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README.md

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+# Burnt & Flooded Crop Benchmark Data (BFC-Bench): 
+
+### Global, Phenology-timed, Agriculutral Hazard Benchmark Data for Geosppatial Foundation Models
+
+The following pipelines build the satellite image chips of Burnt and Flooded cropland demonstrated in https://doi.org/10.xxxx/axxxxxxxx (in preperation)
+The pipelines build one folder per hazard, and include phenological stage along side quality control properties:
+
+![Events with damaged cropland: 1,297 flood events and 1,463 burn events](figures/global_map.png)
+
+| folder | hazard | label source |
+|:-------|:------|:------|
+| [`Flooded/`](Flooded/README.md) | floods | Cross-referenced Global Flood Monitoring (GFM) flood extent on cropland |
+| [`Burnt/`](Burnt/README.md) | agricultural fires | Cross-referenced Sentinel-2 dNBR inside the MODIS burned-area on cropland |
+
+Each chip is 512 × 512 px at 10 m and comes with its label, Sentinel-2 and Sentinel-1 images from
+before and after the event, the crop growth stage at the event date, and a quality-control verdict.
+
+
+## Data access and citation
+
+- **Data:** the released chips and their manifests are on Hugging Face:
+  [eadrah/AgDamage_Benchmark_v1](https://huggingface.co/datasets/eadrah/AgDamage_Benchmark_v1)
+- **Publication:** *title, authors, venue, year* (to be added):
+  [https://doi.org/10.xxxx/axxxxxxxx](https://doi.org/10.xxxx/axxxxxxxx) (in preperation)
+- **Authors:** *Esmaeel Adrah<sup>1</sup>, Julina Maharjan<sup>1</sup>, Jamon Van Den Hoek<sup>2</sup>, He Yin<sup>1,3,4</sup>*
+<br><sup>1</sup> Department of Geography, Kent State University
+<br><sup>2</sup> Department of Geography and Environmental Sciences, Oregon State University
+<br><sup>3</sup> School of Environment, Society & Sustainability, University of Utah
+<br><sup>4</sup> Scientific Computing and Imaging Institute, University of Utah
+- **Contact:** *Esmaeel Adrah (eadrah@kent.edu | esmaeelad@gmail.com)*, *He Yin (he.yin@utah.edu)*
+
+
+If you use this dataset or code, please cite:
+
+```bibtex
+@article{bfcbench,
+  title   = {< Burnt & Flooded Crop Benchmark Data (BFC-Bench): Global, Phenology-timed, Agriculutral Hazard Benchmark Data for Geosppatial Foundation Models>},
+  author  = {<Esmaeel Adrah, Julina Maharjan, Jamon Van Den Hoek, He Yin>>},
+  journal = {<TBD>},
+  year    = {<2026>},
+  doi     = {10.xxxx/axxxxxxxx}
+}
+```
+
+## Overview
+[Summary](#Summary) · [Examples](#examples) · [Statistics](#statistics) ·
+[Chip format](#chip-format) · [Setup](#setup) · [Running](#running)
+
+
+
+## Summary
+
+| | $\color{#185FA5}{\blacksquare}$ Flooded crop | $\color{#d7191c}{\blacksquare}$ Burnt crop |
+|---|:--|:--|
+| events in the event list | 1,952 | 1,619 |
+| events with released chips | 1,297 | 1,463 |
+| released chips | **5,102** | **4,786** |
+| chips per event (mean) | 3.9 | 3.3 |
+| event years | 2020–2026 | 2020–2025 |
+| regions | 10 | 11 |
+| chips with both Sentinel-1 images | 4,999 (98%) | 4,647 (97%) |
+| chips with all four images usable | 3,804 (75%) | 4,593 (96%) |
+| chips with a crop growth stage | 4,486 (88%) | 3,571 (75%) |
+
+The released chips are on Hugging Face:
+[eadrah/AgDamage_Benchmark_v1](https://huggingface.co/datasets/eadrah/AgDamage_Benchmark_v1).
+Each hazard folder here holds its two tables:
+
+- `manifest.csv`: one row per released chip, with its event, dates, damaged-cropland fractions,
+  bounds, shard, crop growth stage and QC verdict
+- `events_master.csv`: every event in the event list, including events that yielded no chips, with
+  its area, dates, how it was confirmed and its chip count (the Flooded table also has the `batch`
+  each event was built in)
+
+`Flooded/excluded_chips.csv` lists the 30 chips left out of the release because both Sentinel-2
+images were missing.
