update tifs

boem_conf/classification_model/USGS.yaml

@@ -11,7 +11,7 @@ batch_size: 96
 workers: 8
 expand: 30
 balance_classes: false
-use_metadata: false
+use_metadata: true
 metadata_dim: 32
 metadata_dropout: 0.5
 # metadata_dir defaults to report.metadata_dir when use_metadata is true.

scripts/USGS_classification.py

@@ -38,6 +38,17 @@
 # Crop image_path is like C1_L6_F560_T20241219_173703_737_23.png -> parent stem is C1_L6_F560_T20241219_173703_737
 _CROP_SUFFIX_RE = re.compile(r"^(.+)_\d+\.(png|PNG|jpg|JPG|jpeg|JPEG)$")
 
+# New-format UBFAI image names embed the flight datetime as T{YYYYMMDD}_{HHMMSS}.
+# This maps directly to a captures CSV key (e.g. 20241219_165719_captures.csv).
+# Old-format names (e.g. 668140-0806171338187-CAM3.png) have no parseable datetime.
+_FLIGHT_DATETIME_RE = re.compile(r"T(\d{8}_\d{6})")
+
+
+def _flight_name_from_image_path(image_path: str) -> str | None:
+    """Extract flight datetime key from new-format UBFAI image paths, or None."""
+    m = _FLIGHT_DATETIME_RE.search(os.path.basename(str(image_path)))
+    return m.group(1) if m else None
+
 # Detection-task combined splits: exclude so we only load per-image CSVs (avoids duplicate rows and Object vs species label conflicts).
 UBFAI_CROPS_EXCLUDE_CSV = frozenset({"train.csv", "test.csv", "zero_shot.csv"})
 UBFAI_CROPS_EXCLUDE_PREFIX = "train_max_empty_"  # e.g. train_max_empty_0_10.csv
@@ -322,6 +333,25 @@ def main(cfg: DictConfig):
     balance_classes = bool(cfg.classification_model.get("balance_classes", False))
     comet_logger.experiment.log_parameter("balance_classes", balance_classes)
 
+    use_metadata = bool(cfg.classification_model.get("use_metadata", False))
+    metadata_dir = cfg.classification_model.get("metadata_dir", None) or cfg.report.get("metadata_dir", None)
+    metadata_dim = int(cfg.classification_model.get("metadata_dim", 32))
+    metadata_dropout = float(cfg.classification_model.get("metadata_dropout", 0.5))
+    comet_logger.experiment.log_parameter("use_metadata", use_metadata)
+    if use_metadata:
+        comet_logger.experiment.log_parameter("metadata_dir", metadata_dir)
+        comet_logger.experiment.log_parameter("metadata_dim", metadata_dim)
+        comet_logger.experiment.log_parameter("metadata_dropout", metadata_dropout)
+        # UBFAI crops span many flights in one directory. Extract per-row flight_name
+        # from the T{YYYYMMDD}_{HHMMSS} pattern in new-format image paths so
+        # build_crop_metadata_rows can look up the right captures CSV for each image.
+        # Old-format images (no T-datetime pattern) get None and are skipped silently.
+        for df in (train_df, validation_df):
+            df["flight_name"] = df["image_path"].map(_flight_name_from_image_path)
+        n_with = int(train_df["flight_name"].notna().sum())
+        n_without = int(train_df["flight_name"].isna().sum())
+        print(f"[metadata] flight_name extracted: {n_with} train crops matched, {n_without} unmatched (old-format, will be skipped)")
+
     trained_model = preprocess_and_train(
         train_df=train_df,
         validation_df=validation_df,
@@ -338,6 +368,10 @@ def main(cfg: DictConfig):
         batch_size=cfg.classification_model.batch_size,
         workers=cfg.classification_model.workers,
         balance_classes=balance_classes,
+        use_metadata=use_metadata,
+        metadata_dir=metadata_dir,
+        metadata_dim=metadata_dim,
+        metadata_dropout=metadata_dropout,
     )
 
 

scripts/visualize_metadata_priors.py

@@ -32,6 +32,31 @@
 except ImportError:  # pragma: no cover - contextily is an optional visual enhancement.
     ctx = None
 
+try:
+    import geopandas as gpd
+except ImportError:
+    gpd = None
+
+# Natural Earth 110m countries — small download, cached after first call.
+_COUNTRIES_URL = "https://naciscdn.org/naturalearth/110m/cultural/ne_110m_admin_0_countries.zip"
+_BOUNDARY_CACHE: "gpd.GeoDataFrame | None" = None
+
+
+def load_land_boundaries() -> "gpd.GeoDataFrame | None":
+    """Return a GeoDataFrame of USA + Canada + Mexico boundaries, or None."""
+    global _BOUNDARY_CACHE
+    if gpd is None:
+        return None
+    if _BOUNDARY_CACHE is not None:
+        return _BOUNDARY_CACHE
+    try:
+        world = gpd.read_file(_COUNTRIES_URL)
+        _BOUNDARY_CACHE = world[world["SOVEREIGNT"].isin(["United States of America", "Canada", "Mexico"])]
+        return _BOUNDARY_CACHE
+    except Exception as exc:
+        print(f"Could not load land boundaries: {exc}")
+        return None
+
 
