refactored download and preselection.
This commit is contained in:
Felix Delattre
parent
3919b8e871
commit
ac0e687956
8 changed files with 206 additions and 164 deletions
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@ -44,6 +44,12 @@ def _find_start_offset(site_name, start_dt, total_count):
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def download_phenocam(season, site_position, site_name, date_range=None):
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"""Wrapper that downloads both phenocam images and GCC time series."""
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_download_phenocam_images(season, site_position, site_name, date_range)
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_download_phenocam_gcc(season, site_position, site_name, date_range)
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def _download_phenocam_images(season, site_position, site_name, date_range=None):
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lat, lon = site_position
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datetime_range = date_range or f"{season}-01-01/{season}-12-31"
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output_dir = Path(f"data/{site_name}/{season}/raw/phenocam/")
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@ -149,10 +155,10 @@ def download_phenocam(season, site_position, site_name, date_range=None):
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print("[PhenoCam] Completed")
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def download_phenocam_greenness(season, site_position, site_name, date_range=None):
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"""Fetch greenness-index time series from PhenoCam API."""
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def _download_phenocam_gcc(season, site_position, site_name, date_range=None):
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"""Fetch greenness-index time series from PhenoCam API. Saves JSON and CSV."""
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datetime_range = date_range or f"{season}-01-01/{season}-12-31"
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output_file = Path(f"data/{site_name}/{season}/raw/phenocam/timeseries.json")
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output_file = Path(f"data/{site_name}/{season}/raw/phenocam/phenocam_gcc.json")
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output_file.parent.mkdir(parents=True, exist_ok=True)
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start_date, end_date = datetime_range.split("/")
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@ -210,7 +216,17 @@ def download_phenocam_greenness(season, site_position, site_name, date_range=Non
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return
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timeseries.sort(key=lambda x: x["date"])
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with open(output_file, "w") as f:
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output_dir = output_file.parent
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json_path = output_dir / "phenocam_gcc.json"
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csv_path = output_dir / "phenocam_gcc.csv"
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with open(json_path, "w") as f:
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json.dump(timeseries, f, indent=2)
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print(f"[PhenoCam-GI] Saved: {output_file} ({len(timeseries)} entries)")
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with open(csv_path, "w", newline="") as f:
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writer = csv.DictWriter(f, fieldnames=["date", "greenness_index"])
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writer.writeheader()
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writer.writerows(timeseries)
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print(f"[PhenoCam-GI] Saved: {json_path} and {csv_path} ({len(timeseries)} entries)")
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@ -9,7 +9,7 @@ from rasterio.warp import Resampling, calculate_default_transform, reproject, tr
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from rasterio.windows import from_bounds, transform as window_transform
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from pystac_client import Client
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BBOX_SIZE = 0.016
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BBOX_SIZE = 0.011
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TARGET_CRS = CRS.from_epsg(32632)
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@ -2,7 +2,7 @@
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from pathlib import Path
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from datetime import datetime, timedelta
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from preselection import detect_clouds
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from preselection import create_timeseries
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from preparation import (
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prepare_s2,
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prepare_s3,
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@ -68,8 +68,8 @@ def run_efast(season, site_position, site_name, cleaning_strategy="aggressive",
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def run_all_efast_scenarios(season, site_position, site_name, sigma_value=30, date_range=None):
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create_timeseries(season, site_position, site_name)
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for strategy in ["aggressive", "nonaggressive"]:
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detect_clouds(season, site_name, cleaning_strategy=strategy)
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prepare_s2(season, site_position, site_name, cleaning_strategy=strategy, date_range=date_range)
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prepare_s3(season, site_position, site_name, cleaning_strategy=strategy, date_range=date_range)
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run_efast(season, site_position, site_name, cleaning_strategy=strategy, sigma=None, date_range=date_range)
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@ -6,6 +6,8 @@ from rasterio.warp import transform as transform_coords
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from pathlib import Path
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from datetime import datetime
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from preselection import _sample_3x3
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RED_BAND = 3
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NIR_BAND = 4
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BLUE_BAND = 1
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@ -65,50 +67,6 @@ def _get_ndvi_value(ndvi_file, site_position):
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return None
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def _get_ndvi_from_original(input_file, site_position):
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"""Calculate NDVI directly from original file without creating GeoTIFF."""
