Complete regional raster exploration

CHANGELOG.md

@@ -7,6 +7,27 @@
 
 # Changelog
 
+## Sprint 218 Regional terrain and flood completion (2026-07-16)
+
+- Provisioned and audited the complete governed Kempen matrices: 56 DHMV
+  DTM/DSM rasters and 336 VMM flood-scenario rasters across all 28 approved
+  municipalities, each with one DatasetVersion and a non-empty checksum-bound
+  GeoTIFF.
+- Bounded official WCS edge-grid rounding to at most 5%/0.25 m for DHMV and
+  VMM tile assembly, resampled accepted edge tiles to the exact 5 m analysis
+  grid and retained every source resolution and harmonized tile index in
+  provenance. Larger mismatches still fail closed.
+- Added exact regional raster-selection endpoints. They open only intersecting
+  persisted municipality partitions, mosaic the selected windows in memory
+  and calculate global cell statistics under the existing 12-million-cell
+  guard; no monolithic or hidden authoritative raster is created.
+- Updated the Map workspace to expose DHMV and VMM on the complete Kempen Area,
+  render all 28 matching MapLibre partitions, deduplicate VMM into twelve
+  scenario choices and analyse a drawn cross-boundary rectangle without first
+  selecting a municipality.
+- Preserved the distinction between DHMV height, modeled VMM scenario depth,
+  permanent water, bathymetry and concurrent flood volume.
+
 ## Sprint 217 Regional DOV soil coverage (2026-07-16)
 
 - Added a governed regional DOV soil operator for all 28 approved Kempen

backend/README.md

@@ -1246,6 +1246,12 @@ and a complete failure summary. Persistence remains inside the canonical
 DHMV acquisition service and Dataset/DatasetVersion/Job flow; the operator
 does not fetch WCS bytes or write raster metadata directly.
 
+The complete live matrix contains 56 ready Datasets and 56 DatasetVersions
+across 28 Areas. On the complete Kempen Area the Map workspace presents those
+partitions as one logical DTM/DSM layer. `POST .../datasets/raster/terrain/select`
+opens only partitions intersecting the drawn rectangle and computes exact
+global cell statistics. It does not create a hidden regional mosaic.
+
 Settings: `DHMV_ENABLED`, `DHMV_WCS_URL`, `DHMV_RESOLUTION_M`,
 `DHMV_MIN_SIDE_M`, `DHMV_MAX_SIDE_M`, `DHMV_MAX_PIXELS`,
 `DHMV_TIMEOUT_SECONDS` and `DHMV_MAX_RESPONSE_MB`.
@@ -1295,6 +1301,12 @@ The full Kempen scope with all products means 28 municipalities times 12
 scenario rasters. This is intentionally explicit operator work, not startup
 work and not a browser-side provider fetch.
 
+The complete live matrix contains 336 ready Datasets and 336 DatasetVersions.
+The regional Map workspace deduplicates them into twelve scenario choices,
+renders all municipality image partitions for the selected scenario and uses
+`POST .../datasets/raster/flood-hazard/select` for exact bounded cross-boundary
+analysis. The same 12-million-cell guard prevents unsafe full-region reads.
+
 Use `--products pluviaal_current_t100`, `--resolution-m 5` or `--force` for an
 explicit subset/refresh. `POST .../raster/flood-hazard/select` returns mapped
 inundated hectares, selection share and local modeled maximum-depth statistics.

backend/app/api/routes/datasets.py

@@ -4,7 +4,6 @@ import json
 from datetime import datetime
 from typing import Any
 from uuid import UUID
-from uuid import UUID as _UUID
 
 from fastapi import APIRouter, Depends, File, Form, HTTPException, Query, Response
 from fastapi import UploadFile
@@ -23,8 +22,10 @@ from app.schemas import (
     RasterNdbiRequest,
     OrthophotoAcquireRequest,
     DhmvAcquireRequest,
+    TerrainPartitionSelectionRequest,
     TerrainSelectionRequest,
     FloodHazardAcquireRequest,
+    FloodHazardPartitionSelectionRequest,
     FloodHazardSelectionRequest,
     ThematicRasterAcquireRequest,
     ThematicRasterSelectionRequest,
@@ -32,14 +33,12 @@ from app.schemas import (
     VectorBufferRequest,
     VectorClipRequest,
     VectorIntersectRequest,
-    VectorSelectionBBox,
+    VectorSelectionBBox,  # noqa: F401 - retained as a route-module compatibility export
     VectorSelectionDeriveRequest,
     VectorSelectionRequest,
     VectorSelectionResponse,
 )
-from app.schemas.job import JobCreate
 from app.schemas.dataset import DatasetCreateResponse, DatasetTemporalUpdate
-from app.schemas.operations import VectorOperationResult
 from app.services.job_service import JobService
 from app.services.raster_operations_service import RasterOperationsService
 from app.services.vector_operations_service import VectorOperationsService
@@ -550,6 +549,15 @@ def raster_terrain_selection(
     return envelope(TerrainAnalysisService.analyze(db, project_id, dataset_id, payload))
 
