# 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-*
---