feat: add governed hydrology and historical imagery

backend/README.md

@@ -1121,16 +1121,40 @@ is never presented as a complete result.
 
 ## Bounded official orthophoto acquisition
 
-`POST /api/v1/projects/{project_id}/datasets/orthophoto/acquire` accepts an
-explicit EPSG:4326 map rectangle and stores the official Digitaal Vlaanderen
-`OMWRGBMRVL`/`Ortho` response as a canonical EPSG:31370 raster Dataset. The
+`GET /api/v1/projects/{project_id}/datasets/orthophoto/products` lists the
+governed product allowlist. `POST .../datasets/orthophoto/acquire` accepts an
+explicit EPSG:4326 map rectangle plus `product_key` and stores the official
+Digitaal Vlaanderen WMS response as a canonical EPSG:31370 raster Dataset. The
 default safety envelope is 128-1,024 m per side, 1 m/pixel, 32 MiB and a
 24-hour exact-request cache. It runs synchronously behind the existing Job
 abstraction and never during startup.
 
+Available products cover the most recent winter image, annual winter mosaics
+for 2012-2025, three older winter periods, RGB 1979-1990 and panchromatic 1971.
+Historical products persist validity metadata and are deliberately excluded
+from configured-YOLO/current-GRB QA. `GET .../datasets/{dataset_id}/raster/image`
+is the constrained binary PNG endpoint used by the MapLibre image overlay.
+
 Settings: `ORTHOPHOTO_ENABLED`, `ORTHOPHOTO_WMS_URL`,
 `ORTHOPHOTO_WMS_LAYER`, `ORTHOPHOTO_RESOLUTION_M`,
 `ORTHOPHOTO_MIN_SIDE_M`, `ORTHOPHOTO_MAX_SIDE_M`,
 `ORTHOPHOTO_TIMEOUT_SECONDS`, `ORTHOPHOTO_MAX_RESPONSE_MB` and
 `ORTHOPHOTO_CACHE_TTL_HOURS`. Keep the official HTTPS URL and 1 m profile
 unless a separately verified deployment/model profile requires a change.
+
+## Waterinfo station histories
+
+Run the explicit operator after the regional workspace and Mol Area exist:
+
+```bash
+docker exec geointel python /app/scripts/provision_waterinfo_station_history.py \
+  --project-name "Kempen Regional Workbench" \
+  --area-name "Gemeente Mol" \
+  --from-year 2013 --to-year 2025
+```
+
+The command retains raw KiWIS JSON/checksums and imports only real annual
+observations through the canonical dataset upload API. Every station has its
+own temporal-series key. Water levels and discharges remain Point measurements;
+they are never averaged across stations or presented as municipal water volume.
+Use `--fetch-only` to prepare and audit artifacts without persistence.

backend/app/api/routes/datasets.py

@@ -6,10 +6,10 @@ from typing import Any
 from uuid import UUID
 from uuid import UUID as _UUID
 
-from fastapi import APIRouter, Depends, File, Form, HTTPException, Query
+from fastapi import APIRouter, Depends, File, Form, HTTPException, Query, Response
 from fastapi import UploadFile
 from sqlalchemy.orm import Session
-from app.models import Area
+from app.models import Area, Project
 
 from app.core.errors import AppError
 from app.db.session import get_db
@@ -143,6 +143,14 @@ def acquire_bounded_orthophoto(
     return envelope(job)
 
 
+@router.get("/datasets/orthophoto/products", response_model=dict)
+def list_orthophoto_products(project_id: UUID, db: Session = Depends(get_db)):
+    if not db.get(Project, project_id):
+        raise AppError(code="PROJECT_NOT_FOUND", message="Project not found", status_code=404)
+    items = OrthophotoAcquisitionService.list_products()
+    return envelope({"items": items, "total": len(items)})
+
+
 @router.get("/datasets", response_model=dict)
 def list_datasets(
     project_id: UUID,
@@ -426,6 +434,20 @@ def raster_preview_readiness(
     return envelope(RasterOperationsService.preview(db, dataset_id))
 
 
+@router.get("/datasets/{dataset_id}/raster/image")
+def raster_orthophoto_image(
+    project_id: UUID,
+    dataset_id: UUID,
+    db: Session = Depends(get_db),
+):
+    content = OrthophotoAcquisitionService.render_png(db, project_id, dataset_id)
+    return Response(
+        content=content,
+        media_type="image/png",
+        headers={"Cache-Control": "private, max-age=86400"},
+    )
+
+
 @router.get("/datasets/{dataset_id}/raster/stats", response_model=dict)
 def raster_stats(
     project_id: UUID,

backend/app/schemas/__init__.py

@@ -32,7 +32,7 @@ from .segmentation import (
 )
 from .health import HealthResponse, SystemCapabilities
 from .job import JobCreate, JobList, JobRead, JobStatus
-from .orthophoto import OrthophotoAcquireRequest, OrthophotoAcquisitionResult
+from .orthophoto import OrthophotoAcquireRequest, OrthophotoAcquisitionResult, OrthophotoProductRead
 from .external import (
     ExternalFetchRequest,
     ExternalFetchResponse,
@@ -134,6 +134,7 @@ __all__ = [
     "JobStatus",
     "OrthophotoAcquireRequest",
     "OrthophotoAcquisitionResult",
+    "OrthophotoProductRead",
     "VectorBBoxResponse",
     "VectorClipRequest",
     "VectorBufferRequest",

backend/app/schemas/orthophoto.py

@@ -10,13 +10,31 @@ from .operations import VectorSelectionBBox
 class OrthophotoAcquireRequest(BaseModel):
     bbox: VectorSelectionBBox
     area_id: UUID | None = None
+    product_key: str = "most_recent"
     force_refresh: bool = False
 
