Pinned Repositories
aiopenclass
aiopenclass
AlgorithmPractice
autoresnetR
The R-version package of autoencoder-based residual network
baggingrnet
BasicAlgorithms
bertmultitask
Enhancing BERT's Performance on Downstream Tasks via Multitask Fine-Tuning and Ensembling
deeplearning_geoscience
《深度学习：原理及遥感地学分析》一书的注解及答疑
resautonet
resmcsegpub
sptemExp
The approach of ensemble spatiotemporal mixed models is to make reliable estimation of air pollutant concentrations at high resolutions. (1) Extraction of covariates from the satellite images such as GeoTiff and NC4 raster (e.g NDVI, AOD, and meteorological parameters); (2) Generation of temporal basis functions to simulate the seasonal trends in the study regions; (3) Generation of the regional monthly or yearly means of air pollutant concentration; (4) Generation of Thiessen polygons and spatial effect modeling; (5) Ensemble modeling for spatiotemporal mixed models, supporting multi-core parallel computing; (6) Integrated predictions with or without weights of the model's performance, supporting multi-core parallel computing; (7) Constrained optimization to interpolate the missing values; (8) Generation of the grid surfaces of air pollutant concentrations at high resolution; (9) Block kriging for regional mean estimation at multiple scales.
lspatial's Repositories
lspatial/resautonet
lspatial/sptemExp
The approach of ensemble spatiotemporal mixed models is to make reliable estimation of air pollutant concentrations at high resolutions. (1) Extraction of covariates from the satellite images such as GeoTiff and NC4 raster (e.g NDVI, AOD, and meteorological parameters); (2) Generation of temporal basis functions to simulate the seasonal trends in the study regions; (3) Generation of the regional monthly or yearly means of air pollutant concentration; (4) Generation of Thiessen polygons and spatial effect modeling; (5) Ensemble modeling for spatiotemporal mixed models, supporting multi-core parallel computing; (6) Integrated predictions with or without weights of the model's performance, supporting multi-core parallel computing; (7) Constrained optimization to interpolate the missing values; (8) Generation of the grid surfaces of air pollutant concentrations at high resolution; (9) Block kriging for regional mean estimation at multiple scales.
lspatial/deeplearning_geoscience
《深度学习：原理及遥感地学分析》一书的注解及答疑
lspatial/resmcsegpub
lspatial/autoresnetR
The R-version package of autoencoder-based residual network
lspatial/aiopenclass
aiopenclass
lspatial/AlgorithmPractice
lspatial/baggingrnet
lspatial/BasicAlgorithms
lspatial/bertmultitask
Enhancing BERT's Performance on Downstream Tasks via Multitask Fine-Tuning and Ensembling
lspatial/Deep-Learning-Using-MXNET-image
Deep learning using MXNet
lspatial/docker-compose-files
Some typical docker compose templates.
lspatial/downscaledl
lspatial/extractebk
Extracting the pollutant surface from images
lspatial/geographnet
Library of Geographic Graph Hybrid Network
lspatial/geographnetdata
lspatial/geometaseg
lspatial/Handwritten-Digit-Recognition
Handwritten Digit Recognition using MP and CNN.
lspatial/lspatial
Technical blogs
lspatial/lspatial.github.io
Blogs for big data miner
lspatial/phygeograph
Library of physics-aware geograph hybrid network
lspatial/spatioEnv
Lib for processing the spatial data and extracting the relevant environmental covariates
lspatial/spavar
Hierarchical Spatial Bayesian Models to Evaluate Environmental Influence on Health Outcomes
lspatial/spstatics_parallel
C++ Codes for parallel algorithms of spatial statistics
lspatial/sptemUS
lspatial/techblog
Technical blogs.
lspatial/test123