Merge all the dist_alerts changes to production

app/main.py

@@ -42,6 +42,8 @@
 from .routes import preview
 
 from .routes.titiler import routes as titiler_routes
+from .routes.titiler.gfw_integrated_alerts import router as integrated_alerts_router
+from .routes.titiler.umd_glad_dist_alerts import router as dist_alerts_router
 
 gunicorn_logger = logging.getLogger("gunicorn.error")
 logger.handlers = gunicorn_logger.handlers
@@ -55,6 +57,8 @@
     burned_areas_tiles.router,
     dynamic_vector_tiles.router,
     vector_tiles.router,
+    integrated_alerts_router,
+    dist_alerts_router,
     umd_tree_cover_loss_raster_tiles.router,
     umd_glad_landsat_alerts_raster_tiles.router,
     umd_glad_sentinel2_alerts_raster_tiles.router,
@@ -79,10 +83,6 @@
 app.include_router(
     titiler_routes.mosaic.router, prefix="/cog/mosaic", tags=["Mosaic Tiles"]
 )
-app.include_router(
-    titiler_routes.custom.router, prefix="/cog/custom", tags=["Custom Tiles"]
-)
-
 
 #####################
 ## Middleware

app/models/enumerators/titiler.py

@@ -0,0 +1,17 @@
+from enum import Enum
+
+
+class AlertConfidence(str, Enum):
+    low = "low"
+    high = "high"
+
+
+class IntegratedAlertConfidence(str, Enum):
+    low = "low"
+    high = "high"
+    highest = "highest"
+
+
+class RenderType(str, Enum):
+    true_color = "true_color"
+    encoded = "encoded"

app/routes/titiler/algorithms/alerts.py

@@ -0,0 +1,175 @@
+from collections import OrderedDict, namedtuple
+from typing import Optional
+
+import numpy as np
+from fastapi.logger import logger
+from rio_tiler.models import ImageData
+from titiler.core.algorithm import BaseAlgorithm
+
+from app.models.enumerators.titiler import IntegratedAlertConfidence, RenderType
+
+Colors: namedtuple = namedtuple("Colors", ["red", "green", "blue"])
+AlertConfig: namedtuple = namedtuple("AlertConfig", ["confidence", "colors"])
+
+
+class Alerts(BaseAlgorithm):
+    """Decode Deforestation Alerts."""
+
+    title: str = "Deforestation Alerts"
+    description: str = "Decode and visualize alerts"
+
+    conf_colors: OrderedDict = OrderedDict(
+        {
+            IntegratedAlertConfidence.low: AlertConfig(
+                confidence=2, colors=Colors(237, 164, 194)
+            ),
+            IntegratedAlertConfidence.high: AlertConfig(
+                confidence=3, colors=Colors(220, 102, 153)
+            ),
+            IntegratedAlertConfidence.highest: AlertConfig(
+                confidence=4, colors=Colors(201, 42, 109)
+            ),
+        }
+    )
+
+    record_start_date: str = "2014-12-31"
+
+    start_date: Optional[str] = None
+    end_date: Optional[str] = None
+    alert_confidence: Optional[str] = None
+    render_type: RenderType = RenderType.true_color
+
+    # metadata
+    input_nbands: int = 2
+    output_nbands: int = 4
+    output_dtype: str = "uint8"
+
+    def __call__(self, img: ImageData) -> ImageData:
+        """Process the input image and decode deforestation or land disturbance
+        alert raster data into RGBA format.
+
+        Args:
+            img (ImageData): Input image data with alert date/confidence and intensity
+            (zoom-level visibility) layers.
+
+        Returns:
+            ImageData: Processed image with RGBA channels either with true colors ready for
+            visualization or encoding date and confidence for front-end processing.
+        """
+        date_conf_data = img.data[0]
+
+        self.intensity = img.data[1]
+        self.no_data = img.array.mask[0]
+        self.data_alert_confidence = date_conf_data // 10000
+        self.alert_date = date_conf_data % 10000
+
+        self.mask = self.create_mask()
+
+        if self.render_type == RenderType.true_color:
+            rgb = self.create_true_color_rgb()
+            alpha = self.create_true_color_alpha()
+        else:  # encoded
+            rgb = self.create_encoded_rgb()
+            alpha = self.create_encoded_alpha()
+
+        data = np.vstack([rgb, alpha[np.newaxis, ...]]).astype(self.output_dtype)
+        data = np.ma.MaskedArray(data, mask=False)
+
+        return ImageData(data, assets=img.assets, crs=img.crs, bounds=img.bounds)
+
+    def create_mask(self):
+        """Generate a mask for pixel visibility based on date and confidence
+        filters, and no data values.
+
+        Returns:
+            np.ndarray: A mask array pixels with no alert or alerts not meeting filter
+            condition are masked.
+        """
+
+        mask = ~self.no_data
+
+        if self.alert_confidence:
+            confidence_mask = (
+                self.data_alert_confidence
+                >= self.conf_colors[self.alert_confidence].confidence
+            )
+            mask *= confidence_mask
+
+        if self.start_date:
+            start_mask = self.alert_date >= (
+                np.datetime64(self.start_date) - np.datetime64(self.record_start_date)
+            )
+            mask *= start_mask
+
+        if self.end_date:
+            end_mask = self.alert_date <= (
+                np.datetime64(self.end_date) - np.datetime64(self.record_start_date)
+            )
+            mask *= end_mask
+
+        return mask
+
+    def create_true_color_rgb(self):
+        """Map alert confidence levels to RGB values for visualization.
+
+        Returns:
+            np.ndarray: A 3D array with RGB channels.
+        """
+        r, g, b = self._rgb_zeros_array()
+
+        for properties in self.conf_colors.values():
+            confidence = properties.confidence
+            colors = properties.colors
+            r[self.data_alert_confidence >= confidence] = colors.red
+            g[self.data_alert_confidence >= confidence] = colors.green
+            b[self.data_alert_confidence >= confidence] = colors.blue
+
+        return np.stack([r, g, b], axis=0)
+
+    def create_encoded_rgb(self):
+        """Encode the alert date and confidence into the RGB channels, allowing
+        interactive date filtering and color control on Flagship.
+
+        Returns:
+            np.ndarray: A 3D array with encoded RGB values.
+        """
+        r, g, b = self._rgb_zeros_array()
+        r = self.alert_date // 255
+        g = self.alert_date % 255
+        b = (self.data_alert_confidence // 3 + 1) * 100 + self.intensity
+
+        return np.stack([r, g, b], axis=0)
+
+    def create_true_color_alpha(self):
+        """Set the transparency (alpha) channel for alert pixels based on date,
+        confidence filters, and intensity input. The intensity multiplier is
+        used to control how isolated alerts fade out at low zoom levels,
+        matching the rendering behavior in Flagship.
+
+        Returns:
+            np.ndarray: Array representing the alpha (transparency) channel, where pixel
+            visibility is adjusted by intensity.
+        """
+        alpha = np.where(self.mask, self.intensity * 150, 0)
+        return np.minimum(255, alpha)
+
+    def create_encoded_alpha(self):
+        """Generate the alpha channel for encoded alerts. The default
+        implementation sets pixel visibility based on date/confidence filters
+        and intensity input. Can be overridden for specific alert types.
+
+        Returns:
+            np.ndarray: An array representing the alpha channel.
+        """
+        logger.info(
+            """Encoded alpha not provided, returning alpha
+                    from input layer and date/confidence mask."""
+        )
+        return self.create_true_color_alpha()
+
+    def _rgb_zeros_array(self):
+        r = np.zeros_like(self.data_alert_confidence, dtype=np.uint8)
+        g = np.zeros_like(self.data_alert_confidence, dtype=np.uint8)
+        b = np.zeros_like(self.data_alert_confidence, dtype=np.uint8)
+
+        return r, g, b

