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Name: MTGeo
Type: User
Name: MTGeo
Type: User
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.
In this repository I will try to learn Python for my works
Interactive Tutorials with R Markdown
Experiments in climatological time series analysis using deep learning
Mask R-CNN for object detection and instance segmentation on Keras and TensorFlow
Miscellaneous codes
ModL2T: hybrid MODIS and Landsat algorithm in Google Earth Engine for estimating post-monsoon burned area from agricultural fires in northwestern India
Opening and visualizing a netCDF file in Python
How does fire behave at night? Has that changed?
Tutorial of fundamental remote sensing and GIS methodologies using open source software in python
Pixel-wise regression between two raster time-series: Rainfall and Sea Surface Temperature
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.
Trend detection in time series of remotely sensed data using PolyTrend algorithm
💌 Create simple, beautiful personal websites and landing pages using only R Markdown.
An introduction to data science using Python and Pandas with Jupyter notebooks
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.
Python for GIS and Remote Sensing Class for St. Louis University, Spring 2017 Semester
Radiometric Slope Correction of Sentinel-1 data on Google Earth Engine
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
Google Earth Engine for R
Resources for deep learning with satellite & aerial imagery
Deep Neural Network with keras-R (TensorFlow GUP backend): Satellite-Image Classification
Sample sample scripts and notebooks on processing satellite imagery
Road and Building Segmentation in Satellite Imagery
land use land cover change script using space and time models: Yucatan and Ecuador
I have a suite of R Markdown templates for academic manuscripts, beamer presentations, and syllabi. I share them here.
A declarative, efficient, and flexible JavaScript library for building user interfaces.
🖖 Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.
TypeScript is a superset of JavaScript that compiles to clean JavaScript output.
An Open Source Machine Learning Framework for Everyone
The Web framework for perfectionists with deadlines.
A PHP framework for web artisans
Bring data to life with SVG, Canvas and HTML. 📊📈🎉
JavaScript (JS) is a lightweight interpreted programming language with first-class functions.
Some thing interesting about web. New door for the world.
A server is a program made to process requests and deliver data to clients.
Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.
Some thing interesting about visualization, use data art
Some thing interesting about game, make everyone happy.
We are working to build community through open source technology. NB: members must have two-factor auth.
Open source projects and samples from Microsoft.
Google ❤️ Open Source for everyone.
Alibaba Open Source for everyone
Data-Driven Documents codes.
China tencent open source team.