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try:
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with rasterio.open(input_file) as src:
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if src.count < 4:
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return None
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red = src.read(RED_BAND).astype(np.float32)
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nir = src.read(NIR_BAND).astype(np.float32)
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lon, lat = site_position[1], site_position[0]
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x, y = transform_coords("EPSG:4326", src.crs, [lon], [lat])
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if not (
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src.bounds.left <= x[0] <= src.bounds.right
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and src.bounds.bottom <= y[0] <= src.bounds.top
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):
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return None
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row, col = src.index(x[0], y[0])
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if row < 0 or row >= src.height or col < 0 or col >= src.width:
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return None
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# Extract 3x3 window with boundary handling
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r0, r1 = max(0, row - 1), min(src.height, row + 2)
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c0, c1 = max(0, col - 1), min(src.width, col + 2)
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red_window = red[r0:r1, c0:c1]
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nir_window = nir[r0:r1, c0:c1]
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# Calculate NDVI for each pixel in window
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mask = (red_window > 0) & (nir_window > 0) & ~np.isnan(red_window) & ~np.isnan(nir_window)
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if not np.any(mask):
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return None
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ndvi_window = np.zeros_like(red_window, dtype=np.float32)
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ndvi_window[mask] = (nir_window[mask] - red_window[mask]) / (nir_window[mask] + red_window[mask])
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# Return mean of valid NDVI values
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valid_ndvi = ndvi_window[mask]
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return float(np.mean(valid_ndvi)) if len(valid_ndvi) > 0 else None
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except Exception as e:
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return None
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def _create_timeseries_for_dir(input_dir, output_dir, site_position, source_name, pattern="*.geotiff"):
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print(f"[NDVI-{source_name}] Creating timeseries.json...")
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timeseries = []
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@ -138,13 +96,17 @@ def _create_timeseries_for_dir(input_dir, output_dir, site_position, source_name
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f"[NDVI-{source_name}] Warning: Could not extract date from {filename}, using '{date_str}'"
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)
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ndvi_value = _get_ndvi_from_original(input_file, site_position)
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ndvi_value, band_means = _sample_3x3(input_file, site_position)
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blue_mean = band_means.get("b02") if band_means else None
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if ndvi_value is None:
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print(
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f"[NDVI-{source_name}] Warning: Could not sample {filename} (outside bounds or nodata)"
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)
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timeseries.append({"date": date, "filename": filename, "ndvi": ndvi_value})
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entry = {"date": date, "filename": filename, "ndvi": ndvi_value}
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if blue_mean is not None:
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entry["blue"] = blue_mean
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timeseries.append(entry)
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timeseries.sort(key=lambda x: x["date"])
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output_dir.mkdir(parents=True, exist_ok=True)
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@ -198,13 +160,6 @@ def generate_ndvi_raw(season, site_position, site_name):
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pass
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def create_ndvi_timeseries_raw(season, site_position, site_name):
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for source in ["s2", "s3"]:
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input_dir = Path(f"data/{site_name}/{season}/raw/{source}/")
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output_dir = Path(f"data/{site_name}/{season}/raw/ndvi/{source}/")
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_create_timeseries_for_dir(input_dir, output_dir, site_position, source.upper())
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def _get_output_name_prepared(geotiff_file):
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if geotiff_file.suffix == ".tif":
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if "REFL" in geotiff_file.stem:
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@ -23,12 +23,16 @@ def _import_distance_to_clouds():
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)
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def _load_clouds(clouds_file):
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def _load_excluded(season, site_name, cleaning_strategy):
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"""Load excluded filenames from NDVI timeseries (excluded_aggressive / excluded_nonaggressive)."""
