yannforget/landsat-sentinel-fusion

Complementarity Between Sentinel-1 and Landsat 8 Imagery for Built-Up Mapping in Sub-Saharan Africa This repository contains the Python code supporting the following paper: * Forget Y., Shimoni M., Gilbert M., and Linard C. *Complementarity Between Sentinel-1 and Landsat 8 Imagery for Built-Up Mapping in Sub-Saharan Africa*, In Press, 2018. Input and output datasets can be downloaded from Zenodo. # Dependencies Python dependencies are listed in the `environment.yml` and the `requirements.txt` files. A virtual environment containing all the required dependencies can be automatically created using `conda`: ``` bash # Clone the repository git clone github.com cd landsat-sentinel-fusion # Create the virtual environment conda env create --file environment.yml # Activate the environment source activate landsat-sentinel-fusion ``` The code also depends on: * Orfeo Toolbox for the computation of GLCM textures ; * SNAP for SAR data preprocessing. # Data Input and output datasets are available in a Zenodo deposit. ``` bash # Download and decompress the data wget -O data.zip zenodo.org unzip data.zip ``` Validation samples can be found in `data/raw/reference` (as shapefiles) or in `data/processed/reference` (as rasters). Classification outputs and performance metrics are located in `data/output` for each case study. Due to storage constraints, input satellite imagery is not included in the Zenodo deposit. However, the product identifiers are available in `data/raw/landsat/products.txt` and `data/raw/sentinel-1/products.txt`. This means that they can be automatically downloaded using auxiliary software such as landsatxplore or sentinelsat. For Landsat 8 scenes: ``` bash pip install landsatxplore # Earth Explorer credentials export LANDSATXPLORE_USERNAME= export LANDSATXPLORE_PASSWORD= cd data/raw/landsat # Download each product with landsatxplore for id in products.txt; do landsatxplore download $id; done # Decompress each product for product in *.zip; do unzi …