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amazon

Satellite-Based Estimates Reveal Widespread Forest Degradation in the Amazon

bayts

Set of tools to apply the probabilistic approach of Reiche et al. (2015, 2018) to combine multiple optical and/or Radar satellite time series and to detect deforestation/forest cover loss in near real-time. The package includes functions to apply the approach to both, single pixel time series and raster time series. Examples and test data are provided below.

bfast_preanalysis

cusum_point_analysis

dcs-stamps-ps

Developer Cloud Sandbox for StaMPS PS processing

ee-preproc

eehelpers

contains pre-processing functions for various satellite missions on earth-engine

eesar

python lib for pre-processing of Sentinel-1 around the python earthengine-api

esbae

python module that facilitates the process of creating activity data for MRV reporting and much more

extract_point_ts

SEPAL module for the GEE backed extraction of time-series from a large point geometry feature collection

extract_ts_data

extracting change info from various algorithms over points

landsatgeepy

opensarkit

Tools for Automatic Preprocessing of SAR Imagery

opensartoolkit

High-level functionality for the inventory, download and pre-processing of Sentinel-1 data in the python language.

ost_demos

ost_notebooks

pre_esbae_notebooks

pyimpute

Spatial classification and regression using Scikit-learn and Rasterio

pyradar

[Python3] PyRadar fork + despeckle filters NUMBA accelerated

sampling-handler

sar

Despeckling Synthetic Aperture Radar Images using a Deep Residual CNN

sepal

Geographical Data Processing in the Cloud

sepal-doc

Documentation of the SEPAL platform (https://sepal.io)

sepalostjupyter

seplanet

python package to ease access to planet nicfi level 2 data

tmf_sepal

Tropical Moist Forest wrapper for SEPAL

unmixing

Interactive tools for spectral mixture analysis of multispectral raster data in Python