Eumap
Comprises environmental, land cover, terrain, climatic, soil and vegetation layers covering the continental Europe at relatively fine spatial resolutions.
https://gitlab.com/geoharmonizer_inea/eumap
Category: Sustainable Development
Sub Category: Data Catalogs and Interfaces
Keywords
ensemble environmental layers europe gis machine learning
Keywords from Contributors
OpenLandMap climate land degradation soil terrain vegetation
Last synced: about 24 hours ago
JSON representation
Repository metadata
Eumap is a library to enable easier access to several spatial layers prepared for Continental Europe, as well the source code used to produce them (http://eumap.readthedocs.org).
- Host: gitlab.com
- URL: https://gitlab.com/geoharmonizer_inea/eumap
- Owner: geoharmonizer_inea
- License: mit
- Created: 2020-09-22T10:20:48.728Z (over 4 years ago)
- Default Branch: master
- Last Synced: 2024-12-27T01:32:38.665Z (4 months ago)
- Topics: ensemble, environmental layers, europe, gis, machine learning
- Stars: 6
- Forks: 4
- Open Issues: 7
- Releases: 0
-
Metadata Files:
- Readme: README.md
- Contributing: CONTRIBUTING.md
- License: LICENSE
README.md
eumap library
Community |
Documentation |
Resources |
Release Notes
Eumap is a library to enable easier access to several spatial layers prepared for Continental Europe (Landsat and Sentinel mosaics, DTM and climate datasets, land cover, potential natural vegetation and environmental quality maps), as well the classes and functions used to produce them.
It implements efficient raster access through rasterio, different gapfiling, approachs spatial and spacetime overlay, training samples preparation (LUCAS points), and Ensemble Machine Learning applied to spatial predictions (fully compatible with scikit-learn).
pyeumap Workflow
The workflow implemented by pyeumap 1) fills all the gaps for different remote sensing time-series, 2) does the space time overlay of point samples on several raster layers according to the reference date, 3) trains and evaluate a machine learning model, and 4) does the space time predictions for a specific target variable. These processing steps are demonstrated using a benchmark dataset for land-cover classification in different areas of the EU
This image presents the output of the gap filing approach for an area located in Croatia (tile 9529). This image refers to a Landsat temporal composites for the 2010 fall season, however all the 4 seasons since 2000 were analysed to fill the gaps.
This animation shows the land-cover classes for an area located in Sweden (tile 22497) according to the space time predictions. This example is a small use case that used 680 point samples, obtained in different years, to train a single model and to predict the land-cover in the region over the time.
Spatiotemporal Machine-Learning
In the Geo-harmonizer project, we prepare Analysis-Ready Earth Observation images from Landsat and
Sentinel missions, then use ground observations from the European Commission projects such as LUCAS surveys and CORINE and similar to
overlay the ground observations in the spacetime cubes. From this data we create spatiotemporal regression and classification matrices (see: sample data set). The eumap package (python and R versions) will allow accessing this data and testing models that apply Machine Learning for predictive mapping in spacetime.
License
© Contributors, 2020. Licensed under an Apache-2 license.
Contribute to eumap
eumap has been developed and used by a group of active community members. Your help is very valuable to make the package better for everyone. Refer to the Community Page.
Reference
- Publication is pending
- eumap is one of the deliverables of the GeoHarmonizer INEA project.
Funding
This work has received funding from the European Union's the Innovation and Networks Executive Agency (INEA) under Grant Agreement Connecting Europe Facility (CEF) Telecom project 2018-EU-IA-0095.
Committers metadata
Last synced: 9 months ago
Total Commits: 393
Total Committers: 12
Avg Commits per committer: 32.75
Development Distribution Score (DDS): 0.662
Commits in past year: 11
Committers in past year: 3
Avg Commits per committer in past year: 3.667
Development Distribution Score (DDS) in past year: 0.364
| Name | Commits | |
|---|---|---|
| Leandro Parente | l****e@g****m | 133 |
| Mohammadreza | m****a@o****g | 120 |
| luka | l****n@g****m | 54 |
| Martin Landa | l****n@g****m | 25 |
| Tomislav Hengl | t****l@o****g | 25 |
| Mo | m****g | 10 |
| Martijn Witjes | m****s@o****g | 9 |
| Mo | m****a@g****m | 9 |
| Carmelo | c****a@o****g | 4 |
| Yu-Feng Ho | y****o@o****g | 2 |
| Chris van Diemen | c****n@o****g | 1 |
| Ondrej Pesek | p****k@g****m | 1 |
Committer domains:
Issue and Pull Request metadata
Last synced: 9 months ago
Total issues: 20
Total pull requests: 20
Average time to close issues: 2 months
Average time to close pull requests: 3 minutes
Total issue authors: 6
Total pull request authors: 6
Average comments per issue: 0.95
Average comments per pull request: 0.1
Merged pull request: 0
Bot issues: 0
Bot pull requests: 0
Past year issues: 0
Past year pull requests: 0
Past year average time to close issues: N/A
Past year average time to close pull requests: N/A
Past year issue authors: 0
Past year pull request authors: 0
Past year average comments per issue: 0
Past year average comments per pull request: 0
Past year merged pull request: 0
Past year bot issues: 0
Past year bot pull requests: 0
Top Issue Authors
- landam (10)
- leal.parente (5)
- olegsson (2)
- chris.vandiemen (1)
- martijn.witjes (1)
- yu-feng.ho (1)
Top Pull Request Authors
- olegsson (11)
- yu-feng.ho (3)
- carmelo.bonannella (2)
- leal.parente (2)
- landam (1)
- martijn.witjes (1)
Top Issue Labels
- lucas (4)
- New functionality (3)
- Doing (2)
- bug (2)
- To Do (1)
- To be discussed (1)
Top Pull Request Labels
Dependencies
- ipython *
- jupyter *
- jupytext *
- matplotlib *
- nbsphinx *
- numpy *
- numpydoc *
- pydata-sphinx-theme *
- sphinx ===4.1.2
- sphinx-autodoc-typehints *
- sphinx-copybutton *
- sphinx-rtd-theme *
- GDAL >=3.1
- OWSLib ==0.22
- affine >=2.3
- geopandas >=0.8
- joblib >=1.1.0
- numpy >=1.19<1.21
- pandas >=1.1
- psutil >=5.8
- pyproj >=3.1
- rasterio >=1.1
- requests >=2.24
- scikit_learn >=0.24
Score: 5.049856007249537