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MTGeo's Projects

lclu_frenchguiana icon lclu_frenchguiana

In order to map LCLU in french-Guyana, few scripts were developped or adapted to enable either to automaticaly map either to explore cloudless mosaic and even automaticaly detect floodings with Sentinel 1 SAR data.

learnr icon learnr

Interactive Tutorials with R Markdown

mask_rcnn icon mask_rcnn

Mask R-CNN for object detection and instance segmentation on Keras and TensorFlow

modl2t-burned-area icon modl2t-burned-area

ModL2T: hybrid MODIS and Landsat algorithm in Google Earth Engine for estimating post-monsoon burned area from agricultural fires in northwestern India

open-geo-tutorial icon open-geo-tutorial

Tutorial of fundamental remote sensing and GIS methodologies using open source software in python

pixel_level_land_classification icon pixel_level_land_classification

Tutorial demonstrating how to create a semantic segmentation (pixel-level classification) model to predict land cover from aerial imagery. This model can be used to identify newly developed or flooded land. Uses ground-truth labels and processed NAIP imagery provided by the Chesapeake Conservancy.

polytrend icon polytrend

Trend detection in time series of remotely sensed data using PolyTrend algorithm

postcards icon postcards

💌 Create simple, beautiful personal websites and landing pages using only R Markdown.

python-practical-application-on-climate-variability-studies icon python-practical-application-on-climate-variability-studies

This tutorial is a companion volume of Matlab versionm but add more. Main objective is the transference of know-how in practical applications and management of statistical tools commonly used to explore meteorological time series, focusing on applications to study issues related with the climate variability and climate change. This tutorial starts with some basic statistic for time series analysis as estimation of means, anomalies, standard deviation, correlations, arriving the estimation of particular climate indexes (Niño 3), detrending single time series and decomposition of time series, filtering, interpolation of climate variables on regular or irregular grids, leading modes of climate variability (EOF or HHT), signal processing in the climate system (spectral and wavelet analysis). In addition, this tutorial also deals with different data formats such as CSV, NetCDF, Binary, and matlab'mat, etc. It is assumed that you have basic knowledge and understanding of statistics and Python.

remote-sensing-resistance icon remote-sensing-resistance

Does heterogeneity in forest structure make a forest resistant to wildfire? That is, does greater heterogeneity decrease wildfire severity when a fire inevitably occurs? A collaborative effort co-authored by: Michael J. Koontz, Malcolm P. North, Chhaya M. Werner, Stephen E. Fick, and Andrew M. Latimer

rgee icon rgee

Google Earth Engine for R

skeyenet icon skeyenet

Road and Building Segmentation in Satellite Imagery

space_time_lucc icon space_time_lucc

land use land cover change script using space and time models: Yucatan and Ecuador

svm-r-markdown-templates icon svm-r-markdown-templates

I have a suite of R Markdown templates for academic manuscripts, beamer presentations, and syllabi. I share them here.

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