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base = Path(f"data/{site_name}/{season}/raw/preselection")
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key = f"excluded_{cleaning_strategy}"
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clouds = {"s2": set(), "s3": set()}
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if clouds_file.exists():
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clouds_data = json.loads(clouds_file.read_text())
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clouds["s2"] = set(clouds_data.get("s2", []))
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clouds["s3"] = set(clouds_data.get("s3", []))
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for source in ["s2", "s3"]:
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ts_file = base / f"{source}_preselection.json"
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if ts_file.exists():
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data = json.loads(ts_file.read_text())
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clouds[source] = {e["filename"] for e in data if e.get(key)}
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return clouds
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@ -71,9 +75,8 @@ def prepare_s2(season, site_position, site_name, cleaning_strategy="aggressive",
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s2_dir = Path(f"data/{site_name}/{season}/raw/s2/")
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s3_dir = Path(f"data/{site_name}/{season}/raw/s3/")
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s2_output_dir = _get_base_dir(season, site_name, cleaning_strategy) / "s2"
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clouds_file = Path(f"data/{site_name}/{season}/clouds_{cleaning_strategy}.json")
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clouds = _load_clouds(clouds_file)
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clouds = _load_excluded(season, site_name, cleaning_strategy)
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s2_output_dir.mkdir(parents=True, exist_ok=True)
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s3_files = [f for f in s3_dir.glob("*.geotiff") if f.name not in clouds["s3"]]
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@ -116,9 +119,8 @@ def prepare_s3(season, site_position, site_name, cleaning_strategy="aggressive",
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base_dir = _get_base_dir(season, site_name, cleaning_strategy)
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s2_prepared_dir = base_dir / "s2"
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s3_preprocessed_dir = base_dir / "s3"
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clouds_file = Path(f"data/{site_name}/{season}/clouds_{cleaning_strategy}.json")
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clouds = _load_clouds(clouds_file)
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clouds = _load_excluded(season, site_name, cleaning_strategy)
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s3_preprocessed_dir.mkdir(parents=True, exist_ok=True)
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s3_by_date = defaultdict(list)
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167
preselection.py
167
preselection.py
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@ -1,69 +1,140 @@
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"""Pre-selection: NDVI-based cloud/flaw filtering for S2 and S3 data."""
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"""Pre-selection: self-contained NDVI timeseries with cloud/dark-imagery exclusion markers."""
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import csv
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import json
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import numpy as np
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import rasterio
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from rasterio.warp import transform as transform_coords
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from pathlib import Path
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from datetime import datetime
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WINDOW_DAYS = 14
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MIN_WINDOW_SIZE = 3
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THRESHOLDS = {"aggressive": {"threshold": 0.3, "delta": 0.15}, "nonaggressive": {"threshold": 0.2, "delta": 0.25}}
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BLUE_MIN = 0.01
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GREEN_BAND = 2
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RED_BAND = 3
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NIR_BAND = 4
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BLUE_BAND = 1
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BAND_KEYS = ["b02", "b03", "b04", "b8a"]
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def detect_clouds(season, site_name, cleaning_strategy="aggressive"):
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"""Filter cloud-covered/flawed S2 and S3 files using NDVI thresholds."""
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output_file = Path(f"data/{site_name}/{season}/clouds_{cleaning_strategy}.json")
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clouds = {"s2": [], "s3": []}
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thresholds = THRESHOLDS[cleaning_strategy]
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def _sample_3x3(input_file, site_position):
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"""Sample mean NDVI and all four bands (3x3 window) at site. Returns (ndvi, {b02,b03,b04,b8a}) or (None, None)."""
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try:
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with rasterio.open(input_file) as src:
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if src.count < 4:
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return None, None
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bands = [src.read(i).astype(np.float32) for i in range(1, 5)]
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lon, lat = site_position[1], site_position[0]
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x, y = transform_coords("EPSG:4326", src.crs, [lon], [lat])
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if not (
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src.bounds.left <= x[0] <= src.bounds.right
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and src.bounds.bottom <= y[0] <= src.bounds.top
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):
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return None, None
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row, col = src.index(x[0], y[0])
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if row < 0 or row >= src.height or col < 0 or col >= src.width:
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return None, None
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r0, r1 = max(0, row - 1), min(src.height, row + 2)
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c0, c1 = max(0, col - 1), min(src.width, col + 2)
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windows = [b[r0:r1, c0:c1] for b in bands]
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red_w, nir_w = windows[RED_BAND - 1], windows[NIR_BAND - 1]
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mask = (red_w > 0) & (nir_w > 0) & ~np.isnan(red_w) & ~np.isnan(nir_w)
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if not np.any(mask):
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return None, None
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ndvi = float(np.mean((nir_w[mask] - red_w[mask]) / (nir_w[mask] + red_w[mask])))
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band_means = {k: round(float(np.mean(w[mask])), 6) for k, w in zip(BAND_KEYS, windows)}
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return ndvi, band_means
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except Exception:
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return None, None
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def _extract_date(filename):
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for part in filename.replace(".geotiff", "").split("_"):
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if len(part) == 8 and part.isdigit():
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return part, datetime.strptime(part, "%Y%m%d").isoformat()
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return None, None
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def _is_excluded(entry, entries, strategy):
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"""True if entry is excluded by strategy (NDVI threshold/delta or dark blue)."""
