https://github.com/nandahkrishna/geolifeclef2019
Submission to the GeoLifeCLEF 2019 Species Recommendation Challenge
https://github.com/nandahkrishna/geolifeclef2019
challenge clef environmental-data jupyter-notebooks machine-learning niche-modelling python raster recommendation-system recommender-system spatial-models species-distribution-modeling species-distribution-modelling
Last synced: about 2 months ago
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Submission to the GeoLifeCLEF 2019 Species Recommendation Challenge
- Host: GitHub
- URL: https://github.com/nandahkrishna/geolifeclef2019
- Owner: nandahkrishna
- Created: 2019-05-22T17:12:55.000Z (almost 6 years ago)
- Default Branch: master
- Last Pushed: 2020-01-29T14:10:07.000Z (over 5 years ago)
- Last Synced: 2025-01-30T12:16:30.249Z (4 months ago)
- Topics: challenge, clef, environmental-data, jupyter-notebooks, machine-learning, niche-modelling, python, raster, recommendation-system, recommender-system, spatial-models, species-distribution-modeling, species-distribution-modelling
- Language: Jupyter Notebook
- Homepage:
- Size: 3.35 MB
- Stars: 1
- Watchers: 2
- Forks: 1
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# GeoLifeCLEF2019
This repository contains Jupyter Notebooks with our team's code for the GeoLifeCLEF 2019 Species Recommendation Challenge.
The original code for the image patch extractor can be found at [this repository](https://github.com/maximiliense/GLC19) and the datasets are available at the challenge's [CrowdAI page](https://www.crowdai.org/challenges/lifeclef-2019-geo).
Requirements can be installed by entering the following command in the Terminal:
``` bash
pip install -r requirements.txt
```
We achieved a maximum Top30 score of 0.1342 and placed 3rd overall, with our top submission ranking 6th on the leaderboard. This was an XGBoost trained on Spatial Coordinates and Environmental Variable values.
## Publication
You can find our paper [here](http://ceur-ws.org/Vol-2380/paper_71.pdf) (CEUR-WS Vol. 2380) in the CLEF 2019 Working Notes.