 
+@router.post("/datasets/raster/terrain/select", response_model=dict)
+def partitioned_raster_terrain_selection(
+    project_id: UUID,
+    payload: TerrainPartitionSelectionRequest,
+    db: Session = Depends(get_db),
+):
+    return envelope(TerrainAnalysisService.analyze_partitions(db, project_id, payload))
+
+
 @router.get("/datasets/{dataset_id}/raster/terrain/image")
 def raster_terrain_image(
     project_id: UUID,
@@ -574,6 +582,15 @@ def raster_flood_hazard_selection(
     return envelope(FloodHazardAnalysisService.analyze(db, project_id, dataset_id, payload))
 
 
+@router.post("/datasets/raster/flood-hazard/select", response_model=dict)
+def partitioned_raster_flood_hazard_selection(
+    project_id: UUID,
+    payload: FloodHazardPartitionSelectionRequest,
+    db: Session = Depends(get_db),
+):
+    return envelope(FloodHazardAnalysisService.analyze_partitions(db, project_id, payload))
+
+
 @router.get("/datasets/{dataset_id}/raster/flood-hazard/image")
 def raster_flood_hazard_image(
     project_id: UUID,

backend/app/schemas/__init__.py

@@ -38,6 +38,7 @@ from .dhmv import (
     DhmvAcquisitionResult,
     DhmvProductRead,
     TerrainMetric,
+    TerrainPartitionSelectionRequest,
     TerrainSelectionRequest,
     TerrainSelectionResponse,
     TerrainSelectionSummary,
@@ -46,6 +47,7 @@ from .flood_hazard import (
     FloodHazardAcquireRequest,
     FloodHazardAcquisitionResult,
     FloodHazardMetric,
+    FloodHazardPartitionSelectionRequest,
     FloodHazardProductRead,
     FloodHazardSelectionRequest,
     FloodHazardSelectionResponse,
@@ -166,12 +168,14 @@ __all__ = [
     "DhmvAcquisitionResult",
     "DhmvProductRead",
     "TerrainMetric",
+    "TerrainPartitionSelectionRequest",
     "TerrainSelectionRequest",
     "TerrainSelectionResponse",
     "TerrainSelectionSummary",
     "FloodHazardAcquireRequest",
     "FloodHazardAcquisitionResult",
     "FloodHazardMetric",
+    "FloodHazardPartitionSelectionRequest",
     "FloodHazardProductRead",
     "FloodHazardSelectionRequest",
     "FloodHazardSelectionResponse",

backend/app/schemas/dhmv.py

@@ -56,6 +56,10 @@ class TerrainSelectionRequest(BaseModel):
     area_id: UUID | None = None
 
 
+class TerrainPartitionSelectionRequest(TerrainSelectionRequest):
+    product_key: str = "dtm_1m"
+
+
 class TerrainMetric(BaseModel):
     metric_key: str
     metric_label: str
@@ -76,6 +80,8 @@ class TerrainSelectionSummary(BaseModel):
 
 class TerrainSelectionResponse(BaseModel):
     dataset_id: UUID
+    dataset_ids: list[UUID] = Field(default_factory=list)
+    partition_count: int = Field(default=1, ge=1)
     product_key: str
     surface_model: str
     selection_bbox: VectorSelectionBBox

backend/app/schemas/flood_hazard.py

@@ -59,6 +59,10 @@ class FloodHazardSelectionRequest(BaseModel):
     area_id: UUID | None = None
 
