mike/IUU 22220

sentinel2_feedback/.gitignore

@@ -0,0 +1,4 @@
+rslearn_data
+chips
+cache
+.DS_Store

sentinel2_feedback/README.md

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+# Sentinel2 Feedback
+
+This project trains a model to determine if a given Sentinel-2 chip is valid or not.
+
+## Setup 
+
+#### Dependencies
+
+```bash
+python3 -m venv venv && source venv/bin/activate
+pip install -r rslearn_projects/requirements.txt
+```
+
+#### Auth
+
+The `retrieve_dataset.py` script expects `--token`, which can be [accessed like this](https://api-int.skylight.earth/docs/#introduction-item-0).
+
+## Source data 
+
+This model is trained on a dataset of Sentinel-2 chips that have been labeled as GOOD or BAD.
+The feedback.csv file is an export from the Skylight In-App Feedback tool, filtered for
+Sentinel-2 events. Each row in the feedback file has an event_id, a label, and a link to the
+event in the Skylight app.
+
+A good way to generate a set of events to be labeled is to use the EAI [sample-events script](https://github.com/VulcanSkylight/eai/blob/master/ais/data/sample_events/sample-events.py#L1-L1) ([readme](https://github.com/VulcanSkylight/eai/blob/master/ais/data/sample_events/README.md#L1-L1)).
+
+## Dataset Pre-processing
+
+The `retrieve_dataset.py` script fetches event metadata from the Skylight API to identify the chip URL, and downloads the chip locally.
+It outputs a csv file with the event_id, label, and local path to the chip, which is input into `create_rslearn_data.py`.
+
+```
+rslearn_projects/sentinel2_feedback $> 
+python retrieve_dataset.py --token $token --feedback_csv feedback.sample.csv --chips_dir chips --output_csv dataset.sample.csv
+```
+
+The `create_rslearn_data.py` script creates an rslearn dataset from the chips and labels.
+
+```
+python create_rslearn_data.py --dataset_csv dataset.2rows-sample.csv  --out_dir rslearn_data
+```

sentinel2_feedback/config.json

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+{
+  "layers": {
+    "sentinel2": {
+      "type": "raster",
+      "band_sets": [
+        {
+          "dtype": "uint8",
+          "bands": [
+            "R",
+            "G",
+            "B"
+          ],
+          "format": {
+            "name": "geotiff"
+          }
+        }
+      ],
+      "data_source": {
+        "name": "rslearn.data_sources.gcp_public_data.Sentinel2",
+        "index_cache_dir": "/home/mikej/rslearn_projects/sentinel2_feedback/cache/",
+        "use_rtree_index": false,
+        "max_time_delta": "1d",
+        "sort_by": "cloud_cover",
+        "query_config": {
+          "space_mode": "CONTAINS"
+        }
+      }
+    },
+    "label": {
+      "type": "vector"
+    }
+  },
+  "tile_store": {
+    "name": "file",
+    "root_dir": "tiles"
+  }
+}

