four scenarios.
This commit is contained in:
Felix Delattre
parent
46df3be8e7
commit
903bdc2598
6 changed files with 136 additions and 83 deletions
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@ -36,6 +36,10 @@ def _load_clouds(clouds_file):
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return clouds
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def _get_base_dir(season, site_name, cleaning_strategy):
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return Path(f"data/{site_name}/{season}/prepared_{cleaning_strategy}/")
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def _reproject_raster_to_target(
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src_path,
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dst_path,
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@ -67,11 +71,11 @@ def _reproject_raster_to_target(
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rio_shutil.copy(vrt, dst_path, **profile)
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def prepare_s2(season, site_position, site_name, date_range=None):
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def prepare_s2(season, site_position, site_name, cleaning_strategy="aggressive", date_range=None):
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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 = Path(f"data/{site_name}/{season}/prepared/s2/")
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clouds_file = Path(f"data/{site_name}/{season}/clouds.json")
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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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s2_output_dir.mkdir(parents=True, exist_ok=True)
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@ -111,11 +115,12 @@ def prepare_s2(season, site_position, site_name, date_range=None):
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distance_to_clouds(s2_output_dir, ratio=RESOLUTION_RATIO)
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def prepare_s3(season, site_position, site_name, date_range=None):
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def prepare_s3(season, site_position, site_name, cleaning_strategy="aggressive", date_range=None):
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s3_dir = Path(f"data/{site_name}/{season}/raw/s3/")
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s2_prepared_dir = Path(f"data/{site_name}/{season}/prepared/s2/")
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s3_preprocessed_dir = Path(f"data/{site_name}/{season}/prepared/s3/")
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clouds_file = Path(f"data/{site_name}/{season}/clouds.json")
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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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s3_preprocessed_dir.mkdir(parents=True, exist_ok=True)
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@ -192,14 +197,14 @@ def prepare_s3(season, site_position, site_name, date_range=None):
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shutil.rmtree(temp_composite_dir)
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def run_efast(season, site_position, site_name, date_range=None):
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def run_efast(season, site_position, site_name, cleaning_strategy="aggressive", sigma=None, 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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efast_base_dir = Path(f"data/{site_name}/{season}/prepared/")
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efast_base_dir = _get_base_dir(season, site_name, cleaning_strategy)
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s2_output_dir = efast_base_dir / "s2"
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s3_output_dir = efast_base_dir / "s3"
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fusion_output_dir = efast_base_dir / "fusion"
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fusion_output_dir = efast_base_dir / (f"fusion_sigma{sigma}" if sigma else "fusion")
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fusion_output_dir.mkdir(parents=True, exist_ok=True)
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print(f"[EFAST] Starting fusion: {site_name} ({lat:.6f}, {lon:.6f}), {season}")
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@ -215,17 +220,16 @@ def run_efast(season, site_position, site_name, date_range=None):
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date_str = current_date.strftime("%Y%m%d")
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output_file = fusion_output_dir / f"REFL_{date_str}.tif"
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try:
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efast.fusion(
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current_date,
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s3_output_dir,
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s2_output_dir,
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fusion_output_dir,
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product="REFL",
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max_days=30,
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date_position=2,
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minimum_acquisition_importance=0.0,
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ratio=RESOLUTION_RATIO,
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)
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kwargs = {
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"product": "REFL",
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"max_days": 30,
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"date_position": 2,
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"minimum_acquisition_importance": 0.0,
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"ratio": RESOLUTION_RATIO,
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}
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if sigma is not None:
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kwargs["sigma"] = sigma
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efast.fusion(current_date, s3_output_dir, s2_output_dir, fusion_output_dir, **kwargs)
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print(
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f"[EFAST] Saved: {output_file}"
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if output_file.exists()
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@ -236,3 +240,14 @@ def run_efast(season, site_position, site_name, date_range=None):
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current_date += timedelta(days=1)
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print("[EFAST] Completed")
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def run_all_efast_scenarios(season, site_position, site_name, sigma_value=30, date_range=None):
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from clouds import detect_clouds
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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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run_efast(season, site_position, site_name, cleaning_strategy=strategy, sigma=sigma_value, date_range=date_range)
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12
clouds.py
12