 
+class FloodHazardPartitionSelectionRequest(FloodHazardSelectionRequest):
+    product_key: str = "pluviaal_current_t100"
+
+
 class FloodHazardMetric(BaseModel):
     metric_key: str
     metric_label: str
@@ -79,6 +83,8 @@ class FloodHazardSelectionSummary(BaseModel):
 
 class FloodHazardSelectionResponse(BaseModel):
     dataset_id: UUID
+    dataset_ids: list[UUID] = Field(default_factory=list)
+    partition_count: int = Field(default=1, ge=1)
     product_key: str
     mechanism: str
     climate_context: str

backend/app/services/flood_hazard_analysis_service.py

@@ -16,11 +16,13 @@ from app.core.errors import AppError
 from app.models import Area, Dataset
 from app.schemas.flood_hazard import (
     FloodHazardMetric,
+    FloodHazardPartitionSelectionRequest,
     FloodHazardSelectionRequest,
     FloodHazardSelectionResponse,
     FloodHazardSelectionSummary,
 )
 from app.services.flood_hazard_acquisition_service import FloodHazardAcquisitionService
+from app.services.raster_partition_analysis_service import RasterPartitionAnalysisService
 
 
 class FloodHazardAnalysisService:
@@ -170,6 +172,8 @@ class FloodHazardAnalysisService:
         primary = metrics[0]
         response = FloodHazardSelectionResponse(
             dataset_id=dataset.id,
+            dataset_ids=[dataset.id],
+            partition_count=1,
             product_key=product.key,
             mechanism=product.mechanism,
             climate_context=product.climate_context,
@@ -195,6 +199,129 @@ class FloodHazardAnalysisService:
         )
         return response.model_dump(mode="json")
 
+    @staticmethod
+    def analyze_partitions(
+        db,
+        project_id: UUID,
+        payload: FloodHazardPartitionSelectionRequest,
+        *,
+        settings: Settings | None = None,
+    ) -> dict:
+        resolved_settings = settings or get_settings()
+        product = FloodHazardAcquisitionService._products().get(payload.product_key.strip().lower())
+        if product is None:
+            raise AppError(
+                code="FLOOD_HAZARD_PRODUCT_NOT_SUPPORTED",
+                message="Select a governed VMM fluvial or pluvial flood-depth scenario",
+                details={"product_key": payload.product_key},
+                status_code=422,
+            )
+        selection_4326 = FloodHazardAnalysisService._selection_geometry(db, project_id, payload)
+        partition = RasterPartitionAnalysisService.select(
+            db,
+            project_id,
+            source_name=FloodHazardAcquisitionService.PROVIDER,
+            product_key=product.key,
+            selection_geometry_4326=selection_4326,
+            nodata=FloodHazardAcquisitionService.NODATA,
+            max_pixels=resolved_settings.flood_hazard_max_pixels,
+        )
+        try:
+            import numpy as np
+        except ImportError as exc:
+            raise AppError(
+                code="RASTER_PROCESSING_UNAVAILABLE",
+                message="Numpy is required for partitioned flood-hazard analysis",
+                status_code=503,
+            ) from exc
+
+        raw = partition.values
+        valid = (
+            partition.selected_cells
+            & np.isfinite(raw)
+            & (raw != FloodHazardAcquisitionService.NODATA)
+            & (raw > 0.0)
+        )
+        values = raw[valid]
+        selected_cell_count = int(partition.selected_cells.sum())
+        inundated_cell_count = int(values.size)
+        cell_area_m2 = partition.resolution_x * partition.resolution_y
+
+        def metric(key: str, label: str, value: float, unit: str, method: str) -> FloodHazardMetric:
+            return FloodHazardMetric(
+                metric_key=key,
+                metric_label=label,
+                metric_value=round(float(value), 4),
+                metric_unit=unit,
+                aggregation_method=method,
+            )
+
+        inundated_area_ha = inundated_cell_count * cell_area_m2 / 10_000.0
+        metrics = [
+            metric(
+                "modelled_inundated_area_ha",
+                "Gemodelleerd overstroomd oppervlak",
+                inundated_area_ha,
+                "ha",
+                "positive_depth_cells_times_cell_area",
+            ),
+            metric(
+                "modelled_inundated_share_pct",
+                "Aandeel selectie met gemodelleerde diepte",
+                inundated_cell_count / max(1, selected_cell_count) * 100.0,
+                "%",
+                "positive_depth_cells_divided_by_selected_cells",
+            ),
+        ]
+        if inundated_cell_count:
+            metrics.extend(
+                [
+                    metric("modelled_depth_mean_m", "Gemiddelde gemodelleerde maximumdiepte", values.mean(), "m", "mean_positive_depth_cells"),
+                    metric("modelled_depth_p90_m", "90e percentiel gemodelleerde maximumdiepte", np.percentile(values, 90), "m", "percentile_90_positive_depth_cells"),
+                    metric("modelled_depth_max_m", "Hoogste gemodelleerde maximumdiepte", values.max(), "m", "maximum_positive_depth_cells"),
+                    metric(
+                        "modelled_max_depth_area_integral_m3",
+                        "Diepte-oppervlakte-integraal (geen gelijktijdig volume)",
+                        values.sum() * cell_area_m2,
+                        "m3",
+                        "sum_local_max_depth_times_cell_area",
+                    ),
+                ]
+            )
+        primary = metrics[0]
+        first_dataset = partition.datasets[0]
+        response = FloodHazardSelectionResponse(
+            dataset_id=first_dataset.id,
+            dataset_ids=[dataset.id for dataset in partition.datasets],
+            partition_count=len(partition.datasets),
+            product_key=product.key,
+            mechanism=product.mechanism,
+            climate_context=product.climate_context,
+            probability_class=product.probability_class,
+            return_period_years=product.return_period_years,
+            selection_bbox=payload.bbox,
+            selection_area_id=payload.area_id,
+            selected_cell_count=selected_cell_count,
+            inundated_cell_count=inundated_cell_count,
+            inundated_fraction=round(inundated_cell_count / max(1, selected_cell_count), 6),
+            resolution_m=round(max(partition.resolution_x, partition.resolution_y), 4),
+            summary=FloodHazardSelectionSummary(
+                metric_label=primary.metric_label,
+                metric_value=primary.metric_value,
+                metric_unit=primary.metric_unit,
+                aggregation_method=primary.aggregation_method,
+                primary_metric_key=primary.metric_key,
+                metrics=metrics,
+            ),
+            unsupported_metrics=FloodHazardAnalysisService.UNSUPPORTED_METRICS,
+            limitation_message=(
+                f"{FloodHazardAnalysisService.LIMITATION} De selectie werd exact berekend over "
+                f"{len(partition.datasets)} persistente gemeentelijke rasterpartities."
+            ),
+            generated_at=datetime.now(UTC).isoformat(),
+        )
+        return response.model_dump(mode="json")
+
     @staticmethod
     def render_png(db, project_id: UUID, dataset_id: UUID, *, max_dimension: int = 1800) -> bytes:
         dataset = FloodHazardAnalysisService._load_dataset(db, project_id, dataset_id)