 
+class OrthophotoProductRead(BaseModel):
+    key: str
+    display_name: str
+    observation_label: str
+    temporal_granularity: str
+    native_resolution_m: float
+    supports_detection: bool
+    color_mode: str
+    catalog_url: str
+    limitation_message: str
+
+
 class OrthophotoAcquisitionResult(BaseModel):
     output_dataset_id: UUID
     reused: bool
     provider: str
+    product_key: str
+    display_name: str
+    observation_label: str
+    temporal_granularity: str
+    supports_detection: bool
     layer: str
     width: int
     height: int

backend/app/services/dataset_service.py

@@ -422,6 +422,11 @@ class DatasetService:
         source_metadata: dict[str, Any],
         provenance_metadata: dict[str, Any],
         area_id: UUID | None = None,
+        temporal_series_key: str | None = None,
+        observed_at: datetime | None = None,
+        valid_from: datetime | None = None,
+        valid_to: datetime | None = None,
+        temporal_granularity: str | None = None,
         source_version: str | None = None,
         content_type: str = "image/tiff",
     ) -> DatasetCreateResponse:
@@ -438,6 +443,14 @@ class DatasetService:
         safe_filename = DatasetService._validate_upload_filename(filename)
         if DatasetService._extension_for_path(safe_filename) not in DatasetService.RASTER_EXTENSIONS:
             raise AppError(code="INVALID_UPLOAD", message="Raster artifacts require a GeoTIFF filename", status_code=415)
+        temporal = DatasetService._validate_temporal_metadata(
+            temporal_series_key=temporal_series_key,
+            observed_at=observed_at,
+            valid_from=valid_from,
+            valid_to=valid_to,
+            temporal_granularity=temporal_granularity,
+            source_version=source_version,
+        )
 
         dataset_id = uuid.uuid4()
         storage_info = StorageService.persist_dataset_file(
@@ -462,7 +475,7 @@ class DatasetService:
                 source_metadata=source_metadata,
                 provenance_metadata=provenance_metadata,
                 imported_at=datetime.now(timezone.utc),
-                source_version=source_version,
+                **temporal,
                 storage_path=storage_info["storage_path"],
                 original_filename=storage_info["original_filename"],
                 stored_filename=storage_info["stored_filename"],
@@ -483,6 +496,9 @@ class DatasetService:
                     version=1,
                     storage_path=dataset.storage_path,
                     source_version=dataset.source_version,
+                    observed_at=dataset.observed_at,
+                    valid_from=dataset.valid_from,
+                    valid_to=dataset.valid_to,
                     checksum_sha256=dataset.checksum_sha256,
                     source_metadata=dataset.source_metadata,
                     provenance_metadata=dataset.provenance_metadata,

backend/app/services/geo_assistant_service.py

@@ -349,6 +349,7 @@ class GeoAssistantService:
                             "measurement_quality": (
                                 "schatting" if summary["is_estimate"] else "exact_binnen_bronrepresentatie"
                             ),
+                            "warning": summary.get("warning"),
                         }
                     )
                     if dataset.id not in source_dataset_ids:

backend/app/services/orthophoto_acquisition_service.py

@@ -1,9 +1,11 @@
 from __future__ import annotations
 
 import hashlib
+import io
 import json
 import math
 import warnings
+from dataclasses import dataclass
 from datetime import UTC, datetime, timedelta
 from pathlib import Path
 from typing import Any, Callable
@@ -20,21 +22,169 @@ from shapely.ops import transform as shapely_transform
 from app.core.config import Settings, get_settings
 from app.core.errors import AppError
 from app.models import Area, Dataset, Project
-from app.schemas.orthophoto import OrthophotoAcquireRequest, OrthophotoAcquisitionResult
+from app.schemas.orthophoto import OrthophotoAcquireRequest, OrthophotoAcquisitionResult, OrthophotoProductRead
 from app.services.dataset_service import DatasetService
 
