Clay pipeline v04

docs/data_datacube.md

@@ -29,11 +29,11 @@ Build the docker image and push it to a ecr repository.
 ```bash
 ecr_repo_id=12345
 cd scripts/pipeline/batch
-docker build -t $ecr_repo_iud.dkr.ecr.us-east-1.amazonaws.com/fetch-and-run .
+docker build -t $ecr_repo_id.dkr.ecr.us-east-1.amazonaws.com/fetch-and-run .
 
-aws ecr get-login-password --profile clay --region us-east-1 | docker login --username AWS --password-stdin $ecr_repo_iud.dkr.ecr.us-east-1.amazonaws.com
+aws ecr get-login-password --profile clay --region us-east-1 | docker login --username AWS --password-stdin $ecr_repo_id.dkr.ecr.us-east-1.amazonaws.com
 
-docker push $ecr_repo_iud.dkr.ecr.us-east-1.amazonaws.com/fetch-and-run:latest
+docker push $ecr_repo_id.dkr.ecr.us-east-1.amazonaws.com/fetch-and-run
 ```
 
 ### Prepare AWS batch

docs/data_sampling.md

@@ -22,8 +22,12 @@ In addition to the landcover classes, we also added diversity by selecting 500
 tiles out of the 3000 tiles with the highest count of land cover classes present
 in the tile.
 
+After selecting MGRS tiles for each of these criteria, we removed duplicates.
+
 The following table summarizes the selection criteria for each class.
 
+## For model version v0.1
+
 | Class | Nr of Tiles | From highest |
 |---|---|---|
 Diversity | 500 | 3000
@@ -39,9 +43,32 @@ Bare / sparse vegetation | 50 | 500
 Snow and Ice | 50 | 500
 Permanent water bodies | 100 | 1000
 
-After selecting MGRS tiles for each of these criteria, we removed duplicates.
 This resulted in a sample of 1517 MGRS tiles total in our sample.
 
 The resulting sample file can be downloaded from the following link
 
 https://clay-mgrs-samples.s3.amazonaws.com/mgrs_sample.fgb
+
+## For model version v0.2
+
+| Class | Nr of Tiles | From highest |
+|---|---|---|
+Diversity | 400 | 2000
+Built-up | 300 | 300
+Built-up | 1000 | 1500
+Herbaceous wetland | 50 | 500
+Mangroves | 50 | 500
+Moss and lichen | 50 | 500
+Cropland | 800 | 3600
+Tree cover | 150 | 750
+Shrubland | 100 | 500
+Grassland | 200 | 500
+Bare / sparse vegetation | 50 | 500
+Snow and Ice | 25 | 500
+Permanent water bodies | 50 | 1000
+
+This resulted in a sample of 2728 MGRS tiles total in our sample.
+
+The resulting sample file can be downloaded from the following link
+
+https://clay-mgrs-samples.s3.amazonaws.com/mgrs_sample_v02.fgb

scripts/pipeline/batch/submit.py

@@ -1,31 +1,40 @@
+import os
+
 import boto3
 
 batch = boto3.client("batch", region_name="us-east-1")
 
-NR_OF_TILES_IN_SAMPLE_FILE = 1517
+NR_OF_TILES_IN_SAMPLE_FILE = 2728
+
+PC_KEY = os.environ["PC_SDK_SUBSCRIPTION_KEY"]
 
-PC_KEY = "***"
 
-for i in range(NR_OF_TILES_IN_SAMPLE_FILE):
-    job = {
-        "jobName": f"fetch-and-run-{i}",
-        "jobQueue": "fetch-and-run",
-        "jobDefinition": "fetch-and-run",
-        "containerOverrides": {
-            "command": ["datacube.py", "--index", f"{i}", "--bucket", "clay-tiles-02"],
-            "environment": [
-                {"name": "BATCH_FILE_TYPE", "value": "zip"},
-                {
-                    "name": "BATCH_FILE_S3_URL",
-                    "value": "s3://clay-fetch-and-run-packages/batch-fetch-and-run.zip",
-                },
-                {"name": "PC_SDK_SUBSCRIPTION_KEY", "value": f"{PC_KEY}"},
-            ],
-            "resourceRequirements": [
-                {"type": "MEMORY", "value": "8000"},
-                {"type": "VCPU", "value": "4"},
-            ],
-        },
-    }
+job = {
+    "jobName": "fetch-and-run-datacube",
+    "jobQueue": "fetch-and-run",
+    "jobDefinition": "fetch-and-run",
+    "containerOverrides": {
+        "command": [
+            "datacube.py",
+            "--bucket",
+            "clay-tiles-04-sample-v02",
+            "--sample",
+            "https://clay-mgrs-samples.s3.amazonaws.com/mgrs_sample_v02.fgb",
+        ],
+        "environment": [
+            {"name": "BATCH_FILE_TYPE", "value": "zip"},
+            {
+                "name": "BATCH_FILE_S3_URL",
+                "value": "s3://clay-fetch-and-run-packages/batch-fetch-and-run.zip",
+            },
+            {"name": "PC_SDK_SUBSCRIPTION_KEY", "value": f"{PC_KEY}"},
+        ],
+        "resourceRequirements": [
+            {"type": "MEMORY", "value": "15500"},
+            {"type": "VCPU", "value": "4"},
+        ],
+    },
+    "arrayProperties": {"size": NR_OF_TILES_IN_SAMPLE_FILE},
+}
 
