hannameyer Goto Github PK
Name: Hanna Meyer
Type: User
Bio: Researcher at the Institute of Landscape Ecology at Münster University, working on remote sensing of the environment, machine learning, spatial data analysis, R
Name: Hanna Meyer
Type: User
Bio: Researcher at the Institute of Landscape Ecology at Münster University, working on remote sensing of the environment, machine learning, spatial data analysis, R
Scripts and functions for a monitoring of air temperature in Antarctica using MODIS data and machine learning
Repository for all kinds of tasks within the project Carbon4D
Developer Version of the R package CAST: Caret Applications for Spatio-Temporal models
data used in tutorials to show the use of the R package CAST
This is the official repository for the online course 'Cubes & Clouds'
Machine learning for spatial data: This repository contains the R-scripts for the analysis described in the paper "Importance of spatial predictor variable selection in machine learning applications - Moving from data reproduction to spatial prediction" submitted to Ecological Modelling
slides and material for eo4statistics
Material for the 2nd joint field course of the EarthObservationNetwork
Material for the 3nd joint field course of the EarthObservationNetwork
Materials for the 4nd joint field course of the EarthObservationNetwork
Material for the course on machine-learning based environmental monitoring at geostat 2018
Material for the conference contribution
Material for the course "Learning from Spatial data" in Poznan, March 2023
Case study material for the paper on mapping the area of applicability
Materials for the workshop "Linking genotype, phenotype and the environment" at WSL Zürich 2019
Material for the workshop on machine learning for environmental spatial mapping in Göttingen, January 2024
R package to handle MSG SEVIRI data that have been processed with the Marburg MSG processing routines
Ressources for the session on machine learning and remote sensing at the OpenGeoHub Summer school in Münster 2019
Ressources for the session on machine learning and remote sensing at the OpenGeoHub Summer school in Wageningen 2020
Material for the tutorial on "Mapping the Area of Applicability of spatial prediction models" at the OpenGeoHub Summer School 2021
Ressources for the session on machine learning, cross validation and the area of applicability at the OpenGeoHub Summer school in Siegburg 2022
R package of supporting functions for spatio-temporal models using machine learning
Material for the course on machine learning taught at the UAV School in Göttingen (26.-30.10.2020)
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Open source projects and samples from Microsoft.
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Data-Driven Documents codes.
China tencent open source team.