clouds.py
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@ -3,14 +3,14 @@ from pathlib import Path
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from datetime import datetime
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WINDOW_DAYS = 14
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NDVI_THRESHOLD = 0.3
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NDVI_DELTA = 0.15
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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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def detect_clouds(season, site_name):
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output_file = Path(f"data/{site_name}/{season}/clouds.json")
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def detect_clouds(season, site_name, cleaning_strategy="aggressive"):
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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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for source in ["s2", "s3"]:
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timeseries_file = Path(
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@ -47,9 +47,9 @@ def detect_clouds(season, site_name):
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continue
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max_ndvi = max(window_ndvi)
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threshold = max_ndvi - NDVI_DELTA
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threshold = max_ndvi - thresholds["delta"]
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if entry["ndvi"] < threshold and entry["ndvi"] < NDVI_THRESHOLD:
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if entry["ndvi"] < threshold and entry["ndvi"] < thresholds["threshold"]:
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clouds[source].append(entry["filename"])
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print(
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@ -235,16 +235,18 @@ def generate_ndvi_post_process(season, site_position, site_name):
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def create_ndvi_timeseries_post_process(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}/processed/{source}/")
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output_dir = Path(f"data/{site_name}/{season}/processed/ndvi/{source}/")
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_create_timeseries_for_dir(
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input_dir, output_dir, site_position, f"POST-PROCESS-{source.upper()}"
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)
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input_dir = Path(f"data/{site_name}/{season}/processed/fusion/")
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output_dir = Path(f"data/{site_name}/{season}/processed/ndvi/fusion/")
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_create_timeseries_for_dir(input_dir, output_dir, site_position, "POST-PROCESS-FUSION")
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for strategy in ["aggressive", "nonaggressive"]:
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for sigma in [20, 30]:
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processed_dir = f"processed_{strategy}_sigma{sigma}"
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for source in ["s2", "s3"]:
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input_dir = Path(f"data/{site_name}/{season}/{processed_dir}/{source}/")
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output_dir = Path(f"data/{site_name}/{season}/{processed_dir}/ndvi/{source}/")
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_create_timeseries_for_dir(
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input_dir, output_dir, site_position, f"POST-PROCESS-{source.upper()}-{strategy}-σ{sigma}"
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)
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input_dir = Path(f"data/{site_name}/{season}/{processed_dir}/fusion/")
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output_dir = Path(f"data/{site_name}/{season}/{processed_dir}/ndvi/fusion/")
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_create_timeseries_for_dir(input_dir, output_dir, site_position, f"POST-PROCESS-FUSION-{strategy}-σ{sigma}")
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def _calculate_and_write_gcc(input_file, output_file):
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@ -434,13 +436,15 @@ def generate_gcc_post_process(season, site_position, site_name):
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def create_gcc_timeseries_post_process(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}/processed/{source}/")
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output_dir = Path(f"data/{site_name}/{season}/processed/gcc/{source}/")
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_create_gcc_timeseries_for_dir(
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input_dir, output_dir, site_position, f"POST-PROCESS-{source.upper()}"
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)
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input_dir = Path(f"data/{site_name}/{season}/processed/fusion/")
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output_dir = Path(f"data/{site_name}/{season}/processed/gcc/fusion/")
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_create_gcc_timeseries_for_dir(input_dir, output_dir, site_position, "POST-PROCESS-FUSION")
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for strategy in ["aggressive", "nonaggressive"]:
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for sigma in [20, 30]:
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processed_dir = f"processed_{strategy}_sigma{sigma}"
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for source in ["s2", "s3"]:
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input_dir = Path(f"data/{site_name}/{season}/{processed_dir}/{source}/")
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output_dir = Path(f"data/{site_name}/{season}/{processed_dir}/gcc/{source}/")
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_create_gcc_timeseries_for_dir(
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input_dir, output_dir, site_position, f"POST-PROCESS-{source.upper()}-{strategy}-σ{sigma}"
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)
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input_dir = Path(f"data/{site_name}/{season}/{processed_dir}/fusion/")
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output_dir = Path(f"data/{site_name}/{season}/{processed_dir}/gcc/fusion/")
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_create_gcc_timeseries_for_dir(input_dir, output_dir, site_position, f"POST-PROCESS-FUSION-{strategy}-σ{sigma}")
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@ -6,20 +6,21 @@ from rasterio.warp import reproject, Resampling
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from rasterio.io import MemoryFile
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def process_cropped(season, site_position, site_name):
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def process_cropped(season, site_position, site_name, cleaning_strategy="aggressive", sigma=None):
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"""Crop fusion to valid data, then crop S2/S3 to match."""