 
+@dataclass(frozen=True)
+class OrthophotoProduct:
+    key: str
+    display_name: str
+    observation_label: str
+    temporal_granularity: str
+    native_resolution_m: float
+    wms_url: str
+    layer: str
+    catalog_url: str
+    limitation_message: str
+    supports_detection: bool = False
+    color_mode: str = "rgb"
+    observed_at: datetime | None = None
+    valid_from: datetime | None = None
+    valid_to: datetime | None = None
+
+
 class OrthophotoAcquisitionService:
     PROVIDER = "digitaal_vlaanderen_orthophoto"
     ATTRIBUTION = "Bron: Orthofotomozaiek Vlaanderen, Digitaal Vlaanderen"
     CATALOG_URL = "https://www.vlaanderen.be/datavindplaats/catalogus/orthofotomozaiek-middenschalig-winteropnamen-kleur-meest-recent-vlaanderen"
     LIMITATION = "Meest recente samengestelde winterorthofoto op het moment van de aanvraag; geen historische opnamedatum per pixel."
+    HISTORICAL_WINTER_WMS_URL = "https://geo.api.vlaanderen.be/OMW/wms"
+    HISTORICAL_WINTER_CATALOG_URL = "https://www.vlaanderen.be/datavindplaats/catalogus/wmts-orthofotomozaiek-middenschalig-winteropnamen"
+    HISTORICAL_SUMMER_WMS_URL = "https://geo.api.vlaanderen.be/OKZ/wms"
+    HISTORICAL_SUMMER_CATALOG_URL = "https://www.vlaanderen.be/datavindplaats/catalogus/orthofotomozaiek-kleinschalig-zomeropnamen"
+
+    @staticmethod
+    def _products(settings: Settings) -> dict[str, OrthophotoProduct]:
+        products: list[OrthophotoProduct] = [
+            OrthophotoProduct(
+                key="most_recent",
+                display_name="Meest recente winterluchtbeeld",
+                observation_label="Meest recent beschikbaar",
+                temporal_granularity="snapshot",
+                native_resolution_m=0.15,
+                wms_url=settings.orthophoto_wms_url,
+                layer=settings.orthophoto_wms_layer,
+                catalog_url=OrthophotoAcquisitionService.CATALOG_URL,
+                limitation_message=OrthophotoAcquisitionService.LIMITATION,
+                supports_detection=True,
+            )
+        ]
+        for year in range(2025, 2011, -1):
+            products.append(
+                OrthophotoProduct(
+                    key=str(year),
+                    display_name=f"Winterluchtbeeld {year}",
+                    observation_label=str(year),
+                    temporal_granularity="year",
+                    native_resolution_m=0.15 if year >= 2022 else 0.25,
+                    wms_url=OrthophotoAcquisitionService.HISTORICAL_WINTER_WMS_URL,
+                    layer=f"OMWRGB{year % 100:02d}VL",
+                    catalog_url=OrthophotoAcquisitionService.HISTORICAL_WINTER_CATALOG_URL,
+                    limitation_message=(
+                        "Officiële samengestelde winterorthofoto voor deze jaargang; de exacte opnamedatum kan per tegel verschillen. "
+                        "Historische beelden worden niet met de actuele GRB-toestand gevalideerd."
+                    ),
+                    observed_at=datetime(year, 1, 1, tzinfo=UTC),
+                    valid_from=datetime(year, 1, 1, tzinfo=UTC),
+                    valid_to=datetime(year, 12, 31, 23, 59, 59, tzinfo=UTC),
+                )
+            )
+        for key, start_year, end_year, layer in (
+            ("2008_2011", 2008, 2011, "OMWRGB08_11VL"),
+            ("2005_2007", 2005, 2007, "OMWRGB05_07VL"),
+            ("2000_2003", 2000, 2003, "OMWRGB00_03VL"),
+        ):
+            products.append(
+                OrthophotoProduct(
+                    key=key,
+                    display_name=f"Winterluchtbeeld {start_year}-{end_year}",
+                    observation_label=f"{start_year}-{end_year}",
+                    temporal_granularity="period",
+                    native_resolution_m=0.25,
+                    wms_url=OrthophotoAcquisitionService.HISTORICAL_WINTER_WMS_URL,
+                    layer=layer,
+                    catalog_url=OrthophotoAcquisitionService.HISTORICAL_WINTER_CATALOG_URL,
+                    limitation_message=(
+                        "Officiële samengestelde winterorthofoto uit een meerjarige opnameperiode; dit is geen exacte jaaropname. "
+                        "Historische beelden worden niet met de actuele GRB-toestand gevalideerd."
+                    ),
+                    observed_at=datetime(start_year, 1, 1, tzinfo=UTC),
+                    valid_from=datetime(start_year, 1, 1, tzinfo=UTC),
+                    valid_to=datetime(end_year, 12, 31, 23, 59, 59, tzinfo=UTC),
+                )
+            )
+        products.extend(
+            [
+                OrthophotoProduct(
+                    key="1979_1990",
+                    display_name="Zomerluchtbeeld 1979-1990",
+                    observation_label="1979-1990",
+                    temporal_granularity="period",
+                    native_resolution_m=1.0,
+                    wms_url=OrthophotoAcquisitionService.HISTORICAL_SUMMER_WMS_URL,
+                    layer="OKZRGB79_90VL",
+                    catalog_url=OrthophotoAcquisitionService.HISTORICAL_SUMMER_CATALOG_URL,
+                    limitation_message="Kleinschalig RGB-mozaïek uit meerdere zomervluchten tussen 1979 en 1990; geen exacte jaartoestand.",
+                    observed_at=datetime(1979, 1, 1, tzinfo=UTC),
+                    valid_from=datetime(1979, 1, 1, tzinfo=UTC),
+                    valid_to=datetime(1990, 12, 31, 23, 59, 59, tzinfo=UTC),
+                ),
+                OrthophotoProduct(
+                    key="1971",
+                    display_name="Zomerluchtbeeld 1971",
+                    observation_label="1971",
+                    temporal_granularity="year",
+                    native_resolution_m=1.0,
+                    wms_url=OrthophotoAcquisitionService.HISTORICAL_SUMMER_WMS_URL,
+                    layer="OKZPAN71VL",
+                    catalog_url=OrthophotoAcquisitionService.HISTORICAL_SUMMER_CATALOG_URL,
+                    limitation_message="Kleinschalig panchromatisch mozaïek uit 1971; zwart-wit en niet geschikt voor het huidige RGB-detectiemodel.",
+                    color_mode="panchromatic",
+                    observed_at=datetime(1971, 1, 1, tzinfo=UTC),
+                    valid_from=datetime(1971, 1, 1, tzinfo=UTC),
+                    valid_to=datetime(1971, 12, 31, 23, 59, 59, tzinfo=UTC),
+                ),
+            ]
+        )
+        return {product.key: product for product in products}
+
+    @staticmethod
+    def list_products(settings: Settings | None = None) -> list[dict[str, Any]]:
+        resolved_settings = settings or get_settings()
+        return [
+            OrthophotoProductRead(
+                key=product.key,
+                display_name=product.display_name,
+                observation_label=product.observation_label,
+                temporal_granularity=product.temporal_granularity,
+                native_resolution_m=product.native_resolution_m,
+                supports_detection=product.supports_detection,
+                color_mode=product.color_mode,
+                catalog_url=product.catalog_url,
+                limitation_message=product.limitation_message,
+            ).model_dump()
+            for product in OrthophotoAcquisitionService._products(resolved_settings).values()
+        ]
+
+    @staticmethod
+    def _product(product_key: str, settings: Settings) -> OrthophotoProduct:
+        product = OrthophotoAcquisitionService._products(settings).get(product_key.strip().lower())
+        if product is None:
+            raise AppError(
+                code="ORTHOPHOTO_PRODUCT_NOT_SUPPORTED",
+                message="Select an orthophoto product from the official product registry",
+                details={"product_key": product_key},
+                status_code=422,
+            )
+        return product
 