+
+ 
+
+## Examples
+
+### $\color{#185FA5}{\blacksquare}$ Flooded crop
+
+![Flood events in six countries: Sentinel-2 and Sentinel-1 before and after, label and flood persistence](figures/example_flooded.png)
+
+Six chips from Niger, Pakistan, China, Brazil, Thailand and Greece, one per event. Columns: 
+Sentinel-2 before and after, Sentinel-1 VV before and after, the label ($\color{#185FA5}{\blacksquare}$ flooded crop, $\color{#639922}{\blacksquare}$ dry cropland
+, $\color{#BA7517}{\blacksquare}$ excluded crop, $\color{#D3D1C7}{\blacksquare}$ non-crop) and flood persistence, the share of valid Sentinel-1 overpasses in which each pixel was flooded. fc is the chip's flooded-cropland share.
+
+### $\color{#d7191c}{\blacksquare}$ Burnt crop
+
+![Burn events in six countries: Sentinel-2 and Sentinel-1 before and after, label and dNBR severity](figures/example_burnt.png)
+
+Six chips from Ethiopia, India, China, Brazil, Vietnam and Turkey. Columns:
+Sentinel-2 before and after, Sentinel-1 before and after, the label ($\color{#d7191c}{\blacksquare}$ burned cropland, $\color{#1a9641}{\blacksquare}$ unburned
+cropland, $\color{#fdae61}{\blacksquare}$ excluded crop, $\color{#e8e8e8}{\blacksquare}$ non-cropland) and dNBR burn severity, from 0 (unburned) to 4
+(high).
+
+
+
+## Chip format
+
+Each chip is a 512 × 512 px tile at 10 m in the event's UTM zone: five GeoTIFFs and a JSON sidecar.
+
+| file | bands | type | content |
+|:---|:--|:--|:---|
+| `label` | 5 | uint8 | class and per-pixel damage layers (below) |
+| `s2_pre`, `s2_post` | 12 | float32 | Sentinel-2 before / after: B2 B3 B4 B5 B6 B7 B8 B8A B9 B11 B12 (surface reflectance × 10,000) and NDVI |
+| `s1_pre`, `s1_post` | 2 | float32 | Sentinel-1 before / after: VV and VH backscatter (dB) |
+| `json` | | | sidecar metadata |
+
+Image pixels with no data are 0. The pipelines write the files as `<chip>_label.tif`,
+`<chip>_s2_pre.tif`, … and `<chip>.json`; in the released shards they are `<chip>.label.tif`,
+`<chip>.s2_pre.tif`, … and `<chip>.json`. Chip ids are `<event>_r<row>_c<col>`.
+
+### Label
+
+Band 1 holds the class:
+
+| value (band 1) | $\color{#185FA5}{\blacksquare}$ Flooded crop label | $\color{#d7191c}{\blacksquare}$ Burnt crop label |
+|:--|:---|:---|
+| 1 | $\color{#185FA5}{\blacksquare}$ flooded cropland | $\color{#d7191c}{\blacksquare}$ burned cropland |
+| 2 | $\color{#639922}{\blacksquare}$ dry cropland | $\color{#1a9641}{\blacksquare}$ unburned cropland |
+| 3 | $\color{#BA7517}{\blacksquare}$ excluded cropland: inside the GFM exclusion mask | $\color{#fdae61}{\blacksquare}$ excluded cropland: no clear Sentinel-2 view before and after |
+| 4 | $\color{#D3D1C7}{\blacksquare}$ non-cropland | $\color{#e8e8e8}{\blacksquare}$ non-cropland |
+| 255 | cropland never observed in label data | cropland never observed in label data |
+
+and bands 2–5 describe each pixel:
+
+| band | $\color{#185FA5}{\blacksquare}$ Flooded crop label | $\color{#d7191c}{\blacksquare}$ Burnt crop label |
+|:--|:---|:---|
+| 2 | GFM flood likelihood, 0–100; 255 = not observed | dNBR, stored as (dNBR + 500) / 8; 255 = not observed |
+| 3 | GFM exclusion mask (1 = excluded) | excluded-cropland mask (0/1) |
+| 4 | cropland (0/1) | cropland (0/1) |
+| 5 | flood persistence, 0–100: % of valid Sentinel-1 overpasses flooded; 255 = not observed | burn severity, 0 unburned · 1 low · 2 moderate-low · 3 moderate-high · 4 high; 255 = not observed |
+
+A Flooded pixel is flooded cropland when GFM mapped it as flooded in any Sentinel-1 overpass in a geo-cross referenced event. A
+Burnt pixel is burned cropland when it was seen clearly before and after, its dNBR is at least 100 in a geo-cross referenced event where it lies inside the MODIS MCD64A1 burned-area perimeter.
