
A Factor Analysis Backpropagation Neural Network Model for Vegetation Net Primary Productivity Time Series Estimation in Western Sichuan
TL;DR: Li et al. as discussed by the authors adopted Factor Analysis Backpropagation neural network model (FA-BP model) to acquire a high-accuracy and high-reliability NPP result without missing or empty areas by using a series of easily accessible datasets, such as meteorological data and remote sensing data.