     @staticmethod
     def _prepared_request(
         payload: OrthophotoAcquireRequest,
         settings: Settings,
     ) -> dict[str, Any]:
+        product = OrthophotoAcquisitionService._product(payload.product_key, settings)
         if payload.bbox.crs.upper() != "EPSG:4326":
             raise AppError(code="INVALID_CRS", message="Orthophoto selection bbox must use EPSG:4326", status_code=400)
         min_x = float(payload.bbox.min_x)
@@ -68,8 +218,9 @@ class OrthophotoAcquisitionService:
         bbox_31370 = [float(value) for value in lambert_bounds]
         request_identity = {
             "provider": OrthophotoAcquisitionService.PROVIDER,
-            "wms_url": settings.orthophoto_wms_url,
-            "layer": settings.orthophoto_wms_layer,
+            "product_key": product.key,
+            "wms_url": product.wms_url,
+            "layer": product.layer,
             "bbox_epsg4326": [round(value, 8) for value in bbox_4326],
             "bbox_epsg31370": [round(value, 3) for value in bbox_31370],
             "width": width,
@@ -77,11 +228,18 @@ class OrthophotoAcquisitionService:
             "resolution_m": settings.orthophoto_resolution_m,
         }
         request_hash = hashlib.sha256(json.dumps(request_identity, sort_keys=True).encode("utf-8")).hexdigest()
+        spatial_identity = {
+            "bbox_epsg4326": request_identity["bbox_epsg4326"],
+            "width": width,
+            "height": height,
+            "resolution_m": settings.orthophoto_resolution_m,
+        }
+        spatial_hash = hashlib.sha256(json.dumps(spatial_identity, sort_keys=True).encode("utf-8")).hexdigest()
         params = {
             "SERVICE": "WMS",
             "VERSION": "1.3.0",
             "REQUEST": "GetMap",
-            "LAYERS": settings.orthophoto_wms_layer,
+            "LAYERS": product.layer,
             "STYLES": "",
             "FORMAT": "image/tiff",
             "CRS": "EPSG:31370",
@@ -91,8 +249,10 @@ class OrthophotoAcquisitionService:
         }
         return {
             **request_identity,
+            "product": product,
+            "spatial_hash": spatial_hash,
             "request_hash": request_hash,
-            "request_url": f"{settings.orthophoto_wms_url}?{urlencode(params)}",
+            "request_url": f"{product.wms_url}?{urlencode(params)}",
             "params": params,
             "bbox_epsg4326": bbox_4326,
             "bbox_epsg31370": bbox_31370,
@@ -124,8 +284,14 @@ class OrthophotoAcquisitionService:
             )
 
     @staticmethod
-    def _cached_dataset(db, project_id: UUID, filename: str, settings: Settings) -> Dataset | None:
-        if settings.orthophoto_cache_ttl_hours <= 0:
+    def _cached_dataset(
+        db,
+        project_id: UUID,
+        filename: str,
+        settings: Settings,
+        product: OrthophotoProduct,
+    ) -> Dataset | None:
+        if product.key == "most_recent" and settings.orthophoto_cache_ttl_hours <= 0:
             return None
         candidate = (
             db.query(Dataset)
@@ -145,7 +311,7 @@ class OrthophotoAcquisitionService:
             return None
         if imported_at.tzinfo is None:
             imported_at = imported_at.replace(tzinfo=UTC)
-        if datetime.now(UTC) - imported_at > timedelta(hours=settings.orthophoto_cache_ttl_hours):
+        if product.key == "most_recent" and datetime.now(UTC) - imported_at > timedelta(hours=settings.orthophoto_cache_ttl_hours):
             return None
         return candidate
 
@@ -196,7 +362,9 @@ class OrthophotoAcquisitionService:
                 with warnings.catch_warnings():
                     warnings.simplefilter("ignore", NotGeoreferencedWarning)
                     with source_memory.open() as source:
-                        if source.width != prepared["width"] or source.height != prepared["height"] or source.count < 3:
+                        product: OrthophotoProduct = prepared["product"]
+                        minimum_band_count = 1 if product.color_mode == "panchromatic" else 3
+                        if source.width != prepared["width"] or source.height != prepared["height"] or source.count < minimum_band_count:
                             raise AppError(
                                 code="ORTHOPHOTO_PROVIDER_INVALID_RESPONSE",
                                 message="Official orthophoto dimensions or RGB bands do not match the bounded request",
@@ -216,7 +384,7 @@ class OrthophotoAcquisitionService:
                         with output_memory.open(**profile) as output:
                             output.write(image)
                             output.update_tags(
-                                source="Digitaal Vlaanderen OMWRGBMRVL WMS Ortho layer",
+                                source=f"Digitaal Vlaanderen WMS {product.layer}",
                                 source_url=prepared["request_url"],
                                 attribution=OrthophotoAcquisitionService.ATTRIBUTION,
                                 acquisition="explicit_bounded_map_selection",
@@ -245,44 +413,74 @@ class OrthophotoAcquisitionService:
         if not resolved_settings.orthophoto_enabled:
             raise AppError(code="ORTHOPHOTO_NOT_CONFIGURED", message="Official orthophoto acquisition is disabled", status_code=503)
         prepared = OrthophotoAcquisitionService._prepared_request(payload, resolved_settings)
+        product: OrthophotoProduct = prepared["product"]
         OrthophotoAcquisitionService._validate_area_scope(db, project_id, payload.area_id, prepared["bbox_epsg4326"])
-        filename = f"orthofoto_selectie_{prepared['request_hash'][:12]}.tif"
+        filename = f"orthofoto_{product.key}_{prepared['request_hash'][:12]}.tif"
 
