Merge branch 'staging' into dev

app/routes/titiler/umd_glad_dist_alerts.py

@@ -18,8 +18,7 @@
 
 router = APIRouter()
 
-# TODO: update to the actual dataset when ready
-DATASET = "dan_test"
+DATASET = "umd_glad_dist_alerts"
 
 today = date.today()
 
@@ -30,8 +29,16 @@
     tags=["Raster Tiles"],
     response_description="PNG Raster Tile",
 )
+@router.get(
+    "/{dataset}/{version}/titiler/{z}/{x}/{y}.png",  # for testing datasets - hidden from docs.
+    response_class=Response,
+    tags=["Raster Tiles"],
+    response_description="PNG Raster Tile",
+    include_in_schema=False,
+)
 async def glad_dist_alerts_raster_tile(
     *,
+    dataset: str = DATASET,
     version,
     xyz: Tuple[int, int, int] = Depends(raster_xyz),
     start_date: Optional[str] = Query(
@@ -45,33 +52,47 @@ async def glad_dist_alerts_raster_tile(
         description="Only show alerts until given date.",
     ),
     render_type: RenderType = Query(
-        RenderType.encoded, description="Render true color or encoded tiles"
+        RenderType.encoded,
+        description=(
+            "Render true color or encoded tiles. Encoded tiles have the alert "
+            "date, confidence and intensity (value for use in alpha/transparency channel to fade out isolated alert pixels at low zoom levels) "
+            "packed in the image RGB channels for front-end interactive use, "
+            "such as date filtering with supported technologies. "
+            "Decoding instructions: Alert Date is calculated as `red * 255 + green`, "
+            "representing days since 2020-12-31. Confidence is calculated as `floor(blue / 100)`, "
+            "with values of `2` (high) or `1` (low). Intensity is calculated as `mod(blue, 100)`, "
+            "with a maximum value of `55`. "
+            "For example, a pixel RGB value of `(3, 26, 255)` would decode to: "
+            "**alert date**: `3 * 255 + 26 = 791` (or 2023-03-02), "
+            "**confidence**: `floor(255 / 100) = 2` (high), and "
+            "**intensity**: `mod(255, 100) = 55`"
+        ),
     ),
     alert_confidence: Optional[AlertConfidence] = Query(
         AlertConfidence.low,
         description="Show alerts that are at least of this confidence level",
     ),
-    tree_cover_density: Optional[int] = Query(
+    tree_cover_density_threshold: Optional[int] = Query(
         None,
         ge=0,
         le=100,
-        description="Alerts in pixels with tree cover density (in percent) below this threshold won't be displayed. `umd_tree_cover_density_2010` is used for this masking.",
+        description="Show alerts in pixels with tree cover density (in percent) greater than or equal to this threshold. `umd_tree_cover_density_2010` is used for this masking.",
     ),
-    tree_cover_height: Optional[int] = Query(
+    tree_cover_height_threshold: Optional[int] = Query(
         None,
-        description="Alerts in pixels with tree cover height (in meters) below this threshold won't be displayed. `umd_tree_cover_height_2020` dataset in the API is used for this masking.",
+        description="Show alerts in pixels with tree cover height (in meters) greater than or equal to this threshold. `umd_tree_cover_height_2020` dataset in the API is used for this masking.",
     ),
-    tree_cover_loss_cutoff: bool = Query(
-        False,
+    tree_cover_loss_threshold: Optional[int] = Query(
+        None,
         ge=2021,
-        description="""This filter is to be used in conjunction with `tree_cover_density` and `tree_cover_height` filters to detect only alerts in forests, by masking out pixels that have had tree cover loss prior to the alert.""",
+        description="""This filter is to be used in conjunction with `tree_cover_density_threshold` and `tree_cover_height_threshold` filters to detect only alerts in forests, by masking out pixels that have had tree cover loss prior to the alert.""",
     ),
 ) -> Response:
     """UMD GLAD DIST alerts raster tiles."""
 
