Datasets:

allenai
/

olmoearth_lcc

license: apache-2.0
pretty_name: OlmoEarth Land Cover Change (LCC) Dataset
tags:
  - remote-sensing
  - earth-observation
  - change-detection
  - sentinel-2
size_categories:
  - 1K<n<10K

OlmoEarth Land Cover Change (LCC) Dataset

This dataset contains point-based annotations of land cover change, used to train the OlmoEarth LCC model (https://olmoearth-lcc.allen.ai). The model detects recent land cover change from Sentinel-2 time series: it inputs a Sentinel-2 image time series with 16 quarterly images (to establish a historical baseline) and 4 recent biweekly images (to detect changes soon after they occur) and predicts, per pixel, whether a land cover change occurred at some point during the time series, and if so, the source and destination land cover categories, the start and end dates of the change, and a fine-grained change category.

We also provide GeoTIFF outputs produced by running the trained model at scale. See Model output summary rasters below.

The dataset is intended to be paired with Sentinel-2 images, but can be used with other modalities as long as they are temporally aligned. We do not include the image inputs here since they can be obtained based on the timestamps in the provided annotations.

Annotation format

Annotations are provided as JSON files. Each file is a list of entries, where each entry corresponds to one 128x128-pixel spatial window at 10 m/pixel in a UTM projection:

{
  "projection": {"crs": "EPSG:32651", "x_resolution": 10, "y_resolution": -10},
  "bounds": [27130, -161561, 27258, -161433],
  "window_name": "example_window",
  "group": "default",
  "time_range": ["2017-01-01T00:00:00+00:00", "2024-01-01T00:00:00+00:00"],
  "positive_points": [
    {
      "lon": 121.5,
      "lat": 14.6,
      "pre_change": "2020-01-15",
      "first_date_change_noticeable": "2020-04-27",
      "post_change": "2020-07-15",
      "pre_category": "tree",
      "post_category": "urban/built-up",
      "post_change_category": "new_building"
    }
  ],
  "negative_points": [
    {"lon": 121.51, "lat": 14.61}
  ]
}

Entry fields:

projection / bounds: the UTM projection and pixel bounds [min_col, min_row, max_col, max_row] of the window. Pixel coordinates are projection coordinates divided by the resolution (10 m/pixel; y_resolution is negative so row indices increase southward).
window_name / group: identifiers for the entry.
time_range: the period over which the annotations are valid. In particular, negative points are only confirmed as no-change within this time range.
positive_points: points where a land cover change was verified.
negative_points: points verified as having no change. These only have lon and lat.

Positive point fields

Each positive point has a location (lon, lat in WGS84), three dates, and two land cover categories:

pre_change: the last date at which the location still appears in its pre-change state in the imagery.
first_date_change_noticeable: the first date at which the change starts to become noticeable.
post_change: the first date at which the change appears complete.
pre_category: the land cover category before the change.
post_category: the land cover category after the change.

The land cover categories (used for both pre_category and post_category) are:

bare, burnt, crops, fallow/shifting cultivation, grassland, Lichen and moss, shrub, snow and ice, tree, urban/built-up, water, wetland (herbaceous).

In addition to the source/destination land cover categories, more recently annotated positive points carry fine-grained change-category fields. These fields were introduced partway through annotation, so some positive points have them and others do not: a point either has at least one of the three fields set, or none of them. During training, the change-category heads are only supervised at points that have at least one of these fields; for such points, any unset sibling field is treated as an explicit "none" label, while points without any of the fields are masked out for these heads.

The three fields describe different aspects of a change, and a point is annotated with one pre_change_category and/or one post_change_category, or one same_change_category:

pre_change_category: what was removed or lost. Options: deforestation, urban_erosion, wetland_loss, water_contract, removed_crop_structure.
post_change_category: what appeared. Options: vegetation_growth, new_building, new_road, new_infrastructure, new_crop_field, new_aquafarm, site_clearing, water_expand, mining, new_crop_structure, selective_logging, landslide, settlement.
same_change_category: an event where the land cover category is disturbed but not permanently converted to a different category. Options: agricultural_activity, wildfire, ice_motion, flooding.

Data collection

The annotations were collected in several phases, all verified manually in an annotation UI showing monthly Sentinel-2 mosaics.