-        cached = None if payload.force_refresh else OrthophotoAcquisitionService._cached_dataset(db, project_id, filename, resolved_settings)
+        cached = None if payload.force_refresh else OrthophotoAcquisitionService._cached_dataset(
+            db,
+            project_id,
+            filename,
+            resolved_settings,
+            product,
+        )
         if cached is not None:
             return OrthophotoAcquisitionResult(
                 output_dataset_id=cached.id,
                 reused=True,
                 provider=OrthophotoAcquisitionService.PROVIDER,
-                layer=resolved_settings.orthophoto_wms_layer,
+                product_key=product.key,
+                display_name=product.display_name,
+                observation_label=product.observation_label,
+                temporal_granularity=product.temporal_granularity,
+                supports_detection=product.supports_detection,
+                layer=product.layer,
                 width=prepared["width"],
                 height=prepared["height"],
                 resolution_m=resolved_settings.orthophoto_resolution_m,
                 bbox_epsg4326=prepared["bbox_epsg4326"],
                 bbox_epsg31370=prepared["bbox_epsg31370"],
                 attribution=OrthophotoAcquisitionService.ATTRIBUTION,
-                limitation_message=OrthophotoAcquisitionService.LIMITATION,
+                limitation_message=product.limitation_message,
             ).model_dump(mode="json")
 
         raw_content, response_content_type = OrthophotoAcquisitionService._fetch(prepared["request_url"], resolved_settings, opener)
         geotiff_content = OrthophotoAcquisitionService._georeference_tiff(raw_content, prepared)
         acquired_at = datetime.now(UTC)
+        observed_at = product.observed_at or acquired_at
         dataset = DatasetService.import_raster_bytes(
             db,
             project_id=project_id,
             area_id=payload.area_id,
             filename=filename,
             content=geotiff_content,
-            source="Digitaal Vlaanderen OMWRGBMRVL WMS",
+            source=f"Digitaal Vlaanderen WMS {product.layer}",
             source_name=OrthophotoAcquisitionService.PROVIDER,
-            source_version=f"most_recent_at_{acquired_at.date().isoformat()}",
+            temporal_series_key=f"digitaal-vlaanderen:orthophoto:{prepared['spatial_hash'][:24]}",
+            observed_at=observed_at,
+            valid_from=product.valid_from or observed_at,
+            valid_to=product.valid_to,
+            temporal_granularity=product.temporal_granularity,
+            source_version=(
+                f"most_recent_at_{acquired_at.date().isoformat()}"
+                if product.key == "most_recent"
+                else product.key
+            ),
             content_type="image/tiff",
             source_metadata={
                 "provider": OrthophotoAcquisitionService.PROVIDER,
                 "service": "WMS",
                 "service_version": "1.3.0",
-                "layer": resolved_settings.orthophoto_wms_layer,
-                "catalog_url": OrthophotoAcquisitionService.CATALOG_URL,
+                "product_key": product.key,
+                "product_display_name": product.display_name,
+                "observation_label": product.observation_label,
+                "observation_date_precision": product.temporal_granularity,
+                "native_resolution_m": product.native_resolution_m,
+                "requested_resolution_m": resolved_settings.orthophoto_resolution_m,
+                "color_mode": product.color_mode,
+                "supports_detection": product.supports_detection,
+                "layer": product.layer,
+                "catalog_url": product.catalog_url,
                 "attribution": OrthophotoAcquisitionService.ATTRIBUTION,
                 "license_note": "Gebruik volgens het gebruiksrecht geografische webdiensten van Digitaal Vlaanderen.",
             },
@@ -290,6 +488,7 @@ class OrthophotoAcquisitionService:
                 "acquisition": "explicit_bounded_map_selection",
                 "acquired_at": acquired_at.isoformat(),
                 "request_hash": prepared["request_hash"],
+                "spatial_hash": prepared["spatial_hash"],
                 "request_url": prepared["request_url"],
                 "response_content_type": response_content_type,
                 "bbox_epsg4326": prepared["bbox_epsg4326"],
@@ -297,19 +496,70 @@ class OrthophotoAcquisitionService:
                 "width": prepared["width"],
                 "height": prepared["height"],
                 "resolution_m": resolved_settings.orthophoto_resolution_m,
-                "limitation_message": OrthophotoAcquisitionService.LIMITATION,
+                "limitation_message": product.limitation_message,
             },
         )
         return OrthophotoAcquisitionResult(
             output_dataset_id=dataset.id,
             reused=False,
             provider=OrthophotoAcquisitionService.PROVIDER,
-            layer=resolved_settings.orthophoto_wms_layer,
+            product_key=product.key,
+            display_name=product.display_name,
+            observation_label=product.observation_label,
+            temporal_granularity=product.temporal_granularity,
+            supports_detection=product.supports_detection,
+            layer=product.layer,
             width=prepared["width"],
             height=prepared["height"],
             resolution_m=resolved_settings.orthophoto_resolution_m,
             bbox_epsg4326=prepared["bbox_epsg4326"],
             bbox_epsg31370=prepared["bbox_epsg31370"],
             attribution=OrthophotoAcquisitionService.ATTRIBUTION,
-            limitation_message=OrthophotoAcquisitionService.LIMITATION,
+            limitation_message=product.limitation_message,
         ).model_dump(mode="json")
+
+    @staticmethod
+    def render_png(db, project_id: UUID, dataset_id: UUID, *, max_dimension: int = 1600) -> bytes:
+        dataset = db.get(Dataset, dataset_id)
+        if (
+            dataset is None
+            or dataset.project_id != project_id
+            or dataset.source_name != OrthophotoAcquisitionService.PROVIDER
+            or dataset.status != "ready"
+            or not dataset.storage_path
+            or not Path(dataset.storage_path).is_file()
+        ):
+            raise AppError(code="ORTHOPHOTO_NOT_FOUND", message="Orthophoto dataset not found", status_code=404)
+        try:
+            import numpy as np
+            import rasterio
+            from PIL import Image
+            from rasterio.enums import Resampling
+        except ImportError as exc:
+            raise AppError(code="RASTER_PROCESSING_UNAVAILABLE", message="Raster preview dependencies are unavailable", status_code=503) from exc
+
+        try:
+            with rasterio.open(dataset.storage_path) as source:
+                scale = min(1.0, max_dimension / max(source.width, source.height))
+                width = max(1, round(source.width * scale))
+                height = max(1, round(source.height * scale))
+                indexes = [1] if source.count == 1 else list(range(1, min(source.count, 3) + 1))
+                pixels = source.read(indexes, out_shape=(len(indexes), height, width), resampling=Resampling.bilinear)
+                if pixels.dtype != np.uint8:
+                    pixels = np.clip(pixels, 0, 255).astype(np.uint8)
+                if len(indexes) == 1:
+                    image = Image.fromarray(pixels[0])
+                else:
+                    image = Image.fromarray(np.moveaxis(pixels[:3], 0, 2))
+                output = io.BytesIO()
+                image.save(output, format="PNG", optimize=True)
+                return output.getvalue()
+        except AppError:
+            raise
+        except Exception as exc:
+            raise AppError(
+                code="ORTHOPHOTO_PREVIEW_FAILED",
+                message="The persisted orthophoto could not be rendered",
+                details={"reason": str(exc)},
+                status_code=500,
+            ) from exc

