Update README.md

README.md

@@ -1,3 +1,251 @@
 ---
 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](#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:
+
+```json
+{
+  "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](https://github.com/allenai/rslearn_projects/pull/294).
+- Encoder: [OlmoEarth-v1.2-Base](https://huggingface.co/allenai/OlmoEarth-v1_2-Base).
+- Imagery: Sentinel-2 L2A (Copernicus).