2imi9/OlmoEarth-v1-Chesapeake-Bay-Nutrient-Loads

OlmoEarth-v1-Chesapeake-Bay-Nutrient-Loads

Annual and monthly water-quality loads (Total Nitrogen, Total Phosphorus, Suspended Sediment) at 121 USGS Nontidal Network monitoring stations across the Chesapeake Bay watershed (2014–2023). Built for use with OlmoEarth-v1-Base embeddings + classical regression, with optional fine-tuning support via quantile-binned classes.

14,166 monthly records · 1,208 annual records · 121 stations · 118 watershed polygons · 6 sub-basins · 4 equal-frequency classes

v4 (April 2026) structural fixes:

• —Replaced bbox AOIs with USGS NLDI contributing-area polygons (one per station, 118/121 with valid upstream basin)
• —Re-binned categories using equal-frequency quartiles per metric (was watershed-relative quantiles → produced majority-class bias)
• —Added predefined spatial_fold (leave-one-sub-basin-out) and random_fold columns for honest cross-validation

Quick Start

Two formats are provided for two upload paths:

Option A — OlmoEarth Studio Web UI (studio/import_*.json)

Upload one metric at a time (TN, TP, or Sediment). Studio auto-detects sample_category, sample_number, sample_true_false.

import json
with open("studio/import_tn.json") as f:
    data = json.load(f)

Option B — Pipeline / rslearn (rslearn/*_features.geojson)

For direct use with olmoearth_run prepare_labeled_windows or any rslearn-compatible workflow.

import json
with open("rslearn/tn_features.geojson") as f:
    annotations = json.load(f)
with open("rslearn/annotation_task_features.geojson") as f:
    tasks = json.load(f)

Files

Schemas

rslearn classification (rslearn/*_features.geojson)

{
  "type": "Feature",
  "geometry": {"type": "Point", "coordinates": [lon, lat]},
  "properties": {
    "oe_labels": {"category": 0},
    "oe_start_time": "2014-01-01 00:00:00+00:00",
    "oe_end_time": "2014-01-31 00:00:00+00:00",
    "oe_annotations_task_id": "uuid",
    "STAID": "01491000",
    "metric": "tn_load",
    "year": 2014,
    "month": 1,
    "value": 88.1,
    "flow_normalized": true,
    "class_name": "reference_baseline"
  }
}

Studio import schema (studio/import_*.json)

{
  "type": "Feature",
  "geometry": {"type": "Point", "coordinates": [lon, lat]},
  "properties": {
    "task_name": "patuxent_choptank",
    "observation_time": "2014-01-01T00:00:00Z",
    "sample_category": "reference_baseline",
    "sample_number": 88.1,
    "sample_true_false": true
  }
}

Field mapping:

• —sample_category → 4-category label (reference_baseline, low_stress, moderate, high_stress)
• —sample_number → continuous load value (regression target)
• —sample_true_false → flow-normalized flag

These names are required for Studio's auto-detection of training fields.

Categories

Quantile-binned per metric. reference_baseline (bottom 10%) serves as the negative reference.

Counts shown are monthly across all three metrics combined.

AOI Sub-basins

Six sub-basin bbox AOIs covering the Chesapeake watershed.

Studio Configuration

Upload tip: Studio auto-locks sample_number range based on the first upload. Loosen to 0–100M before uploading a second metric, OR use one Studio project per metric.

Caveats

• —Class boundaries are watershed-relative quantiles, not absolute thresholds. A high_stress load in one basin may differ in absolute magnitude from another.
• —Bbox AOIs, not actual watershed polygons. Adequate for windowing imagery; replace with HUC-8 shapes for production-grade analysis.
• —Reference_baseline is value-based, not site-based — marks low-load observations, not pristine reference watersheds.
• —Monthly granularity is a scaled format; the case study specifies annual scale, so data_annual.parquet is the primary doc-compliant deliverable.
• —Flow-normalized vs raw loads — flag in *_flow_norm columns indicates which.

Sources

All data is public domain.

• —USGS Chesapeake Bay Loads & Trends — Annual Loads Table and Monthly Loads Table (2024 release, filtered to 2014–2023)
• —USGS NWIS — Station metadata via waterservices.usgs.gov
• —Chesapeake Bay Program Data Hub — datahub.chesapeakebay.net (NTN reference)

Related

• —allenai/OlmoEarth-v1-Base
• —olmoearth_projects
• —rslearn
• —OlmoEarth Studio docs
• —2imi9/OlmoEarth-v1-FT-Karst-Groundwater-Base — sister dataset (PA karst FT)
• —2imi9/OlmoEarth-v1-Potomac-Sewage-Spill-2026 — sister dataset (event reference)

Citation

@dataset{qi2026chesbay,
  author    = {Qi, Ziming and BAI Group},
  title     = {OlmoEarth-v1-Chesapeake-Bay-Nutrient-Loads: Analysis-Ready Loads Dataset for GeoAI Modeling},
  year      = {2026},
  publisher = {Hugging Face},
  url       = {https://huggingface.co/datasets/2imi9/OlmoEarth-v1-Chesapeake-Bay-Nutrient-Loads}
}

License

Apache 2.0. USGS source data are public domain.