Stars
A template of writing manuscripts using Rmarkdown/Quarto
Buscombe & Ritchie (2018) Landscape Classification with Deep Neural Networks. Geosciences 2018, 8(7), 244
A library for creating complex UpSet plots with ggplot2 geoms
🎨 Visualisation toolbox for beautiful and publication-ready figures
statistical models of forest carbon potential and risks
Well-documented Python demonstrations for spatial data analytics, geostatistical and machine learning to support my courses.
A repository with Google Earth Engine workshops, courses and conferences.
Machine Learning From Scratch. Bare bones NumPy implementations of machine learning models and algorithms with a focus on accessibility. Aims to cover everything from linear regression to deep lear…
Here you will find various ready-to-use Stata schemes.
R package for spatial analysis and modelling of ecological systems
Super-Resolution of Sentinel-2 Images: Learning a Globally Applicable Deep Neural Network
Creates an analysis ready sentinel-1 SAR image collection in Google Earth Engine by applying additional border noise correction, speckle filtering and radiometric terrain normalization.
Gengchen Mai, Krzysztof Janowicz, Bo Yan, Rui Zhu, Ling Cai, Ni Lao. Multi-Scale Representation Learning for Spatial Feature Distributions using Grid Cells, In: Proceedings of ICLR 2020, Apr. 26 - …
Code associated with the paper on downscaling sun-induced fluorescence (SIF) data by G. Duveiller et al in ESSD (https://doi.org/10.5194/essd-2019-121)
This is for a short 1 hour class on how to write R packages
An easy-to-use Python library to download SAR imagery from Google Earth Engine
Python package with functions for assessing land change, drought vulnerability, and urbanization
Calculate textures from grey-level co-occurrence matrices (GLCMs) in R
Resilience Atlas - Evidence-based decision-making around resilience
Automated processing of historical aerial photography
Swirl course on landscape genetic data analysis with R