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base = Path(f"data/{site_name}/{season}")
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prepared = base / "prepared"
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processed = base / "processed"
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prepared = base / f"prepared_{cleaning_strategy}"
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processed_dir = f"processed_{cleaning_strategy}_sigma{sigma}" if sigma else f"processed_{cleaning_strategy}_sigma20"
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processed = base / processed_dir
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s2_prep = prepared / "s2"
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s3_prep = prepared / "s3"
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fusion_prep = prepared / "fusion"
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fusion_prep = prepared / (f"fusion_sigma{sigma}" if sigma else "fusion")
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for output_dir in [processed / "s2", processed / "s3", processed / "fusion"]:
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output_dir.mkdir(parents=True, exist_ok=True)
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print(f"[PROCESS] Processing files: {site_name}, {season}")
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print(f"[PROCESS] Processing files: {site_name}, {season}, {cleaning_strategy}, sigma={sigma or 20}")
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# Crop fusion to valid data and get dimensions
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fusion_dims = {}
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@ -87,3 +88,10 @@ def process_cropped(season, site_position, site_name):
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print(f"[PROCESS] Cropped: {output_file}")
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print("[PROCESS] Completed")
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def process_all_scenarios(season, site_position, site_name):
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"""Process all 4 EFAST scenarios."""
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for strategy in ["aggressive", "nonaggressive"]:
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for sigma in [None, 30]:
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process_cropped(season, site_position, site_name, cleaning_strategy=strategy, sigma=sigma)
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29
run.py
29
run.py
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@ -1,5 +1,5 @@
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from call_efast import run_efast, prepare_s2, prepare_s3
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from post_process import process_cropped
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from call_efast import run_all_efast_scenarios
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from post_process import process_all_scenarios
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from generate_indexes import (
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generate_ndvi_raw,
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create_ndvi_timeseries_raw,
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@ -23,26 +23,17 @@ def run_pipeline(season, site_position, site_name):
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#download_phenocam_greenness(season, site_position, site_name)
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# print(f"Generating NDVI for raw data: {site_name}, {season}")
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# generate_ndvi_raw(season, site_position, site_name)
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create_ndvi_timeseries_raw(season, site_position, site_name)
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# create_ndvi_timeseries_raw(season, site_position, site_name)
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# print(f"Detecting clouds for {site_name}, {season}")
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#detect_clouds(season, site_name)
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# print(f"Running EFAST fusion for all scenarios: {site_name}, {season}")
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# run_all_efast_scenarios(season, site_position, site_name)
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#print(f"Preparing data for EFAST fusion for {site_name}, {season}")
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#prepare_s2(season, site_position, site_name)
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#prepare_s3(season, site_position, site_name)
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#print(f"Running EFAST fusion for {site_name}, {season}")
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#run_efast(season, site_position, site_name)
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#print(f"Post-processing data: {site_name}, {season}")
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#process_cropped(season, site_position, site_name)
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#print(f"Generating NDVI for final outputs: {site_name}, {season}")
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#generate_ndvi_post_process(season, site_position, site_name)
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print(f"Post-processing data: {site_name}, {season}")
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process_all_scenarios(season, site_position, site_name)
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print(f"Generating NDVI for final outputs: {site_name}, {season}")
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create_ndvi_timeseries_post_process(season, site_position, site_name)
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#print(f"Generating GCC for final outputs: {site_name}, {season}")
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generate_gcc_post_process(season, site_position, site_name)
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print(f"Generating GCC for final outputs: {site_name}, {season}")
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# generate_gcc_post_process(season, site_position, site_name) # No-op function
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create_gcc_timeseries_post_process(season, site_position, site_name)
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except Exception as e:
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@ -9,6 +9,8 @@
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<style>
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body { margin: 0; font-family: sans-serif; }
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.slider-container { position: sticky; top: 0; background: white; padding: 20px; z-index: 1000; border-bottom: 1px solid #ccc; }