backend/app/services/vector_feature_service.py

@@ -23,6 +23,7 @@ FULL_AREA_CLIPPED_OPERATOR_TOOLS = {
     "provision_official_landuse_timeseries.py",
     "provision_regional_grb_buildings.py",
     "provision_regional_grb_context.py",
+    "provision_waterinfo_station_history.py",
 }
 
 
@@ -439,7 +440,7 @@ class VectorFeatureService:
             length_m = db.query(func.coalesce(func.sum(length_expression), 0.0)).filter(*metric_filter).scalar()
             divisor = 1_000.0 if unit == "km" else 1.0
             metric_value = float(length_m or 0.0) / divisor
-        elif method in {"sum", "area_weighted_sum"}:
+        elif method in {"sum", "mean", "area_weighted_sum"}:
             property_name = str(config.get("property") or "").strip()
             if not property_name:
                 raise AppError(
@@ -457,8 +458,9 @@ class VectorFeatureService:
                 )
                 coverage_ratio = intersection_area / func.nullif(source_area, 0.0)
                 value_expression = numeric_value * coverage_ratio
+            aggregate_function = func.avg if method == "mean" else func.sum
             aggregate_value = (
-                db.query(func.coalesce(func.sum(value_expression), 0.0))
+                db.query(func.coalesce(aggregate_function(value_expression), 0.0))
                 .filter(*selection_filter)
                 .filter(VectorFeature.properties_json.op("->>")(property_name).isnot(None))
                 .scalar()

backend/tests/test_sprint196_map_orthophoto_analysis.py

@@ -86,7 +86,13 @@ class FakeImageResponse:
         return self.content[:limit]
 
 
-def _selection_payload(*, side_m: float = 512.0, force_refresh: bool = True, area_id=None) -> OrthophotoAcquireRequest:
+def _selection_payload(
+    *,
+    side_m: float = 512.0,
+    force_refresh: bool = True,
+    area_id=None,
+    product_key: str = "most_recent",
+) -> OrthophotoAcquireRequest:
     west, south = 199_000.0, 210_000.0
     transformer = Transformer.from_crs("EPSG:31370", "EPSG:4326", always_xy=True)
     min_lon, min_lat = transformer.transform(west, south)
@@ -100,6 +106,7 @@ def _selection_payload(*, side_m: float = 512.0, force_refresh: bool = True, are
             "crs": "EPSG:4326",
         },
         area_id=area_id,
+        product_key=product_key,
         force_refresh=force_refresh,
     )
 
@@ -136,6 +143,25 @@ def test_orthophoto_request_is_bounded_and_uses_official_wms_contract() -> None:
     assert len(prepared["request_hash"]) == 64
 
