Datasets:
File size: 3,564 Bytes
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# Project Resilience Emissions from Land-Use Change Dataset
### Project Resilience
To contribute to this project see [Project Resilience](https://www.itu.int/en/ITU-T/extcoop/ai-data-commons/Pages/project-resilience.aspx) ([Github Repo](https://github.com/Project-Resilience/mvp))
Land Use Change data is provided by the [Land Use Harmonization Project](https://doi.org/10.5194/gmd-2019-360), providing land-use changes from 850-2100
Emissions from Land-Use Change (ELUC) data is provided by the [Global Carbon Budget 2023](https://doi.org/10.5194/essd-15-5301-2023) Bookkeeping of Land-Use Emissions (BLUE) model.
Data was used in [Discovering Effective Policies for Land-Use Planning](https://doi.org/10.48550/arXiv.2311.12304) at [NeurIPS 2023 Workshop: Tackling Climate Change with Machine Learning](https://www.climatechange.ai/events/neurips2023)
### Land Use Types
- Primary: Vegetation that is untouched by humans
- primf: Primary forest
- primn: Primary nonforest vegetation
- Secondary: Vegetation that has been touched by humans
- secdf: Secondary forest
- secdn: Secondary nonforest vegetation
- Urban
- Crop
- c3ann: Annual C3 crops (e.g. wheat)
- c4ann: Annual C4 crops (e.g. maize)
- c3per: Perennial C3 crops (e.g. banana)
- c4per: Perennial C4 crops (e.g. sugarcane)
- c3nfx: Nitrogen fixing C3 crops (e.g. soybean)
- Pasture
- pastr: Managed pasture land
- range: Natural grassland/savannah/desert/etc.
### Dataset
The dataset is indexed by latitude, longitude, and time, with each row consisting of the land use of a given year, the land-use change from year to year+1, and the ELUC at the end of year in tons of carbon per hectare (tC/ha)
In addition, the cell area of the cell in hectares and the name of the country the cell is located in are provided.
A crop and crop_diff column consisting of the sums of all the crop types and crop type diffs is provided as well as the BLUE model treats all crop types the same.
---
dataset_info:
features:
- name: ELUC_diff
dtype: float32
- name: c3ann
dtype: float32
- name: c3ann_diff
dtype: float32
- name: c3nfx
dtype: float32
- name: c3nfx_diff
dtype: float32
- name: c3per
dtype: float32
- name: c3per_diff
dtype: float32
- name: c4ann
dtype: float32
- name: c4ann_diff
dtype: float32
- name: c4per
dtype: float32
- name: c4per_diff
dtype: float32
- name: cell_area_diff
dtype: float32
- name: pastr
dtype: float32
- name: pastr_diff
dtype: float32
- name: primf
dtype: float32
- name: primf_diff
dtype: float32
- name: primn
dtype: float32
- name: primn_diff
dtype: float32
- name: range
dtype: float32
- name: range_diff
dtype: float32
- name: secdf
dtype: float32
- name: secdf_diff
dtype: float32
- name: secdn
dtype: float32
- name: secdn_diff
dtype: float32
- name: urban
dtype: float32
- name: urban_diff
dtype: float32
- name: ELUC
dtype: float32
- name: cell_area
dtype: float32
- name: country
dtype: float64
- name: crop
dtype: float32
- name: crop_diff
dtype: float32
- name: country_name
dtype: string
- name: time
dtype: int64
- name: lat
dtype: float64
- name: lon
dtype: float64
splits:
- name: train
num_bytes: 6837499488
num_examples: 41630020
download_size: 3195082319
dataset_size: 6837499488
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
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