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Search and download of Sentinel, Landsat and Planet crops.

Project description

Time Series Downloader (TSD)

Build Status

Automatic download of Sentinel, Landsat and Planet crops.

Carlo de Franchis, CMLA, ENS Cachan, Université Paris-Saclay, 2016-19

With contributions from Enric Meinhardt-Llopis, Axel Davy and Tristan Dagobert.

The main source code repository for this software is

Installation and dependencies


The main dependency is GDAL. All the others can be installed with pip as shown in the next section.

On Ubuntu

gdal can be installed with apt-get. In order to get a recent version we recommend adding the PPA ubuntugis-unstable (first command below):

sudo add-apt-repository ppa:ubuntugis/ubuntugis-unstable
sudo apt-get update
sudo apt-get install libgdal-dev gdal-bin

On macOS

There are several ways of installing gdal. I recommend option 1 as it gives a version of gdal 2.3 that works with JP2 files.

Note: a shell script installing all the needed stuff (brew, python, gdal...) on an empty macOS system is given in the file

Option 1: using the GDAL Complete Compatibility Framework.

Download and install the .dmg file. Update your PATH after the installation by running this command:

export PATH="/Library/Frameworks/GDAL.framework/Programs:$PATH"

Copy it in your ~/.profile.

Option 2: using brew

brew install gdal --with-complete

Note that this version doesn't support JP2 files (hence it will fail to get Sentinel-2 crops).

Install TSD as a python package

Once gdal is installed on your machine you can install tsd with pip:

git clone
cd tsd
pip install numpy  # required by rasterio
pip install -e . --no-binary rasterio

Alternatively tsd can also be installed without downloading a tarball or a git clone:

pip install --upgrade --no-binary rasterio


Search and download is performed by,, and (one file per satellite constellation). They can be used both as command line scripts or as Python modules.

They use the Python modules,, and (one file per API provider).

From the command line

TSD can be used from the command line through the Python scripts get_*.py. For instance, to download and process Sentinel-2 images of the Jamnagar refinery, located at latitude 22.34806 and longitude 69.86889, run

python --lat 22.34806 --lon 69.86889 -b B02 B03 B04 -o test

This downloads crops of size 5000 x 5000 meters from the bands 2, 3 and 4, corresponding to the blue, green and red channels, and stores them in geotif files in the test directory.

It should print something like this on stdout (the number of images might vary):

Found 22 images
Elapsed time: 0:00:02.301129

Downloading 66 crops (22 images with 3 bands)... 66 / 66
Elapsed time: 0:00:57.620805

Reading 22 cloud masks... 22 / 22
6 cloudy images out of 22
Elapsed time: 0:00:15.066992

Images with more than half of the pixels covered by clouds (according to the cloud polygons available in Sentinel-2 images metadata, or Landsat-8 images quality bands) are moved in the test/cloudy subfolder.

To specify the desired bands, use the -b or --band flag. The crop size can be changed with the --width and --height flags. For instance

python --lat 22.34806 --lon 69.86889 -b B11 B12 --width 8000 --height 6000

downloads crops of size 8000 x 6000 meters, only for the SWIR channels (bands 11 and 12).

All the available options are listed with the -h or --help flag:

python -h

You can also run any of the search_*.py scripts from the command line separately. Run them with -h to get the list of available options. For a nice output formatting, pipe their output to jq (brew install jq).

python --lat 22.34806 --lon 69.86889 | jq

As Python modules

The Python modules can be imported to call their functions from Python. Refer to their docstrings to get usage information. Here are some examples.

# define an area of interest
import tsd
lat, lon = 42, 3
aoi = tsd.utils.geojson_geometry_object(lat, lon, 5000, 5000)

# search Landsat-8 images available on the AOI with Development Seed's API
x =, satellite='Landsat-8')

Project details

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Files for tsd, version 0.6.0
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