 SPECIES_ALIASES = {
     "Northern Gannet": "Morus bassanus",
@@ -189,10 +214,17 @@ def plot_species_map(
         vmax=1 if plot_column == "relative_score" else None,
     )
     fig.colorbar(image, ax=ax, label=plot_column.replace("_", " "))
+
+    boundaries = load_land_boundaries()
+    if boundaries is not None:
+        boundaries.boundary.plot(ax=ax, color="black", linewidth=0.8, zorder=3)
+
     ax.set_title(f"{species} metadata prior, {date}")
     ax.set_xlabel("Longitude")
     ax.set_ylabel("Latitude")
     ax.grid(color="white", linewidth=0.3, alpha=0.4)
+    ax.set_xlim(min_lon, max_lon)
+    ax.set_ylim(min_lat, max_lat)
     fig.savefig(output_path, dpi=250, bbox_inches="tight")
     plt.close(fig)
 
@@ -269,6 +301,7 @@ def main() -> None:
     species = resolve_species(args.species)
     grid = make_grid(tuple(args.bounds), args.cell_degrees)
     model = load_metadata_model(args.checkpoint, args.device)
+    load_land_boundaries()  # warm cache once before the plotting loop
 
     all_scores = []
     for date in args.dates:

src/classification.py

@@ -208,7 +208,12 @@ def preprocess_and_train(
             ignore_index=True,
         ).drop_duplicates(subset=["filename"])
         if metadata_rows.empty:
-            raise ValueError(f"No crop metadata rows were created for flight {flight_name}")
+            print(
+                f"[preprocess_and_train] WARNING: no crop metadata rows found for any image "
+                f"(default flight_name={flight_name!r}). Disabling metadata for this run."
+            )
+            use_metadata = False
+            metadata_csv = None
         metadata_csv = os.path.join(checkpoint_dir, "classification_crop_metadata.csv")
         os.makedirs(checkpoint_dir, exist_ok=True)
         metadata_rows.to_csv(metadata_csv, index=False)

src/pipeline.py

@@ -23,6 +23,13 @@
 import random
 import tempfile
 
+_IMAGE_EXTS = ("jpg", "JPG", "jpeg", "JPEG", "tif", "TIF", "tiff", "TIFF")
+
+
+def _collect_images(image_dir: str) -> list[str]:
+    return [p for ext in _IMAGE_EXTS for p in glob.glob(os.path.join(image_dir, f"*.{ext}"))]
+
+
 class Pipeline:
     """Pipeline for training and evaluating a detection and classification model"""
     def __init__(self, cfg: DictConfig):
@@ -33,7 +40,7 @@ def __init__(self, cfg: DictConfig):
         self.annotator = get_annotator(self.config)
 
         # Pool of all images
-        self.all_images = glob.glob(os.path.join(self.config.image_dir, "*.jpg")) + glob.glob(os.path.join(self.config.image_dir, "*.JPG"))
+        self.all_images = _collect_images(self.config.image_dir)
 
         self.comet_logger = CometLogger(project_name=self.config.comet.project, workspace=self.config.comet.workspace)
         self.comet_logger.experiment.add_tag("pipeline")
@@ -388,7 +395,7 @@ def run(self):
         else:
             raise NotImplementedError("Only deepforest classification backend is currently implemented")
 
-        pool = glob.glob(os.path.join(self.config.image_dir, "*.jpg")) + glob.glob(os.path.join(self.config.image_dir, "*.JPG"))
+        pool = _collect_images(self.config.image_dir)
         pool = [image for image in pool if os.path.basename(image) not in self.existing_images]
         pool_limit = getattr(self.config.active_learning, "pool_limit", None)
         print(f"Pool: {len(pool)} images (after excluding existing), pool_limit={pool_limit}")

src/spatiotemporal_metadata.py

@@ -24,9 +24,16 @@ def _image_stem(image_path: str) -> str:
 
 
 def load_flight_metadata(flight_name: str, metadata_dir: str | Path) -> dict[str, dict]:
-    """Return image-stem keyed metadata dicts for one flight."""
+    """Return image-stem keyed metadata dicts for one flight.
+
+    Returns an empty dict (rather than raising) when no captures CSV exists for
+    the flight — this happens for old-format UBFAI images whose filenames don't
+    encode a parseable flight datetime.
+    """
     metadata_dir = Path(metadata_dir)
     captures_path = metadata_dir / f"{flight_datetime_key(flight_name)}_captures.csv"
+    if not captures_path.exists():
+        return {}
     captures = pd.read_csv(captures_path)
     required = {"Basename", "Lat", "Lon"}
     missing = required - set(captures.columns)

src/visualization.py

@@ -181,7 +181,8 @@ def crop_images(annotations, root_dir, experiment=None, expand=30):
     return crop_image_paths
 
 def select_images_for_video(image_dir, thin_factor):
-    all_images = glob.glob(image_dir + "/*.jpg")
+    exts = ("jpg", "JPG", "jpeg", "JPEG", "tif", "TIF", "tiff", "TIFF")
+    all_images = [p for ext in exts for p in glob.glob(os.path.join(image_dir, f"*.{ext}"))]
     # Thin by factor, select every nth image
     thinned_images = all_images[::thin_factor]
     return thinned_images