A pure Julia package for querying and downloading Landsat data.
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Updated
May 15, 2024 - Julia
A pure Julia package for querying and downloading Landsat data.
A Python package for interactive geospatial analysis and visualization with Google Earth Engine.
Earth Observation Data Access Gateway
A ninja python package that unifies the Google Earth Engine ecosystem.
Awesome Spectral Indices for the Google Earth Engine JavaScript API (Code Editor).
A list of all the scale and offset parameters for each raster dataset in Google Earth Engine.
An interactive toolbox for downloading satellite imagery, applying image segmentation models, mapping shoreline positions and more. The mapping extension for CoastSat and Zoo.
Pipeline for remotely sensed imagery. The pipeline processes satellite imagery alongside auxiliary data in multiple steps to arrive at a set of trend files related to land-cover changes.
Generating intertidal elevation, exposure and extents from satellite earth observation data and ocean tide modelling
Repository for Digital Earth Australia Jupyter Notebooks: tools and workflows for geospatial analysis with Open Data Cube and Xarray
Satellite image time series in R
Remote-sensing opensource python library reading optical and SAR sensors, loading and stacking bands, clouds, DEM and spectral indices in a sensor-agnostic way.
Laboratórios didáticos de Geoprocessamento - Poli-USP
A ready-to-use curated list of Spectral Indices for Remote Sensing applications.
Awesome Spectral Indices in Julia
Atmospheric correction processor for high spatial resolution multispectral satellite images
On-Demand Earth System Data Cubes (ESDCs) in Python
A simple CLI interface to generate urls for Landsat Collection 2 Level 1 product bundles and download them
A Bayesian hierarchical model that quantifies long-term annual land surface phenology from sparse time series of vegetation indices.
GRASS GIS Addon to remove clouds (e.g. Sentinel-2, Landsat), aiming at filling raster gaps using r.series and r.series.lwr and aggregates temporally the maps of a space time raster dataset by a user defined granularity using t.rast.aggregate
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