+
+### Before and after imagery
+
+| | $\color{#185FA5}{\blacksquare}$ Flooded crop | $\color{#d7191c}{\blacksquare}$ Burnt crop |
+|:--|:---|:---|
+| Sentinel-2 before | the scene clearest over the chip, 30 days before the event | clearest-first mosaic, 110 to 8 days before the first burn day |
+| Sentinel-2 after | the scene clearest over the chip, 30 days after the event | clearest-first mosaic, 10 to 75 days after the last burn day |
+| Sentinel-1 before | the nearest scene, 30 days before the event | nearest-first , up to 60 days before the first burn day − 5 days |
+| Sentinel-1 after | the nearest scene on the same pass and orbit, 30 days after | nearest-first , up to 75 days after the last burn day + 8 days |
+
+
+## Statistics
+
+
+### Temporal distriubtion of the chips
+##### Distribution across crop growth stages and by month at the event date
+
+![Chips by crop growth stage at the event date, and by month of the event](figures/phenology.png)
+
+The stage comes from VIIRS VNP22Q2 land-surface phenology: the event date's position in the local
+growth cycle, by majority over the chip. *No data* means the event is after 2024, beyond VNP22Q2
+coverage, or no valid growth cycle covers the chip.
+
+
+### Spatial distribution of the chips
+##### Distribution across regions and latitude
+
+![Chips by region and by latitude](figures/spatial.png)
+
+
+### Chip quality
+
+![Chip quality-control verdicts and usable imagery](figures/qc.png)
+
+Every image of every chip is scanned for missing, empty or corrupt data, cloud and
+swath-edge gaps, and the chip gets its worst defect as the verdict. An image counts as usable
+unless it is corrupt, cloud-blank or cut by a severe swath edge. The
+status definitions are in [`Flooded/README.md`](Flooded/README.md#04--quality-control) and
+[`Burnt/README.md`](Burnt/README.md#04--quality-control).
+ 
+
+### Sidecar Metadata (`<chip>.json`)
+
+| field | content |
+|:---|:---|
+| `chip_id`, `event_id`, `rank` | the chip, its event, and its rank within the event, best first (Flooded counts from 0, Burnt from 1) |
+| `source`, `tier`, `provenance` | source of the event and confirmation source: `confirmed_by`, `n_independent`, and the GFM flood area (Flooded) or the MODIS / VIIRS burned fractions and active-fire count (Burnt) |
+| `continent`, `country`, `place` | location labels |
+| `event_date_start`, `event_date_end` | the event dates: reported flood dates (Flooded), first and last burn day (Burnt) |
+| `crs`, `res_m`, `size` | UTM projection, 10 m, 512 px |
+| `min_lon`, `min_lat`, `max_lon`, `max_lat` | chip bounds (WGS 84) |
+| `cropland_frac`, `excluded_crop_frac` | cropland share and excluded-cropland share |
+| `flooded_crop_frac`, `flooded_crop_px`, `flood_persistence_mean` | Flooded: flooded-cropland share and pixel count; mean persistence over flooded cropland (%) |
+| `burned_crop_frac`, `burned_crop_px`, `burn_severity_mean` | Burnt: burned-cropland share and pixel count; mean severity over burned cropland (0–4) |
+| `label_path`, `imagery` | the label file; per image its `date`, `clear_frac` (Sentinel-2), `pass` and `rel_orbit` (Sentinel-1) and `path`; Burnt also records the Sentinel-2 search windows |
+| `phenology` | crop growth stage at the event date (`stage_at_flood` / `stage_at_burn`), its agreement over the chip, the stage shares, the growth cycle used and its transition dates; `null` with a `phenology_note` when unavailable |
+| `qc` | `verdict`, `all_rasters_ok`, `all_rasters_usable`, `s2_usable`, `s1_usable`, `n_usable_rasters`, `defects`, and the status of each image under `rasters` |
+| `has_s1` | both Sentinel-1 images are present |
+
+Example snapshots below, file paths are shortened to the file name.