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.scenario-selector { margin-bottom: 10px; }
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.scenario-selector select { padding: 5px 10px; font-size: 14px; }
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.container { max-width: 1400px; margin: 0 auto; padding: 20px; }
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.header { display: flex; gap: 20px; margin-bottom: 20px; border-bottom: 1px solid #ccc; padding-top: 10px;padding-bottom: 20px;}
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.header-col { flex: 1; }
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@ -58,6 +60,15 @@
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<div id="sitemap" class="sitemap"></div>
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</div>
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</div>
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<div class="scenario-selector">
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<label for="scenarioSelect">Scenario: </label>
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<select id="scenarioSelect">
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<option value="aggressive_20">Aggressive (σ=20)</option>
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<option value="aggressive_30">Aggressive (σ=30)</option>
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<option value="nonaggressive_20">Non-aggressive (σ=20)</option>
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<option value="nonaggressive_30">Non-aggressive (σ=30)</option>
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</select>
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</div>
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<div class="maps">
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<div class="map-container">
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<h3>S2</h3>
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@ -106,6 +117,16 @@
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const osmOpts = { attribution: "OpenStreetMap", opacity: 0.4 };
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const mapOpts = { zoomControl: false };
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const sitePosition = [47.116171, 11.320308];
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const siteName = "innsbruck";
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const season = "2024";
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const urlParams = new URLSearchParams(location.search);
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const strategy = urlParams.get("strategy") || "aggressive";
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const sigma = urlParams.get("sigma") || "20";
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function getFusionPath() {
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return `processed_${strategy}_sigma${sigma}`;
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}
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const siteMap = L.map("sitemap", { zoomControl: false }).setView(sitePosition, 4).addLayer(L.tileLayer(osmUrl, { attribution: "OpenStreetMap", opacity: 1 }));
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L.marker(sitePosition, { icon: L.divIcon({ className: "site-marker", html: "<div style='width:8px;height:8px;background:red;border:2px solid white;border-radius:50%;box-shadow:0 0 2px rgba(0,0,0,0.5);'></div>", iconSize: [8, 8] }) }).addTo(siteMap);
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const maps = {
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@ -141,14 +162,15 @@
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});
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async function loadTimeseries() {
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const fusionPath = getFusionPath();
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const [s2, fusion, s3, s2gcc, fusiongcc, s3gcc, phenocam] = await Promise.all([
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fetch("../data/innsbruck/2024/processed/ndvi/s2/timeseries.json").then(r => r.json()),
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fetch("../data/innsbruck/2024/processed/ndvi/fusion/timeseries.json").then(r => r.json()).catch(() => []),
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fetch("../data/innsbruck/2024/processed/ndvi/s3/timeseries.json").then(r => r.json()),
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fetch("../data/innsbruck/2024/processed/gcc/s2/timeseries.json").then(r => r.json()).catch(() => []),
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fetch("../data/innsbruck/2024/processed/gcc/fusion/timeseries.json").then(r => r.json()).catch(() => []),
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fetch("../data/innsbruck/2024/processed/gcc/s3/timeseries.json").then(r => r.json()).catch(() => []),
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fetch("../data/innsbruck/2024/raw/phenocam/timeseries.json").then(r => r.json()).catch(() => [])
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fetch(`../data/${siteName}/${season}/processed_${strategy}_sigma${sigma}/ndvi/s2/timeseries.json`).then(r => r.json()).catch(() => []),
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fetch(`../data/${siteName}/${season}/${fusionPath}/ndvi/fusion/timeseries.json`).then(r => r.json()).catch(() => []),
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fetch(`../data/${siteName}/${season}/processed_${strategy}_sigma${sigma}/ndvi/s3/timeseries.json`).then(r => r.json()).catch(() => []),
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fetch(`../data/${siteName}/${season}/processed_${strategy}_sigma${sigma}/gcc/s2/timeseries.json`).then(r => r.json()).catch(() => []),
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fetch(`../data/${siteName}/${season}/${fusionPath}/gcc/fusion/timeseries.json`).then(r => r.json()).catch(() => []),
|
||||
fetch(`../data/${siteName}/${season}/processed_${strategy}_sigma${sigma}/gcc/s3/timeseries.json`).then(r => r.json()).catch(() => []),
|
||||
fetch(`../data/${siteName}/${season}/raw/phenocam/timeseries.json`).then(r => r.json()).catch(() => [])
|
||||
]);
|
||||
timeseries = { s2, fusion, s3 };
|
||||
greennessTimeseries = { s2: s2gcc, fusion: fusiongcc, s3: s3gcc };
|
||||
|
|
@ -487,6 +509,7 @@
|
|||
|
||||
async function findFile(dateStr, source) {
|
||||
const target = new Date(dateStr);
|
||||
const basePath = source === "fusion" ? getFusionPath() : `processed_${strategy}_sigma${sigma}`;
|
||||
// Search outward from target date (0, ±1, ±2, ±3, ...) until we find the closest file
|
||||
// Check dates in order: exact, then -1, +1, then -2, +2, etc.