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th = THRESHOLDS[strategy]
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if entry.get("ndvi") is None:
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return True
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if entry.get("b02") is not None and entry["b02"] < BLUE_MIN:
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return True
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entry_date = datetime.fromisoformat(entry["date"].replace("Z", "+00:00"))
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window_ndvi = []
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for e in entries:
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if e.get("ndvi") is None:
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continue
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d = datetime.fromisoformat(e["date"].replace("Z", "+00:00"))
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if abs((d - entry_date).days) <= WINDOW_DAYS:
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window_ndvi.append(e["ndvi"])
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if len(window_ndvi) < MIN_WINDOW_SIZE:
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return False
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threshold = max(window_ndvi) - th["delta"]
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return entry["ndvi"] < threshold and entry["ndvi"] < th["threshold"]
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def create_timeseries(season, site_position, site_name):
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"""Build NDVI timeseries (3x3 window) for raw S2/S3, with exclusion markers for both strategies."""
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lat, lon = site_position
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base = Path(f"data/{site_name}/{season}")
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print(f"[PRESELECT] Creating NDVI timeseries: {site_name} ({lat:.6f}, {lon:.6f}), {season}")
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for source in ["s2", "s3"]:
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timeseries_file = Path(
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f"data/{site_name}/{season}/raw/ndvi/{source}/timeseries.json"
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)
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if not timeseries_file.exists():
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print(f"[CLOUDS-{source.upper()}] No timeseries.json found")
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input_dir = base / "raw" / source
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out_dir = base / "raw" / "preselection"
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out_dir.mkdir(parents=True, exist_ok=True)
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output_file = out_dir / f"{source}_preselection.json"
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if not input_dir.exists():
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print(f"[PRESELECT] Skipping {source}: {input_dir} not found")
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continue
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print(f"[CLOUDS-{source.upper()}] Processing {timeseries_file}...")
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timeseries = []
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for f in sorted(input_dir.glob("*.geotiff")):
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if "DIST_CLOUD" in f.name:
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continue
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date_str, date_iso = _extract_date(f.name)
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if not date_str:
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continue
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ndvi, band_means = _sample_3x3(f, site_position)
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entry = {"filename": f.name, "date": date_iso, "ndvi": ndvi}
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if band_means:
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entry.update(band_means)
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timeseries.append(entry)
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with open(timeseries_file) as f:
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timeseries = json.load(f)
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# Flag entries with ndvi: None as outliers (bad/invalid data)
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timeseries.sort(key=lambda e: e["date"])
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for e in timeseries:
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if e.get("ndvi") is None:
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clouds[source].append(e["filename"])
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e["excluded_aggressive"] = _is_excluded(e, timeseries, "aggressive")
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e["excluded_nonaggressive"] = _is_excluded(e, timeseries, "nonaggressive")
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entries = [
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(e, datetime.fromisoformat(e["date"].replace("Z", "+00:00")))
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for e in timeseries
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if e.get("ndvi") is not None
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]
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with open(output_file, "w") as out:
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json.dump(timeseries, out, indent=2)
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for entry, entry_date in entries:
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window_ndvi = [
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e["ndvi"]
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for e, d in entries
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if abs((d - entry_date).days) <= WINDOW_DAYS
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]
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csv_file = out_dir / f"{source}_preselection.csv"
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fieldnames = ["filename", "date", "ndvi"] + BAND_KEYS + ["excluded_aggressive", "excluded_nonaggressive"]
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with open(csv_file, "w", newline="") as out:
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w = csv.DictWriter(out, fieldnames=fieldnames, extrasaction="ignore")
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w.writeheader()
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for e in timeseries:
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w.writerow({k: e.get(k) for k in fieldnames})
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if len(window_ndvi) < MIN_WINDOW_SIZE:
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continue
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n_excl_agg = sum(1 for e in timeseries if e["excluded_aggressive"])
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n_excl_non = sum(1 for e in timeseries if e["excluded_nonaggressive"])
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print(f"[PRESELECT] Saved {output_file} + {csv_file.name}: {len(timeseries)} entries ({n_excl_agg} aggressive, {n_excl_non} nonaggressive excluded)")
|
||||
|
||||
max_ndvi = max(window_ndvi)
|
||||
threshold = max_ndvi - thresholds["delta"]
|
||||
|
||||
if entry["ndvi"] < threshold and entry["ndvi"] < thresholds["threshold"]:
|
||||
clouds[source].append(entry["filename"])
|
||||
|
||||
print(
|
||||
f"[CLOUDS-{source.upper()}] Found {len(clouds[source])} cloud-covered files"
|
||||
)
|
||||
|
||||
output_file.parent.mkdir(parents=True, exist_ok=True)
|
||||
with open(output_file, "w") as f:
|
||||
json.dump(clouds, f, indent=2)
|
||||
|
||||
print(f"[CLOUDS] Saved: {output_file}")
|
||||
print("[PRESELECT] Completed")
|
||||
|
||||
|
||||
# Alias for backward compatibility
|
||||
preselect = detect_clouds
|
||||
# Backward compatibility
|
||||
def detect_clouds(season, site_position, site_name, cleaning_strategy="aggressive"):
|
||||
"""Create timeseries with exclusion markers. Strategy is read from timeseries when preparing."""