 
+def test_orthophoto_product_registry_exposes_only_governed_official_layers() -> None:
+    settings = Settings(_env_file=None)
+    products = OrthophotoAcquisitionService.list_products(settings)
+    keys = [item["key"] for item in products]
+
+    assert keys[0] == "most_recent"
+    assert {"2025", "2012", "2008_2011", "2000_2003", "1979_1990", "1971"}.issubset(keys)
+    assert next(item for item in products if item["key"] == "most_recent")["supports_detection"] is True
+    assert all(item["supports_detection"] is False for item in products if item["key"] != "most_recent")
+
+    prepared = OrthophotoAcquisitionService._prepared_request(_selection_payload(product_key="1971"), settings)
+    assert prepared["params"]["LAYERS"] == "OKZPAN71VL"
+    assert prepared["wms_url"] == "https://geo.api.vlaanderen.be/OKZ/wms"
+
+    with pytest.raises(AppError) as exc_info:
+        OrthophotoAcquisitionService._prepared_request(_selection_payload(product_key="arbitrary-layer"), settings)
+    assert exc_info.value.code == "ORTHOPHOTO_PRODUCT_NOT_SUPPORTED"
+
+
 @pytest.mark.parametrize(
     ("side_m", "expected_code"),
     [(64.0, "ORTHOPHOTO_SELECTION_TOO_SMALL"), (1_200.0, "ORTHOPHOTO_SELECTION_TOO_LARGE")],
@@ -240,7 +266,7 @@ def test_orthophoto_acquisition_reuses_fresh_exact_request_without_provider_call
     cached = Dataset(
         id=uuid4(),
         project_id=project_id,
-        name=f"orthofoto_selectie_{prepared['request_hash'][:12]}.tif",
+        name=f"orthofoto_most_recent_{prepared['request_hash'][:12]}.tif",
         dataset_type="raster",
         source="Digitaal Vlaanderen",
         source_name="digitaal_vlaanderen_orthophoto",
@@ -277,6 +303,54 @@ def test_orthophoto_provider_rejects_non_image_response() -> None:
     assert exc_info.value.code == "ORTHOPHOTO_PROVIDER_INVALID_RESPONSE"
 
 
+def test_historical_orthophoto_persists_temporal_product_provenance(tmp_path) -> None:
+    project_id = uuid4()
+    payload = _selection_payload(product_key="2020")
+    db = FakeSession({(Project, project_id): Project(id=project_id, name="Mol")})
+    settings = Settings(_env_file=None, storage_root=str(tmp_path), orthophoto_resolution_m=1.0)
+    prepared = OrthophotoAcquisitionService._prepared_request(payload, settings)
+    response = FakeImageResponse(_source_tiff(prepared["width"], prepared["height"]))
+
+    result = OrthophotoAcquisitionService.acquire(
+        db,
+        project_id,
+        payload,
+        settings=settings,
+        opener=lambda *_args, **_kwargs: response,
+    )
+
+    dataset = next(row for row in db.added if isinstance(row, Dataset))
+    assert result["product_key"] == "2020"
+    assert result["supports_detection"] is False
+    assert dataset.observed_at.year == 2020
+    assert dataset.temporal_granularity == "year"
+    assert dataset.source_metadata["layer"] == "OMWRGB20VL"
+    assert dataset.source_metadata["product_key"] == "2020"
+    assert dataset.provenance_metadata["spatial_hash"] == prepared["spatial_hash"]
+
+
+def test_persisted_orthophoto_renders_browser_png(tmp_path) -> None:
+    project_id = uuid4()
+    dataset_id = uuid4()
+    path = tmp_path / "ortho.tif"
+    path.write_bytes(_source_tiff(32, 24))
+    dataset = Dataset(
+        id=dataset_id,
+        project_id=project_id,
+        name="ortho.tif",
+        dataset_type="raster",
+        source="Digitaal Vlaanderen",
+        source_name="digitaal_vlaanderen_orthophoto",
+        status="ready",
+        storage_path=str(path),
+    )
+    db = FakeSession({(Dataset, dataset_id): dataset})
+
+    png = OrthophotoAcquisitionService.render_png(db, project_id, dataset_id)
+
+    assert png.startswith(b"\x89PNG\r\n\x1a\n")
+
+
 def test_orthophoto_endpoint_returns_canonical_job_envelope(monkeypatch) -> None:
     project_id = uuid4()
     output_dataset_id = uuid4()
@@ -307,6 +381,22 @@ def test_orthophoto_endpoint_returns_canonical_job_envelope(monkeypatch) -> None
     assert any(isinstance(row, Job) for row in db.added)
 
 
+def test_orthophoto_product_endpoint_returns_canonical_envelope() -> None:
+    project_id = uuid4()
+    db = FakeSession({(Project, project_id): Project(id=project_id, name="Mol")})
+    app.dependency_overrides[get_db] = lambda: db
+    try:
+        response = TestClient(app).get(f"/api/v1/projects/{project_id}/datasets/orthophoto/products")
+    finally:
+        app.dependency_overrides.clear()
+
+    assert response.status_code == 200
+    body = response.json()
+    assert set(body) == {"data"}
+    assert body["data"]["total"] == len(body["data"]["items"])
+    assert body["data"]["items"][0]["key"] == "most_recent"
+
+
 def test_frontend_connects_map_selection_to_existing_detection_and_qa_flows() -> None:
     app_source = (ROOT / "frontend" / "src" / "App.tsx").read_text(encoding="utf-8")
     hook_source = (ROOT / "frontend" / "src" / "hooks" / "useMapOrthophotoAnalysis.ts").read_text(encoding="utf-8")

backend/tests/test_sprint201_semantic_selection_metrics.py

@@ -114,6 +114,36 @@ def test_population_keeps_configured_metric_and_adds_sector_count() -> None:
     }
 