|
||||
// Limit to ±365 days to avoid infinite search
|
||||
|
|
@ -496,7 +519,7 @@
|
|||
const date = target.toISOString().split("T")[0].replace(/-/g, "");
|
||||
const filename = `${date}_0.geotiff`;
|
||||
try {
|
||||
const res = await fetch(`../data/innsbruck/2024/processed/${source}/${filename}`, { method: 'HEAD' });
|
||||
const res = await fetch(`../data/${siteName}/${season}/${basePath}/${source}/${filename}`, { method: 'HEAD' });
|
||||
if (res.ok) return filename;
|
||||
} catch {}
|
||||
} else {
|
||||
|
|
@ -507,7 +530,7 @@
|
|||
const date = d.toISOString().split("T")[0].replace(/-/g, "");
|
||||
const filename = `${date}_0.geotiff`;
|
||||
try {
|
||||
const res = await fetch(`../data/innsbruck/2024/processed/${source}/${filename}`, { method: 'HEAD' });
|
||||
const res = await fetch(`../data/${siteName}/${season}/${basePath}/${source}/${filename}`, { method: 'HEAD' });
|
||||
if (res.ok) return filename;
|
||||
} catch {}
|
||||
}
|
||||
|
|
@ -522,7 +545,8 @@
|
|||
}
|
||||
|
||||
async function loadGeotiff(source, filename) {
|
||||
const path = `../data/innsbruck/2024/processed/${source}/${filename}`;
|
||||
const basePath = source === "fusion" ? getFusionPath() : `processed_${strategy}_sigma${sigma}`;
|
||||
const path = `../data/${siteName}/${season}/${basePath}/${source}/${filename}`;
|
||||
const tiff = await GeoTIFF.fromArrayBuffer(await (await fetch(path)).arrayBuffer());
|
||||
const image = await tiff.getImage();
|
||||
const rasters = await image.readRasters();
|
||||
|
|
@ -587,7 +611,7 @@
|
|||
const d = new Date(target);
|
||||
d.setDate(d.getDate() + offset * dir);
|
||||
const date = d.toISOString().split("T")[0].replace(/-/g, "");
|
||||
const url = `../data/innsbruck/2024/raw/phenocam/${date}.jpg`;
|
||||
const url = `../data/${siteName}/${season}/raw/phenocam/${date}.jpg`;
|
||||
try {
|
||||
const res = await fetch(url, { method: 'HEAD' });
|
||||
if (res.ok) {
|
||||
|
|
@ -607,6 +631,8 @@
|
|||
dateDisplay.textContent = date;
|
||||
const params = new URLSearchParams();
|
||||
params.set("date", date);
|
||||
params.set("strategy", strategy);
|
||||
if (sigma !== "20") params.set("sigma", sigma);
|
||||
history.replaceState({}, "", `?${params}`);
|
||||
drawTimeseries();
|
||||
drawGreennessTimeseries();
|
||||
|
|
@ -625,7 +651,16 @@
|
|||
await loadPhenoCam(date);
|
||||
}
|
||||
|
||||
const urlParams = new URLSearchParams(location.search);
|
||||
const scenarioSelect = document.getElementById("scenarioSelect");
|
||||
scenarioSelect.value = `${strategy}_${sigma}`;
|
||||
scenarioSelect.addEventListener("change", function() {
|
||||
const [newStrategy, newSigma] = this.value.split("_");
|
||||
const params = new URLSearchParams(location.search);
|
||||
params.set("strategy", newStrategy);
|
||||
params.set("sigma", newSigma);
|
||||
window.location.search = params.toString();
|
||||
});
|
||||
|
||||
const urlDate = urlParams.get("date");
|
||||
if (urlDate) slider.value = daysFromDate(urlDate);
|
||||
slider.addEventListener("input", updateImages);
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue