Der condition pour financement
Training School and Workshop on Dust Aerosol Detection and Monitoring
Sols tropicaux video cours universite agrocampus
Bonne ressources sur R en pedologie
Collection de cours et documents en Science du sol
Le spatial au service de la transition agro-écologique
base de donnee spatial avec qgis
Video creation de site web avec Rmarkdown all packages blogdown,distill
Rmarkdow distill blogdown site
Apprendre les statistiques avec les meilleurs ressources en francais
Data science with R correction
These resources below are awesome if you need to dive in spatial data science by using R
Reference pour apprendre shiny app
Conference sur r spatial OpenGeoHub
R for geographical data science
R programming machinee learning spatial granolarr
Spatial Data Programming with R sur differents annees
Spatial Predictions using Ensemble Machine Learning
Introduction to Spatial Data Programming with R
This website provides materials to learn about spatial data analysis and modeling with R. R is a widely used programming language and software environment for data science. R has advanced capabilities for managing spatial data; and it provides unparalleled opportunities for analyzing such data. Powered by Feed The Future
Intro to GIS and Spatial Analysis
This workshop is designed for the attendance of FOSS4G 2019. So basics knowledge in GIS is expected (simple features, projections and CRS, geometrical operations, etc.).
Introduction to geovisualization and web cartography
Geospatial Health Data: Modeling and Visualization with R-INLA and Shiny
The book covers the following topics:
- Types of spatial data and coordinate reference systems,
- Manipulating and transforming point, areal, and raster data,
- Retrieving high-resolution spatially referenced environmental data,
- Fitting and interpreting Bayesian spatial and spatio-temporal models with the R-INLA package,
- Modeling disease risk and quantifying risk factors in different settings,
- Creating interactive and static visualizations such as disease risk maps and time plots,
- Creating reproducible reports with R Markdown,
- Developing dashboards with flexdashboard,
- Building interactive Shiny web applications.
Les données spatiales avec R : French
L’objectif de ce cours est de présenter les éléments de manipulation des données spatiales à partir de R. Nous verrons ainsi :
- Ce que sont les données spatiales
- Comment lire des données spatiales ?
- Comment manipuler les données spatiales ?
- Comment visualiser les données spatiales ?
L’OSGeo et les logiciels libres en géomatique
Edzer Pebesma, Roger Bivand
This book introduces and explains the concepts underlying spatial data: points, lines, polygons, rasters, coverages, geometry attributes, data cubes, reference systems, as well as higher-level concepts including how attributes relate to geometries and how this affects analysis
Why R workshop with Roger Bivan Paula Tom Hengl
Conference sur le passé et l'avenir de l'usage du langage R dans le domaine spatial computing. les principaux packages qui de plus en plus utilisé dans le spatial sont exposé.
CASA0005 Geographic Information Systems and Science
After having taking this module, you should be able to:
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Develop a working knowledge of ArcMap, QGIS and R to support the application of GI Science techniques
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Visualise geographic information through producing appropriate maps to high cartographic standards
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Carry out spatial data management tasks (joining attribute to geometry data, cleaning data, converting between file formats and spatial reference systems)
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Interpret data and apply relevant spatial analyses (e.g. auto correlation/hot spot analysis, areal interpolation, point in polygon/buffer analysis, spatial statistical analysis) to answer a variety of spatial problems
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Explain and evaluate common issues with geographic data such as representation and uncertainty
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Apply and critique (spatial) statistical analysis techniques to infer relationships between spatial phenomena
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Experience the diversity of the global spatial data landscape and evaluate the relative drawbacks and merits of different spatial datasets
Modern Geospatial Data Analysis with R
A workshop by Zev Ross, ZevRoss Spatial Analysis, delivered at the RStudio conference 2020
Coference python spatial OpenGeoHub
Lessons: Use Remote sensing data in R or Python
Introduction to Python for Geographic Data Analysis
Deep Learning in remote sensing
Building a Crop Yield Prediction App in Senegal Using Satellite Imagery and Jupyter
Environmental Monitoring and Modelling
This unit brings together the theoretical concepts of landscape ecology with spatial analysis techniques from remote sensing and GIS to address landscape scale applications of relevance to natural resource management.
Computer vision for agriculture
Machine Learning on Earth Observation: ML4EO Bootcamp
Suitability with raster and vector and QGIS BLOG
ICLR 2020 Workshop on Computer Vision for Agriculture
Machine learning and deep learning in agricultural field
This document primarily lists resources for performing deep learning (DL) on satellite imagery. To a lesser extent Machine learning (ML, e.g. random forests, stochastic gradient descent) are also discussed, as are classical image processing techniques.
In this repo we gonna find many useful links for teaching and learning Geographic / Spatial Data Science, GIS and Statistics.
Geographic Data Science, a course taught by Dr. Dani Arribas-Bel in the Autumn of 2018 at the University of Liverpool.
Resources build by Michael Pyrcz, an Associate Professor at The University of Texas
A curated list of awesome tools, tutorials, code, helpful projects, links, stuff about Earth Observation and Geospatial stuff!