-    print(batch.submit_job(**job))
+print(batch.submit_job(**job))

scripts/pipeline/datacube.py

@@ -26,6 +26,8 @@
       Process Sentinel-2, Sentinel-1, and DEM data for a specified time range,
       area of interest, and resolution.
 """
+import logging
+import os
 import random
 from datetime import timedelta
 
@@ -46,8 +48,15 @@
 CLOUD_COVER_PERCENTAGE = 50
 NODATA_PIXEL_PERCENTAGE = 20
 NODATA = 0
-S1_MATCH_ATTEMPTS = 20
-DATES_PER_LOCATION = 3
+S1_MATCH_ATTEMPTS = 40
+DATES_PER_LOCATION = 4
+
+logger = logging.getLogger("datacube")
+hdr = logging.StreamHandler()
+formatter = logging.Formatter("[%(asctime)s] %(levelname)s: %(message)s")
+hdr.setFormatter(formatter)
+logger.addHandler(hdr)
+logger.setLevel(logging.INFO)
 
 
 def get_surrounding_days(reference, interval_days):
@@ -123,7 +132,7 @@ def search_sentinel2(
     )
 
     s2_items = search.item_collection()
-    print(f"Found {len(s2_items)} Sentinel-2 items")
+    logger.info(f"Found {len(s2_items)} Sentinel-2 items")
     if not len(s2_items):
         return None, None
 
@@ -143,7 +152,7 @@ def search_sentinel2(
     bbox = least_clouds.geometry.bounds
 
     epsg = s2_item.properties["proj:epsg"]
-    print("EPSG code based on Sentinel-2 item: ", epsg)
+    logger.info(f"EPSG code based on Sentinel-2 item: {epsg}")
 
     return s2_item, bbox
 
@@ -187,7 +196,7 @@ def search_sentinel1(bbox, catalog, date_range):
         },
     )
     s1_items = search.item_collection()
-    print(f"Found {len(s1_items)} Sentinel-1 items")
+    logger.info(f"Found {len(s1_items)} Sentinel-1 items")
 
     if not len(s1_items):
         return
@@ -203,7 +212,7 @@ def search_sentinel1(bbox, catalog, date_range):
         s1_gdf = s1_gdf.sort_values(by="overlap", ascending=False)
 
         most_overlap_orbit = s1_gdf.iloc[0]["sat:orbit_state"]
-        print("Most overlapped orbit: ", most_overlap_orbit)
+        logger.info(f"Most overlapped orbit: {most_overlap_orbit}")
         selected_item_ids = []
         intersection = None
         orbit = None
@@ -246,7 +255,7 @@ def search_dem(bbox, catalog):
     """
     search = catalog.search(collections=["cop-dem-glo-30"], bbox=bbox)
     dem_items = search.item_collection()
-    print(f"Found {len(dem_items)} DEM items")
+    logger.info(f"Found {len(dem_items)} DEM items")
 
     return dem_items
 
@@ -354,15 +363,15 @@ def process(
             continue
 
         surrounding_days = get_surrounding_days(s2_item.datetime, interval_days=3)
-        print("Searching S1 in date range", surrounding_days)
+        logger.info(f"Searching S1 in date range {surrounding_days}")
 
         s1_items = search_sentinel1(bbox, catalog, surrounding_days)
 
         if s1_items:
             break
 
     if i == S1_MATCH_ATTEMPTS - 1:
-        print(
+        logger.info(
             "No match for S1 scenes found for date range "
             f"{date_range} after {S1_MATCH_ATTEMPTS} attempts."
         )
@@ -379,6 +388,10 @@ def process(
         resolution,
     )
 
+    if 0 in (dat.shape[0] for dat in result):
+        logger.info("S2/S1 pixel coverages do not overlap although bounds do")
+        return None, None
+
     return date, result
 
