Prepared by: Tom Hengl (OpenGeoHub), Ichsani Wheeler (OpenGeoHub), Robert A. McMillan (OpenGeoHub), Kris Deveria (OpenGeoHub), Leandro Parente (OpenGeoHub), Chris van Diemen (OpenGeoHub), Valentina Delconte (OpenGeoHub)

Land degradation is a systematic loss of function in terrestrial ecosystems: a serious drop in primary productivity, biomass and biodiversity. This means the land produces less than what it is able to at its natural capacity and even eventually deteriorates completely with little to no production at all. No plants. No animals. No water. Where this occurs extensively enough, it also changes the climate. Since the start of the industrial revolution 150 years ago, land…

Prepared by: Tom Hengl (OpenGeoHub)

Some popular tree-based Machine Learning (ML) algorithms such as Random Forest (RF) and/or Gradient Boosting have been criticized about over-fitting effects and prediction / extrapolation in feature space that can lead to serious blunders and artifacts. Extrapolation seems to be especially cumbersome for regression problems, and many at the order of magnitude less complex models seem to outperform RF when it comes to extrapolation. Serious artifacts due to extrapolation and over-fitting decreases confidence in RF, especially if the prediction intervals are also unrealistic. Here we demonstrate that an Ensemble approach, that combines both diverse learners…

Prepared by: Tom Hengl (OpenGeoHub) and Leandro Parente (OpenGeoHub)

Earth Observation, soil, terrain, land cover and land use, climate data are increasingly available for Africa for research and businesses. This tutorial explains: how to access the iSDAsoil property and nutrient maps for Africa and number of Sentinel-2 cloud-free bands and terrain variables, how to compute with it without a need to download terrabytes of data. A complete tutorial written using Rmarkdown is available here. To learn more about Cloud-Optimized GeoTIFFs and geocomputing in Python please visit also this tutorial.

iSDAsoil methodology and data

Innovative Solutions for Decision Agriculture Ltd (iSDA) is a social enterprise…

Prepared by: Leandro Parente (OpenGeoHub), Martijn Witjes (OpenGeoHub), Tom Hengl (OpenGeoHub), Codrina Maria Ilie (Terrasigna) and Martin Landa (CVUT Prague)

Our harmonized land cover product based on spatiotemporal Machine Learning (left) and the existing CORINE land cover product (right).

OpenGeoHub together with partners CVUT Prague, mundialis, Terrasigna, MultiOne and GiLAB, has released Open Data Science Europe data portal on 1st of March 2021. It comprises terabytes of gridded data available as Cloud-Optimized GeoTiffs and served via Geoserver. The project will collate, extend, harmonize, integrate, and distribute a wide range of freely available environmental and administrative data for Europe in the years to come. …

Perspectives from the OpenGeoHub 2020 Summer School

The key to achieving scientific advancements in health, development, and environmental issues really comes down to the idea that “good questions are taken as good questions no matter where they come from,” Edzer Pebesma of the Institute for Geoinformatics at the University of Münster, suggested as leading developers from the open geospatial software community gathered from August 16–22, 2020 at the Wageningen International Conference Centre, Netherlands, for the 16th OpenGeoHub Summer School.

As lecturers dove deep into the worlds of R (#rspatial) and Python, machine learning, remote sensing, data science, and applied geographic information systems, the conversation moved beyond mere…

On the critical role of remote sensing, legacy field observations, Data Science and reproducible research for monitoring environment

Co-authored by: Tom N. Collins, Ichsani Wheeler and Robert A. MacMillan

Since 2004, OpenStreetMap has been the main platform for building an Open Map of the world. More recently, the concept of building geo-information through distributed…

Originally published at on May 25, 2019.


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