     tile_x, tile_y, zoom = xyz
     bands = ["default", "intensity"]
-    folder: str = f"s3://{DATA_LAKE_BUCKET}/{DATASET}/{version}/raster/epsg-4326/cog"
+    folder: str = f"s3://{DATA_LAKE_BUCKET}/{dataset}/{version}/raster/epsg-4326/cog"
     with AlertsReader(input=folder) as reader:
         # NOTE: the bands in the output `image_data` array will be in the order of
         # the input `bands` list
@@ -82,30 +103,30 @@ async def glad_dist_alerts_raster_tile(
         end_date=end_date,
         render_type=render_type,
         alert_confidence=alert_confidence,
-        tree_cover_density_mask=tree_cover_density,
-        tree_cover_height_mask=tree_cover_height,
-        tree_cover_loss_mask=tree_cover_loss_cutoff,
+        tree_cover_density_mask=tree_cover_density_threshold,
+        tree_cover_height_mask=tree_cover_height_threshold,
+        tree_cover_loss_mask=tree_cover_loss_threshold,
     )
 
     filter_datasets = GLOBALS.dist_alerts_forest_filters
-    if tree_cover_density:
-        dataset = filter_datasets["tree_cover_density"]
+    if tree_cover_density_threshold:
+        filter_dataset = filter_datasets["tree_cover_density"]
         with COGReader(
-            f"s3://{DATA_LAKE_BUCKET}/{dataset['dataset']}/{dataset['version']}/raster/epsg-4326/cog/default.tif"
+            f"s3://{DATA_LAKE_BUCKET}/{filter_dataset['dataset']}/{filter_dataset['version']}/raster/epsg-4326/cog/default.tif"
         ) as reader:
             dist_alert.tree_cover_density_data = reader.tile(tile_x, tile_y, zoom)
 
-    if tree_cover_height:
-        dataset = filter_datasets["tree_cover_height"]
+    if tree_cover_height_threshold:
+        filter_dataset = filter_datasets["tree_cover_height"]
         with COGReader(
-            f"s3://{DATA_LAKE_BUCKET}/{dataset['dataset']}/{dataset['version']}/raster/epsg-4326/cog/default.tif"
+            f"s3://{DATA_LAKE_BUCKET}/{filter_dataset['dataset']}/{filter_dataset['version']}/raster/epsg-4326/cog/default.tif"
         ) as reader:
             dist_alert.tree_cover_height_data = reader.tile(tile_x, tile_y, zoom)
 
-    if tree_cover_loss_cutoff:
-        dataset = filter_datasets["tree_cover_loss"]
+    if tree_cover_loss_threshold:
+        filter_dataset = filter_datasets["tree_cover_loss"]
         with COGReader(
-            f"s3://{DATA_LAKE_BUCKET}/{dataset['dataset']}/{dataset['version']}/raster/epsg-4326/cog/default.tif"
+            f"s3://{DATA_LAKE_BUCKET}/{filter_dataset['dataset']}/{filter_dataset['version']}/raster/epsg-4326/cog/default.tif"
         ) as reader:
             dist_alert.tree_cover_loss_data = reader.tile(tile_x, tile_y, zoom)
 

terraform/modules/content_delivery_network/main.tf

@@ -423,7 +423,7 @@ resource "aws_cloudfront_distribution" "tiles" {
     }
   }
 
-  # send all Titiler requests to tile cache app
+  # send all generic Titiler requests to tile cache app
   ordered_cache_behavior {
     allowed_methods        = local.methods
     cached_methods         = local.methods
@@ -455,6 +455,32 @@ resource "aws_cloudfront_distribution" "tiles" {
     }
   }
 
+# pass requests for DIST alerts test datasets to tile cache app
+  ordered_cache_behavior {
+    allowed_methods        = local.methods
+    cached_methods         = local.methods
+    target_origin_id       = "dynamic"
+    compress               = true
+    path_pattern           = "*/titiler/*"
+    default_ttl            = 86400
+    max_ttl                = 86400
+    min_ttl                = 0
+    smooth_streaming       = false
+    trusted_signers        = []
+    viewer_protocol_policy = "redirect-to-https"
+
+    forwarded_values {
+      headers                 = local.headers
+      query_string            = true
+      query_string_cache_keys = []
+
+      cookies {
+        forward           = "none"
+        whitelisted_names = []
+      }
+    }
+  }
+
   # Default static vector tiles are stored on S3
   # They won't change and can stay in cache for a year
   # We will set response headers for selected tile caches in S3 if required