sentinel2_feedback/create_rslearn_data.py

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+import argparse
+import csv
+import json
+import math
+import os
+import shutil
+from datetime import datetime, timedelta
+from pathlib import Path
+
+import shapely
+from pydantic import BaseModel
+from pyproj import Transformer
+from rslearn.const import WGS84_PROJECTION
+from rslearn.dataset.window import Window
+from rslearn.utils import get_utm_ups_crs
+from rslearn.utils.geometry import Projection, STGeometry
+from upath import UPath
+
+point_geojson = {
+    "type": "FeatureCollection",
+    "features": [
+        {
+            "type": "Feature",
+            "geometry": {
+                "type": "Point",
+                "coordinates": [[32, 32]],
+            },
+            "properties": {
+                "label": None,
+            },
+        }
+    ],
+    "properties": None,
+}
+
+
+class ArgsModel(BaseModel):
+    dataset_csv: str
+    out_dir: str
+
+
+class Record(BaseModel):
+    event_id: str
+    label: str
+    lat: float
+    lon: float
+    time: str
+
+
+def latlon_to_utm_zone(lat, lon):
+    """Determine the UTM zone for a given latitude and longitude."""
+    zone_number = math.floor((lon + 180) / 6) + 1
+    if lat >= 0:
+        epsg_code = 32600 + zone_number  # Northern Hemisphere
+    else:
+        epsg_code = 32700 + zone_number  # Southern Hemisphere
+    return epsg_code
+
+
+def calculate_bounds(
+    record: Record, projection: Projection
+) -> tuple[int, int, int, int]:
+    window_size = 128
+    point = shapely.Point(record.lon, record.lat)
+    stgeometry = STGeometry(WGS84_PROJECTION, point, None)
+    geometry = stgeometry.to_projection(projection)
+
+    bounds = [
+        int(geometry.shp.x) - window_size // 2,
+        int(geometry.shp.y) - window_size // 2,
+        int(geometry.shp.x) + window_size // 2,
+        int(geometry.shp.y) + window_size // 2,
+    ]
+    return bounds
+
+
+def get_label_data(record: Record, window: Window):
+    return {
+        "type": "FeatureCollection",
+        "features": [
+            {
+                "type": "Feature",
+                "geometry": {
+                    "type": "Point",
+                    "coordinates": [[32, 32]],
+                },
+                "properties": {
+                    "label": record.label,
+                },
+            }
+        ],
+        "properties": window.projection.serialize(),
+    }
+
+
+def create_rslearn_data(args: ArgsModel):
+    with open(args.dataset_csv, mode="r") as file:
+        reader = csv.DictReader(file)
+        for row in reader:
+            record = Record(**row)
+            pixel_size = 10  # 10 meters per pixel for Sentinel-2
+            crs = get_utm_ups_crs(record.lat, record.lon)
+            projection = Projection(
+                crs=crs, x_resolution=pixel_size, y_resolution=-pixel_size
+            )
+
+            bounds = calculate_bounds(record, projection)
+            timestamp = datetime.fromisoformat(record.time)
+            window_root = UPath(f"{args.out_dir}/windows/sentinel2/{record.event_id}")
+            os.makedirs(window_root, exist_ok=True)
+
+            # Create the Window object
+            window = Window(
+                path=window_root,
+                group=record.label,
+                name=record.event_id,
+                projection=projection,
+                bounds=bounds,
+                time_range=(
+                    timestamp - timedelta(minutes=20),
+                    timestamp + timedelta(minutes=20),
+                ),
+            )
+            window.save()
+
+            # Populate the label layer
+            label_layer_dir = os.path.join(window_root, "layers", "label")
+            os.makedirs(label_layer_dir, exist_ok=True)
+            with open(os.path.join(label_layer_dir, "data.geojson"), "w") as f:
+                json.dump(get_label_data(record, window), f)
+            Path(f"{label_layer_dir}/completed").touch()
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser(
+        description="Creates rslearn data from a CSV of events."
+    )
+    parser.add_argument(
+        "--dataset_csv",
+        type=str,
+        required=True,
+        help="Dataset CSV file which was the --output_csv from retrieve_dataset.py.",
+    )
+    parser.add_argument(
+        "--out_dir",
+        type=str,
+        required=True,
+        help="Location of the rslearn dataset.",
+    )
+    parsed_args = parser.parse_args()
+    args = ArgsModel(**vars(parsed_args))  # convert parsed args to pydantic model
+    os.makedirs(args.out_dir, exist_ok=True)
+
+    # Copy the model architecture definition to the window directory
+    shutil.copyfile("config.json", os.path.join(args.out_dir, "config.json"))
+    shutil.copyfile("model_config.yaml", os.path.join(args.out_dir, "config.yaml"))
+
+    create_rslearn_data(args)

sentinel2_feedback/dataset.2rows-sample.csv

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sentinel2_feedback/dataset.sample.csv

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