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https://github.com/yannforget/builtup-classification-osm
Replication code for "Supervised Classification of Built-up Areas in Sub-Saharan African Cities using Landsat Imagery and OpenStreetMap"
https://github.com/yannforget/builtup-classification-osm
landsat openstreetmap remote-sensing supervised-learning
Last synced: about 1 month ago
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Replication code for "Supervised Classification of Built-up Areas in Sub-Saharan African Cities using Landsat Imagery and OpenStreetMap"
- Host: GitHub
- URL: https://github.com/yannforget/builtup-classification-osm
- Owner: yannforget
- License: mit
- Created: 2018-05-13T10:36:02.000Z (over 6 years ago)
- Default Branch: master
- Last Pushed: 2019-06-14T15:36:08.000Z (over 5 years ago)
- Last Synced: 2024-01-29T21:14:20.848Z (12 months ago)
- Topics: landsat, openstreetmap, remote-sensing, supervised-learning
- Language: Jupyter Notebook
- Size: 915 KB
- Stars: 17
- Watchers: 4
- Forks: 7
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE.txt
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README
[](https://zenodo.org/badge/latestdoi/133227237)
# 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](http://maupp.ulb.ac.be/page/forget2018/) in an interactive map.
Input, intermediary and output data can be downloaded from [zenodo](https://zenodo.org/record/1291961).
# Dependencies
Dependencies are listed in the `environment.yml` file at the root of the repository. Using the [Anaconda](https://www.anaconda.com/download) distribution, a virtual environment containing all the required dependencies can be created automatically:
``` sh
# Clone the repository
git clone https://github.com/yannforget/builtup-classification-osm.git
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](https://zenodo.org/record/1291961). 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 https://zenodo.org/record/1291961/files/input.zip?download=1
wget -O intermediary.zip https://zenodo.org/record/1291961/files/intermediary.zip?download=1
# 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 http://cidportal.jrc.ec.europa.eu/ftp/jrc-opendata/GHSL/GHS_BUILT_LDSMT_GLOBE_R2015B/GHS_BUILT_LDSMT_GLOBE_R2015B_3857_38/V1-0/GHS_BUILT_LDSMT_GLOBE_R2015B_3857_38_v1_0.zip
unzip GHS_BUILT_LDSMT_GLOBE_R2015B_3857_38_v1_0.zip
rm GHS_BUILT_LDSMT_GLOBE_R2015B_3857_38_v1_0.zip
mv GHS_BUILT_LDSMT_GLOBE_R2015B_3857_38_v1_0 ghsl
```
# Code
The code of the analysis in divided in two parts: the Python scripts and modules used to support the analysis, and the notebooks where the outputs of the analysis have been produced.
## Notebooks
* `notebooks/01-Evolution_of_OSM.ipynb` : Analysis of OSM data availability and its evolution from 2011 to 2018. Please note that this analysis requires additionnal files and softwares: the full historic OSM data dump (available through the geofabrik.de website for OpenStreetMap members, and the `osmium` command-line tool).
* `notebooks/02-External_Datasets.ipynb` : Assessment of the GHSL and HBASE datasets in the context of our case studies.
* `notebooks/03-Buildings_Footprints.ipynb` : Assessment of OSM buildings footprints as built-up training samples.
* `notebooks/04-Nonbuilt_Tags.ipynb` : Assessment of OSM non-built polygons (leisure, natural or landuse objects) as non-built-up training samples.
* `notebooks/05-Urban_Blocks.ipynb` : Assessment of OSM-based urban blocks as built-up training samples.
* `notebooks/06-Urban_Distance.ipynb` : Assessment of OSM-based urban distance as non-built-up training samples.
* `notebooks/07-Comparative_Analysis.ipynb` : Supervised classification of built-up areas.
To run the notebooks, `input` and `intermediary` data must have been downloaded (see above). Additionnaly, the following pre-processing scripts must be executed:
``` sh
python src/preprocess_reference.py
python src/preprocess_osm.py
```
The outputs of the other pre-processing scripts are already included in the `intermediary` zenodo archive.
## Scripts
* `src/download_osm.py` : script used for the acquisition of OSM data.
* `src/generate_aoi.py` : script used to generate 40km x 40km area of interest for each case study.
* `src/preprocess_landsat.py` : script used to mask input landsat data according to our areas of interest.
* `src/preprocess_osm.py` : script used to produce the intermediary OSM products: rasterized buildings and non-built objects, urban blocks, urban distance, water mask...
* `src/preprocess_reference.py` : script used to rasterize the reference polygons used as validation data.
## Modules
* `src/metadata.py` : used to access metadata specific to each case study.
* `src/landsat.py` : processing of Landsat data.
* `src/raster.py` : raster processing functions.
* `src/classification.py` : supervised classification and performance assessment.