app/routes/titiler/algorithms/dist_alerts.py

@@ -0,0 +1,66 @@
+from collections import OrderedDict
+from typing import Optional
+
+from pydantic import ConfigDict
+from rio_tiler.models import ImageData
+
+from app.models.enumerators.titiler import AlertConfidence
+
+from .alerts import AlertConfig, Alerts, Colors
+
+
+class DISTAlerts(Alerts):
+    title: str = "Land Disturbance (DIST) Alerts"
+    description: str = "Decode and visualize DIST alerts"
+
+    model_config = ConfigDict(arbitrary_types_allowed=True)
+    conf_colors: OrderedDict = OrderedDict(
+        {
+            AlertConfidence.low: AlertConfig(
+                confidence=2, colors=Colors(237, 164, 194)
+            ),
+            AlertConfidence.high: AlertConfig(
+                confidence=3, colors=Colors(220, 102, 153)
+            ),
+        }
+    )
+
+    record_start_date: str = "2020-12-31"
+
+    tree_cover_density_mask: Optional[int] = None
+    tree_cover_density_data: Optional[ImageData] = None
+
+    tree_cover_height_mask: Optional[int] = None
+    tree_cover_height_data: Optional[ImageData] = None
+
+    # the highest loss year that is used to exclude alerts for
+    # the purpose of showing only alerts in forests
+    tree_cover_loss_mask: Optional[int] = None
+    tree_cover_loss_data: Optional[ImageData] = None
+
+    def create_mask(self):
+        mask = super().create_mask()
+
+        if self.tree_cover_density_mask:
+            mask *= (
+                self.tree_cover_density_data.array[0, :, :]
+                >= self.tree_cover_density_mask
+            )
+
+        if self.tree_cover_height_mask:
+            mask *= (
+                self.tree_cover_height_data.array[0, :, :]
+                >= self.tree_cover_height_mask
+            )
+
+        if self.tree_cover_loss_mask:
+            # Tree cover loss data before 2020 can't be used to filter out pixels as not forest.
+            # Instead, we use tree cover height taken that year as source of truth.
+            # For example, if a pixel had tree cover loss in 2018, but has tree cover
+            # height (2020) that meets the forest threshold, the pixel meets
+            # the forest criteria for alerts and is not masked out.
+            mask *= (
+                self.tree_cover_loss_data.array[0, :, :] > self.tree_cover_loss_mask
+            ) | (self.tree_cover_loss_data.array[0, :, :] <= 2020)
+
+        return mask