|
||||
create_timeseries(season, site_position, site_name)
|
||||
|
||||
|
||||
preselect = create_timeseries
|
||||
|
|
|
|||
74
run.py
74
run.py
|
|
@ -1,46 +1,39 @@
|
|||
from fusion import run_all_efast_scenarios
|
||||
from postprocessing import process_all_scenarios
|
||||
from metrics_indices import (
|
||||
create_ndvi_timeseries_raw,
|
||||
create_ndvi_timeseries_post_process,
|
||||
create_gcc_timeseries_post_process,
|
||||
create_s2_bands_timeseries_post_process,
|
||||
)
|
||||
# from fusion import run_all_efast_scenarios
|
||||
# from postprocessing import process_all_scenarios
|
||||
# from metrics_indices import (
|
||||
# create_ndvi_timeseries_post_process,
|
||||
# create_gcc_timeseries_post_process,
|
||||
# create_s2_bands_timeseries_post_process,
|
||||
# )
|
||||
from acquisition_s2 import download_s2
|
||||
from acquisition_s3 import download_s3
|
||||
from acquisition_phenocam import download_phenocam, download_phenocam_greenness
|
||||
from metrics_stats import calculate_all_metrics
|
||||
from acquisition_phenocam import download_phenocam
|
||||
from preselection import create_timeseries
|
||||
# from metrics_stats import calculate_all_metrics
|
||||
|
||||
|
||||
def run_pipeline(season, site_position, site_name):
|
||||
"""Run pipeline (downloads + EFAST fusion + post-process + metrics)."""
|
||||
"""Run pipeline (downloads + preselection)."""
|
||||
try:
|
||||
# Download steps (needed for new site/season)
|
||||
#download_s2(season, site_position, site_name)
|
||||
#download_s3(season, site_position, site_name)
|
||||
#download_phenocam(season, site_position, site_name)
|
||||
#download_phenocam_greenness(season, site_position, site_name)
|
||||
download_s2(season, site_position, site_name)
|
||||
download_s3(season, site_position, site_name)
|
||||
download_phenocam(season, site_position, site_name)
|
||||
|
||||
#print(f"Generating NDVI for raw data: {site_name}, {season}")
|
||||
#create_ndvi_timeseries_raw(season, site_position, site_name)
|
||||
print(f"Creating preselection timeseries: {site_name}, {season}")
|
||||
create_timeseries(season, site_position, site_name)
|
||||
|
||||
print(f"Running EFAST fusion for all scenarios: {site_name}, {season}")
|
||||
run_all_efast_scenarios(season, site_position, site_name)
|
||||
|
||||
print(f"Post-processing data: {site_name}, {season}")
|
||||
process_all_scenarios(season, site_position, site_name)
|
||||
|
||||
print(f"Generating NDVI for final outputs: {site_name}, {season}")
|
||||
create_ndvi_timeseries_post_process(season, site_position, site_name)
|
||||
|
||||
print(f"Generating GCC for final outputs: {site_name}, {season}")
|
||||
create_gcc_timeseries_post_process(season, site_position, site_name)
|
||||
|
||||
print(f"Generating S2 band timeseries: {site_name}, {season}")
|
||||
create_s2_bands_timeseries_post_process(season, site_position, site_name)
|
||||
|
||||
print(f"Calculating metrics: {site_name}, {season}")
|
||||
calculate_all_metrics(season, site_name, site_position)
|
||||
# print(f"Running EFAST fusion for all scenarios: {site_name}, {season}")
|
||||
# run_all_efast_scenarios(season, site_position, site_name)
|
||||
# print(f"Post-processing data: {site_name}, {season}")
|
||||
# process_all_scenarios(season, site_position, site_name)
|
||||
# print(f"Generating NDVI for final outputs: {site_name}, {season}")
|
||||