+
+<details>
+<summary>🟦 Flooded crop sidecar: <code>DFO5292_r53248_c52736.json</code> (Niger, first chip of the example above)</summary>
+
+```json
+{
+  "chip_id": "DFO5292_r53248_c52736",
+  "event_id": "DFO5292",
+  "rank": 0,
+  "source": "dfo",
+  "tier": "C",
+  "continent": "Africa",
+  "country": "Niger",
+  "place": "Niger",
+  "provenance": {
+    "origin": "dfo",
+    "confirmed_by": [
+      "GFM"
+    ],
+    "n_independent": 1,
+    "tier": "C",
+    "gfm_flood_km2": 143.155
+  },
+  "event_date_start": "2022-09-17",
+  "event_date_end": "2022-09-21",
+  "crs": "EPSG:32632",
+  "res_m": 10,
+  "size": 512,
+  "label_path": "…/DFO5292_r53248_c52736_label.tif",
+  "flood_persistence_mean": 48.3,
+  "cropland_frac": 0.7851,
+  "flooded_crop_frac": 0.356,
+  "flooded_crop_px": 93323,
+  "excluded_crop_frac": 0.0745,
+  "min_lon": 10.968826,
+  "min_lat": 12.78406,
+  "max_lon": 11.016339,
+  "max_lat": 12.830689,
+  "imagery": {
+    "s2_pre": {
+      "date": "2022-09-11",
+      "clear_frac": 0.8976,
+      "path": "…/DFO5292_r53248_c52736_s2_pre.tif"
+    },
+    "s2_post": {
+      "date": "2022-09-26",
+      "clear_frac": 1.0,
+      "path": "…/DFO5292_r53248_c52736_s2_post.tif"
+    },
+    "s1_pre": {
+      "date": "2022-09-09",
+      "path": "…/DFO5292_r53248_c52736_s1_pre.tif",
+      "pass": "ASCENDING",
+      "rel_orbit": 59
+    },
+    "s1_post": {
+      "date": "2022-09-21",
+      "path": "…/DFO5292_r53248_c52736_s1_post.tif",
+      "pass": "ASCENDING",
+      "rel_orbit": 59
+    }
+  },
+  "phenology": {
+    "stage_at_flood": "peak/maturity",
+    "stage_agreement": 0.61,
+    "stage_fractions": {
+      "dormant": 0.058,
+      "green-up": 0.138,
+      "peak/maturity": 0.61,
+      "senescence": 0.193
+    },
+    "flood_date": "2022-09-17",
+    "growth_cycle": "event-year cycle 1",
+    "transitions": {
+      "green_up_onset": "2022-07-12",
+      "maximum": "2022-08-25",
+      "senescence_onset": "2022-09-30",
+      "dormancy_onset": "2022-12-03"
+    },
+    "n_valid_pixels": 103
+  },
+  "qc": {
+    "all_rasters_ok": true,
+    "all_rasters_usable": true,
+    "s2_usable": true,
+    "s1_usable": true,
+    "n_usable_rasters": 4,
+    "verdict": "clean",
+    "defects": [],
+    "rasters": {
+      "s2_pre": {
+        "status": "ok",
+        "nodata_frac": 0.0,
+        "white_frac": 0.0477
+      },
+      "s2_post": {
+        "status": "ok",
+        "nodata_frac": 0.0,
+        "white_frac": 0.0
+      },
+      "s1_pre": {
+        "status": "ok",
+        "nodata_frac": 0.0
+      },
+      "s1_post": {
+        "status": "ok",
+        "nodata_frac": 0.0
+      }
+    }
+  },
+  "has_s1": true
+}
+```
+
+</details>
+
+<details>
+<summary>🟥 Burnt crop sidecar: <code>MC202001224756_r0187_c0148.json</code> (India, October 2020)</summary>
+
+```json
+{
+  "chip_id": "MC202001224756_r0187_c0148",
+  "event_id": "MC202001224756",
+  "rank": 3,
+  "source": "mcd64-cluster",
+  "tier": "A",
+  "continent": "SouthAsia",
+  "country": "India",
+  "place": "India",
+  "provenance": {
+    "origin": "MCD64A1 burned-area clustering (GlobFire method)",
+    "confirmed_by": [
+      "MODIS MCD64A1",
+      "VIIRS VNP64A1",
+      "VIIRS VNP14A1 active fire"
+    ],
+    "n_independent": 2,
+    "tier": "A",
+    "burn_km2": 235.28,
+    "modis_frac": 0.956,
+    "viirs_frac": 0.905,
+    "active_fire_px": 145
+  },
+  "event_date_start": "2020-10-14",
+  "event_date_end": "2020-10-31",
+  "crs": "EPSG:32643",
+  "res_m": 10,