 
@@ -404,13 +417,13 @@ def convert_attrs_and_coords_objects_to_str(data):
 @click.option(
     "--sample",
     required=False,
-    default="https://clay-mgrs-samples.s3.amazonaws.com/mgrs_sample.fgb",
+    default="https://clay-mgrs-samples.s3.amazonaws.com/mgrs_sample_v02.fgb",
     help="Location of MGRS tile sample",
 )
 @click.option(
     "--index",
     required=False,
-    default=0,
+    default=None,
     help="Index of MGRS tile from sample file that should be processed",
 )
 @click.option(
@@ -441,13 +454,20 @@ def convert_attrs_and_coords_objects_to_str(data):
     type=str,
     help="Comma separated list of date ranges, each provided as YYYY-MM-DD/YYYY-MM-DD.",
 )
-def main(sample, index, subset, bucket, localpath, dateranges):
-    index = int(index)
+@click.option("-v", "--verbose", is_flag=True)
+def main(sample, index, subset, bucket, localpath, dateranges, verbose):  # noqa: PLR0913
+    if verbose:
+        logger.setLevel(logging.DEBUG)
+
+    if index is None:
+        index = int(os.environ.get("AWS_BATCH_JOB_ARRAY_INDEX", 0))
+    else:
+        index = int(index)
     tiles = gpd.read_file(sample)
     tile = tiles.iloc[index]
     mgrs = tile["name"]
 
-    print(f"Starting algorithm for MGRS tile {tile['name']} with index {index}")
+    logger.info(f"Starting algorithm for MGRS tile {tile['name']} with index {index}")
 
     if subset:
         subset = [int(dat) for dat in subset.split(",")]
@@ -466,7 +486,7 @@ def main(sample, index, subset, bucket, localpath, dateranges):
 
     match_count = 0
     for date_range in date_ranges:
-        print(f"Processing data for date range {date_range}")
+        logger.info(f"Processing data for date range {date_range}")
         date, pixels = process(
             tile.geometry,
             date_range,
@@ -480,7 +500,7 @@ def main(sample, index, subset, bucket, localpath, dateranges):
             match_count += 1
 
         if subset:
-            print(f"Subsetting to {subset}")
+            logger.info(f"Subsetting to {subset}")
             pixels = [
                 part[:, subset[1] : subset[3], subset[0] : subset[2]] for part in pixels
             ]
@@ -493,7 +513,7 @@ def main(sample, index, subset, bucket, localpath, dateranges):
             break
 
     if not match_count:
-        raise ValueError("No matching data found")
+        logger.info("No matching data found")
 
 
 if __name__ == "__main__":

scripts/pipeline/landcover.py

@@ -13,9 +13,7 @@
 WGS84 = CRS.from_epsg(4326)
 NODATA = 0
 WATER = 80
-WATER_LOWER_TH = 0.2
-WATER_UPPER_TH = 0.7
-RANDOM_SEED = 42
+RANDOM_SEED = 23
 CLASSES = {
     10: "Tree cover",
     20: "Shrubland",
@@ -156,31 +154,25 @@ def percentages(data):
 def sample(wd):
     """
     Sample the mgrs tiles based on landcover statistics.
-
-    Target: ~1000 tiles
-    Set very small counts to zero. Exclude high latitudes.
-    200 samples from the 2000 most diverse
-    50 samples from the 1000 highest for all other categories except water
-    100 samples from all tiles with water between 30% an 70% (making sure we
-    capture some, but exclude only purely water so we catch coasts)
     """
     data = geopandas.read_file(Path(wd, "mgrs_stats.fgb"))
 
     data_norm = percentages(data.loc[:, data.columns != "count"])
     data[data_norm.columns] = data_norm
 
-    diversity = split_highest(data, "count", 500, 3000)
-    urban = split_highest(data, "Built-up", 400)
+    diversity = split_highest(data, "count", 400, 2000)
+    urban = split_highest(data, "Built-up", 300, 300)
+    urban = split_highest(data, "Built-up", 1000, 1500)
     wetland = split_highest(data, "Herbaceous wetland", 50, 500)
     mangroves = split_highest(data, "Mangroves", 50, 500)
     moss = split_highest(data, "Moss and lichen", 50, 500)
-    cropland = split_highest(data, "Cropland", 100, 500)
-    trees = split_highest(data, "Tree cover", 100, 500)
-    shrubland = split_highest(data, "Shrubland", 50, 500)
-    grassland = split_highest(data, "Grassland", 50, 500)
+    cropland = split_highest(data, "Cropland", 800, 3600)
+    trees = split_highest(data, "Tree cover", 150, 750)
+    shrubland = split_highest(data, "Shrubland", 100, 500)
+    grassland = split_highest(data, "Grassland", 200, 500)
     bare = split_highest(data, "Bare / sparse vegetation", 50, 500)
-    snow = split_highest(data, "Snow and Ice", 50, 500)
-    water = split_highest(data, "Permanent water bodies", 100, 1000)
+    snow = split_highest(data, "Snow and Ice", 25, 250)
+    water = split_highest(data, "Permanent water bodies", 50, 1000)
 
     result = pandas.concat(
         [
@@ -200,6 +192,7 @@ def sample(wd):
     )
 
     result = result.drop_duplicates(subset=["name"])
+    print(f"Found {len(result)} MGRS tiles")
 
     result.to_file(Path(wd, "mgrs_sample.fgb", driver="FlatGeobuf"))