# create_ndvi_timeseries_post_process(season, site_position, site_name)
|
||||
# print(f"Generating GCC for final outputs: {site_name}, {season}")
|
||||
# create_gcc_timeseries_post_process(season, site_position, site_name)
|
||||
# print(f"Generating S2 band timeseries: {site_name}, {season}")
|
||||
# create_s2_bands_timeseries_post_process(season, site_position, site_name)
|
||||
# print(f"Calculating metrics: {site_name}, {season}")
|
||||
# calculate_all_metrics(season, site_name, site_position)
|
||||
|
||||
except Exception as e:
|
||||
print(f"Error: {e}")
|
||||
|
|
@ -48,8 +41,11 @@ def run_pipeline(season, site_position, site_name):
|
|||
|
||||
|
||||
if __name__ == "__main__":
|
||||
|
||||
run_pipeline(2024, (47.116171, 11.320308), "innsbruck")
|
||||
# forthgr - FORTH Heraklion Greece, Agriculture, 2024
|
||||
# sites.geojson: lon=25.0743, lat=35.3045
|
||||
#run_pipeline(2024, (35.3045, 25.0743), "forthgr")
|
||||
run_pipeline(2024, (47.116171, 11.320308), "innsbruck")
|
||||
#run_pipeline(2020, (47.116171, 11.320308), "innsbruck")
|
||||
#run_pipeline(2024, (58.5633, 24.3688), "pitsalu")
|
||||
#run_pipeline(2023, (64.2437, 19.7673), "vindeln2")
|
||||
#run_pipeline(2024, (36.7455, -6.0033), "sunflowerjerez1")
|
||||
#run_pipeline(2024, (42.6558, 26.9837), "institutekarnobat")
|
||||
|
||||
|
|
|
|||
|
|
@ -84,13 +84,15 @@
|
|||
const showCloudsCheckbox = document.getElementById("showClouds");
|
||||
|
||||
async function loadTimeseries() {
|
||||
const [s2, s3, cloudData] = await Promise.all([
|
||||
fetch("../data/innsbruck/2024/raw/ndvi/s2/timeseries.json").then(r => r.json()),
|
||||
fetch("../data/innsbruck/2024/raw/ndvi/s3/timeseries.json").then(r => r.json()),
|
||||
fetch("../data/innsbruck/2024/clouds.json").then(r => r.json()).catch(() => ({ s2: [], s3: [] }))
|
||||
const [s2, s3] = await Promise.all([
|
||||
fetch("../data/innsbruck/2024/raw/preselection/s2_preselection.json").then(r => r.json()),
|
||||
fetch("../data/innsbruck/2024/raw/preselection/s3_preselection.json").then(r => r.json())
|
||||
]);
|
||||
timeseries = { s2, s3 };
|
||||
clouds = { s2: new Set(cloudData.s2 || []), s3: new Set(cloudData.s3 || []) };
|
||||
clouds = {
|
||||
s2: new Set((s2 || []).filter(e => e.excluded_aggressive).map(e => e.filename)),
|
||||
s3: new Set((s3 || []).filter(e => e.excluded_aggressive).map(e => e.filename))
|
||||
};
|
||||
drawTimeseries();
|
||||
}
|
||||
|
||||
|
|
@ -244,7 +246,7 @@
|
|||
}
|
||||
|
||||
async function loadNDVI(source, filename) {
|
||||
const tiff = await GeoTIFF.fromArrayBuffer(await (await fetch(`../data/innsbruck/2024/raw/ndvi/${source}/${filename}`)).arrayBuffer());
|
||||
const tiff = await GeoTIFF.fromArrayBuffer(await (await fetch(`../data/innsbruck/2024/raw/${source}/${filename}`)).arrayBuffer());
|
||||
const image = await tiff.getImage();
|
||||
const data = Array.from((await image.readRasters())[0]);
|
||||
const width = image.getWidth();
|
||||
|
|
@ -303,7 +305,7 @@
|
|||
const filename = `${date}_${i}.geotiff`;
|
||||
if (!showCloudsCheckbox.checked && clouds[source] && clouds[source].has(filename)) continue;
|
||||
try {
|
||||
const res = await fetch(`../data/innsbruck/2024/raw/ndvi/${source}/${filename}`);
|
||||
const res = await fetch(`../data/innsbruck/2024/raw/${source}/${filename}`);
|
||||
if (res.ok) return filename;
|
||||
} catch {}
|
||||
}
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue