yannforget/builtup-classification-osm

Replication code for "Supervised Classification of Built-up Areas in Sub-Saharan African Cities using Landsat Imagery and OpenStreetMap" # Description This repository contains all the code required to reproduce the results presented in the following paper: * Y. Forget, C. Linard, M. Gilbert. *Supervised Classification of Built-up Areas in Sub-Saharan African Cities using Landsat Imagery and OpenStreetMap*, 2018. The results of the study can be explored here in an interactive map. Input, intermediary and output data can be downloaded from zenodo. # Dependencies Dependencies are listed in the `environment.yml` file at the root of the repository. Using the Anaconda distribution, a virtual environment containing all the required dependencies can be created automatically: ``` sh # Clone the repository git clone github.com cd builtup-classification-osm # Create the Python environment conda env create --file environment.yml # Activate the environment source activate landsat-osm # Or, depending on the system: conda activate landsat-osm ``` # Data Due to storage constraints, input data are not integrated to this repository. However, input and intermediary files required to run the analysis can be downloaded from a zenodo deposit. Alternatively, output files of the study can be directly downloaded from this repository. To run the following code, input and intermediary files must be downloaded in the `/data` folder. For example, in Linux: ``` sh # Create the data directory cd builtup-classification-osm mkdir data cd data # Download input and intermediary data wget -O input.zip zenodo.org wget -O intermediary.zip zenodo.org # Decompress the archives unzip input.zip unzip intermediary.zip rm *.zip ``` Likewise, the Global Humans Settlements Layer is required to run the notebook `02-External_Datasets.ipynb`: ``` sh cd builtup-classification-osm/data/input wget cidportal.jrc.ec.europa.eu …