+  "size": 512,
+  "label_path": "…/MC202001224756_r0187_c0148_label.tif",
+  "burn_severity_mean": 2.34,
+  "cropland_frac": 0.9346,
+  "burned_crop_frac": 0.8585,
+  "burned_crop_px": 225045,
+  "excluded_crop_frac": 0.0,
+  "min_lon": 75.352022,
+  "min_lat": 30.760139,
+  "max_lon": 75.405713,
+  "max_lat": 30.806494,
+  "imagery": {
+    "s2_pre": {
+      "date": "2020-09-29",
+      "clear_frac": 1,
+      "path": "…/MC202001224756_r0187_c0148_s2_pre.tif"
+    },
+    "s2_post": {
+      "date": "2020-12-08",
+      "clear_frac": 1,
+      "path": "…/MC202001224756_r0187_c0148_s2_post.tif"
+    },
+    "s1_pre": {
+      "date": "2020-10-04",
+      "path": "…/MC202001224756_r0187_c0148_s1_pre.tif",
+      "pass": "ASCENDING",
+      "rel_orbit": 100
+    },
+    "s1_post": {
+      "date": "2020-11-09",
+      "path": "…/MC202001224756_r0187_c0148_s1_post.tif",
+      "pass": "ASCENDING",
+      "rel_orbit": 100
+    },
+    "s2_pre_window": [
+      "2020-06-26",
+      "2020-10-06"
+    ],
+    "s2_post_window": [
+      "2020-11-10",
+      "2021-01-14"
+    ]
+  },
+  "qc": {
+    "all_rasters_ok": true,
+    "all_rasters_usable": true,
+    "s2_usable": true,
+    "s1_usable": true,
+    "n_usable_rasters": 4,
+    "verdict": "clean",
+    "defects": [],
+    "rasters": {
+      "s2_pre": {
+        "status": "ok",
+        "nodata_frac": 0.0,
+        "white_frac": 0.0005
+      },
+      "s2_post": {
+        "status": "ok",
+        "nodata_frac": 0.0,
+        "white_frac": 0.0002
+      },
+      "s1_pre": {
+        "status": "ok",
+        "nodata_frac": 0.0
+      },
+      "s1_post": {
+        "status": "ok",
+        "nodata_frac": 0.0
+      }
+    }
+  },
+  "phenology": {
+    "stage_at_burn": "senescence",
+    "stage_agreement": 0.629,
+    "stage_fractions": {
+      "dormant": 0.371,
+      "senescence": 0.629
+    },
+    "burn_date": "2020-10-14",
+    "growth_cycle": "event-year cycle 2",
+    "transitions": {
+      "green_up_onset": "2020-07-21",
+      "maximum": "2020-08-18",
+      "senescence_onset": "2020-10-06",
+      "dormancy_onset": "2020-11-14"
+    },
+    "n_valid_pixels": 5
+  },
+  "has_s1": true
+}
+```
+
+</details>
+
+## Setup
+
+```bash
+conda env create -f environment.yml
+conda activate agdamage
+earthengine authenticate
+export EE_PROJECT=<your-project-id>      # a Cloud project registered for Earth Engine
+```
+
+## Running
+
+The scripts read and write in the folder they are run from, so run every step of a pipeline from
+the same working folder, for example:
+
+```bash
+mkdir -p ~/work/burnt && cd ~/work/burnt
+python /path/to/AgDamage/Burnt/01_build_events.py --n 100
+python /path/to/AgDamage/Burnt/02_build_chips.py
+```
+
+The steps, options and outputs of each pipeline are in its folder's README.
+
+## Acknowledgments
+
+
+**Affiliated institutions:** 
+
+
+<p align="center">
+  <img src="figures/logos/institution_1.png" alt="Institution 1" height="70">
+  &nbsp;&nbsp;&nbsp;&nbsp;
+  <img src="figures/logos/institution_2.png" alt="Institution 2" height="70">
+  &nbsp;&nbsp;&nbsp;&nbsp;
+  <img src="figures/logos/institution_3.png" alt="Institution 3" height="70">
+</p>
+
+<p align="center">
+  <img src="figures/logos/institution_4.png" alt="Institution 4" height="80">
+</p>
+
+**Funding:** *This research was funded by NASA Disaster Program Grant number#* (to be added).
+
+<p align="center">
+  &nbsp;&nbsp;&nbsp;&nbsp;
+  <img src="figures/logos/funder.png" alt="Funder" height="150">
+</p>