 
+def test_station_measurement_uses_numeric_mean_without_area_extrapolation() -> None:
+    dataset = themed_dataset("water", method="mean")
+    dataset.source_name = "waterinfo"
+    dataset.source_metadata.update(
+        {
+            "semantic_metrics": False,
+            "selection_aggregation": {
+                "metric_key": "water_level",
+                "method": "mean",
+                "property": "annual_mean_water_level_m",
+                "label": "Jaargemiddelde waterstand",
+                "unit": "m",
+                "warning": "Puntmeting; geen gebiedsdekkend watervolume.",
+            },
+        }
+    )
+
+    result = VectorFeatureService.summarize_features_by_bbox(
+        SequenceScalarSession([30.455]),
+        dataset=dataset,
+        bbox=BBOX,
+        total_feature_count=1,
+    )
+
+    assert result["metric_value"] == 30.455
+    assert result["aggregation_method"] == "mean"
+    assert result["metric_unit"] == "m"
+    assert result["warning"] == "Puntmeting; geen gebiedsdekkend watervolume."
+
+
 def test_future_regional_imports_persist_semantic_aggregation_configuration() -> None:
     buildings = (ROOT / "scripts/provision_regional_grb_buildings.py").read_text(encoding="utf-8")
     context = (ROOT / "scripts/provision_regional_grb_context.py").read_text(encoding="utf-8")

backend/tests/test_sprint203_waterinfo_history.py

@@ -0,0 +1,135 @@
+from __future__ import annotations
+
+import importlib.util
+import sys
+from pathlib import Path
+
+from shapely.geometry import box
+
+
+ROOT = Path(__file__).resolve().parents[2]
+
+
+def load_operator():
+    script_path = ROOT / "scripts" / "provision_waterinfo_station_history.py"
+    spec = importlib.util.spec_from_file_location("waterinfo_history_operator", script_path)
+    assert spec is not None
+    assert spec.loader is not None
+    module = importlib.util.module_from_spec(spec)
+    sys.modules[spec.name] = module
+    spec.loader.exec_module(module)
+    return module
+
+
+class JsonResponse:
+    ok = True
+    status_code = 200
+    text = ""
+
+    def __init__(self, payload):
+        self.payload = payload
+
+    def raise_for_status(self):
+        return None
+
+    def json(self):
+        return self.payload
+
+
+class JsonSession:
+    def __init__(self, payloads):
+        self.payloads = iter(payloads)
+        self.calls = []
+
+    def get(self, url, *, params, timeout):
+        self.calls.append((url, params, timeout))
+        return JsonResponse(next(self.payloads))
+
+
+def test_waterinfo_station_discovery_filters_exact_area_and_uses_annual_group() -> None:
+    module = load_operator()
+    payload = {
+        "type": "FeatureCollection",
+        "features": [
+            {
+                "type": "Feature",
+                "geometry": {"type": "Point", "coordinates": [5.1, 51.2]},
+                "properties": {"ts_id": 5319042, "station_no": "L10_089", "station_name": "Mol/ScheppelijkeNete"},
+            },
+            {
+                "type": "Feature",
+                "geometry": {"type": "Point", "coordinates": [6.0, 52.0]},
+                "properties": {"ts_id": 999, "station_no": "outside", "station_name": "Outside"},
+            },
+        ],
+    }
+    session = JsonSession([payload])
+
+    raw, stations = module.discover_station_series(
+        session,
+        module.PARAMETERS["water_level"],
+        box(5.0, 51.0, 5.3, 51.4),
+        timeout=30,
+    )
+
+    assert raw == payload
+    assert [item["ts_id"] for item in stations] == ["5319042"]
+    assert session.calls[0][1]["timeseriesgroup_id"] == "192784"
+    assert session.calls[0][1]["request"] == "getTimeseriesValueLayer"
+
+
+def test_waterinfo_annual_values_reject_invalid_sentinel_and_keep_real_zero() -> None:
+    module = load_operator()
+    payload = [
+        {
+            "ts_id": 5319042,
+            "data": [
+                ["2013-01-01T00:00:00.000+01:00", 30.46],
+                ["2014-01-01T00:00:00.000+01:00", -9999],
+                ["2015-01-01T00:00:00.000+01:00", 0.0],
+                ["2026-01-01T00:00:00.000+01:00", 99.0],
+            ],
+        }
+    ]
+    session = JsonSession([payload])
+
+    raw, values = module.fetch_annual_values(session, "5319042", from_year=2013, to_year=2025, timeout=30)
+
+    assert raw == payload
+    assert values == {2013: 30.46, 2015: 0.0}
+    assert session.calls[0][1]["request"] == "getTimeseriesValues"
+
+
+def test_waterinfo_snapshot_and_series_keep_station_identity_and_honest_metric() -> None:
+    module = load_operator()
+    parameter = module.PARAMETERS["water_level"]
+    station = {
+        "ts_id": "5319042",
+        "geometry": {"type": "Point", "coordinates": [5.1, 51.2]},
+        "properties": {
+            "station_id": "123",
+            "station_no": "L10_089",
+            "station_name": "Mol/ScheppelijkeNete",
+            "ts_unitsymbol": "m",
+        },
+    }
+
+    snapshot = module.build_snapshot(parameter, station, 2025, 30.455)
+
+    feature = snapshot["features"][0]
+    assert module.series_key(parameter, station) == "waterinfo:water_level:annual:l10-089"
+    assert feature["geometry"]["type"] == "Point"
+    assert feature["properties"]["annual_mean_water_level_m"] == 30.455
+    assert feature["properties"]["timeseries_id"] == "5319042"
+    assert "volume" in parameter.limitation
+
+
+def test_waterinfo_operator_is_packaged_and_readiness_checked() -> None:
+    readiness = (ROOT / "scripts" / "run_readiness_check.sh").read_text(encoding="utf-8")
+    dockerfile = (ROOT / "deploy" / "unraid" / "Dockerfile.all-in-one").read_text(encoding="utf-8")
+    vector_service = (ROOT / "backend" / "app" / "services" / "vector_feature_service.py").read_text(encoding="utf-8")
+
+    assert "py_compile scripts/provision_waterinfo_station_history.py" in readiness
+    assert "COPY scripts/provision_waterinfo_station_history.py" in dockerfile
+    assert '"provision_waterinfo_station_history.py"' in vector_service
+    assert '"sum", "mean", "area_weighted_sum"' in vector_service