Phase 1: we trained a model to predict per-year land cover, then applied it on many randomly sampled locationsover a ten-year period. Candidate change locations were proposed by comparing the per-year land cover predictions: specifically, we looked for places where the model was confident that the land cover change was one type for three years, then we ignored the next year in case the change is gradual, and then the model was confident that the land cover change was another type for three more years.
Phase 2: output-based labeling (apply the model at scale, look for change predictions at a confidence threshold tuned for high-recall and low-precision, then label based on those).
Phase 3: output-based labeling.
Phase 4: similar to Phase 1 but use a per-pixel land cover model instead of one that has a larger spatial context. This way we ensure we can find very small-scale changes.
Phase 5: output-based labeling.
Phase 6: label random points in 0.1 x 0.1 degree tiles that are likely to have changes (e.g. agricultural activity, wildfire, flooding, new airport, etc.).
Phase 7: output-based labeling.
Phase 8: output-based labeling.
Phase 9: output-based labeling and focus on rare transitions (src_land_cover, dst_land_cover) that didn't appear frequently in the labels collected so far.
Phase 10: similar to Phase 4 but in subsaharan Africa, and oversampling the tree -> bare transitions.
Phase 11: output-based labeling focused on mining predictions.
Phase 12: output-based labeling focused on mining predictions.
Phase 13: output-based labeling focused on mining predictions.
Phase 14: output-based labeling focused on mining predictions.
Phase 15: output-based labeling focused on mining predictions.
Phase 16: label points based on a set of 10 datasets related to mining in different parts of sub-saharan Africa.
Phase 17: output-based labeling.
Phase 18: output-based labeling.

Model output summary rasters

Alongside the annotations, we provide summary GeoTIFFs produced by running the trained LCC model at scale. The world is divided into 32768x32768-pixel UTM tiles at 10 m/pixel (327.68 km per side), and each processed tile yields one file named {EPSG code}_{col}_{row}_summary.tif, where col and row are the tile's top-left corner in the 10 m/pixel UTM pixel grid (multiply by the resolution to get projection coordinates).

Each file is a 9-band uint8 GeoTIFF summarizing the model's per-pixel predictions for one reference timestamp. Pixels the model did not predict (e.g. no cloud-free imagery) are 0 in every band.

Band	Name	Description
1	`binary_change`	Probability of change, scaled to 0-255.
2	`pre_class`	Argmax class of the pre/same change-category head (see table below).
3	`post_class`	Argmax class of the post change-category head (see table below).
4	`src_class`	Argmax source land cover class (see table below).
5	`dst_class`	Argmax destination land cover class (see table below).
6	`pre_score`	Probability (0-255) of the argmax class in band 2.
7	`post_score`	Probability (0-255) of the argmax class in band 3.
8	`ts_pre_month`	Predicted change start (last pre-change date), month-encoded.
9	`ts_post_month`	Predicted change end (change complete date), month-encoded.

Month encoding (bands 8-9): 0 means no prediction; otherwise the value is 1 + the number of whole calendar months between January 2015 and the predicted date. So 1 = January 2015, 13 = January 2016, and so on (255 reaches March 2036).

Land cover classes (bands 4-5): 0 = no prediction, then 1 = bare, 2 = burnt, 3 = crops, 4 = fallow/shifting cultivation, 5 = grassland, 6 = Lichen and moss, 7 = shrub, 8 = snow and ice, 9 = tree, 10 = urban/built-up, 11 = water, 12 = wetland (herbaceous).

Pre/same change-category classes (band 2): in the deployed model, the pre_change_category and same_change_category heads are merged into a single head, with the "same" categories appended after the "pre" categories:

Value	Category
0	no prediction
1	none
2	deforestation
3	urban_erosion
4	wetland_loss
5	water_contract
6	removed_crop_structure
7	agricultural_activity
8	wildfire
9	ice_motion
10	flooding

Post change-category classes (band 3):

Value	Category
0	no prediction
1	none
2	vegetation_growth
3	new_building
4	new_road
5	new_infrastructure
6	new_crop_field
7	new_aquafarm
8	site_clearing
9	water_expand
10	mining
11	new_crop_structure
12	selective_logging
13	landslide
14	settlement

Note that "none" (value 1) is a valid prediction for both category heads: it indicates the model predicts a change at that pixel but none of the listed categories apply (or, for the merged pre/same head, that the change is fully described by the post category, and vice versa). The argmax in bands 2-5 is computed excluding the internal nodata class, so value 0 always means "no prediction" rather than a predicted class.

For most applications, threshold band 1 (binary_change) first (e.g. at 128, corresponding to probability 0.5) and interpret the remaining bands only at pixels that pass the threshold; bands 2-9 are populated at every predicted pixel regardless of whether change is predicted there.

For applications interested in specific types of changes that correspond to a change category, filter for the class and threshold the corresponding probability. For example, to create a mining mask, filter for band 3 post_class = 10 (10 is mining) and then threshold the band 7 probabilty.

Related resources

Training and inference code: WIP.
Encoder: OlmoEarth-v1.2-Base.
Imagery: Sentinel-2 L2A (Copernicus).