backend/app/services/raster_partition_analysis_service.py

@@ -0,0 +1,200 @@
+from __future__ import annotations
+
+import math
+from contextlib import ExitStack
+from dataclasses import dataclass
+from pathlib import Path
+from typing import Any
+from uuid import UUID
+
+from pyproj import Transformer
+from shapely.geometry import mapping
+from shapely.ops import transform as shapely_transform
+
+from app.core.errors import AppError
+from app.models import Dataset
+
+
+@dataclass(frozen=True)
+class RasterPartitionSelection:
+    datasets: list[Dataset]
+    values: Any
+    selected_cells: Any
+    resolution_x: float
+    resolution_y: float
+
+
+class RasterPartitionAnalysisService:
+    MAX_PARTITIONS = 64
+
+    @staticmethod
+    def _bbox_intersects(dataset: Dataset, bbox: tuple[float, float, float, float]) -> bool:
+        source_bbox = (dataset.source_metadata or {}).get("bbox_epsg4326")
+        if not isinstance(source_bbox, list) or len(source_bbox) != 4:
+            return True
+        try:
+            min_x, min_y, max_x, max_y = (float(value) for value in source_bbox)
+        except (TypeError, ValueError):
+            return True
+        return not (
+            max_x <= bbox[0]
+            or min_x >= bbox[2]
+            or max_y <= bbox[1]
+            or min_y >= bbox[3]
+        )
+
+    @staticmethod
+    def _candidate_datasets(
+        db,
+        project_id: UUID,
+        *,
+        source_name: str,
+        product_key: str,
+        bbox: tuple[float, float, float, float],
+    ) -> list[Dataset]:
+        rows = (
+            db.query(Dataset)
+            .filter(
+                Dataset.project_id == project_id,
+                Dataset.source_name == source_name,
+                Dataset.dataset_type == "raster",
+                Dataset.status == "ready",
+            )
+            .all()
+        )
+        candidates = [
+            dataset
+            for dataset in rows
+            if str((dataset.source_metadata or {}).get("product_key") or "") == product_key
+            and dataset.storage_path
+            and Path(dataset.storage_path).is_file()
+            and RasterPartitionAnalysisService._bbox_intersects(dataset, bbox)
+        ]
+        candidates.sort(key=lambda dataset: (str(dataset.area_id or ""), str(dataset.id)))
+        if not candidates:
+            raise AppError(
+                code="RASTER_PARTITIONS_NOT_FOUND",
+                message="No persisted raster partitions cover this selection",
+                details={"source_name": source_name, "product_key": product_key},
+                status_code=404,
+            )
+        if len(candidates) > RasterPartitionAnalysisService.MAX_PARTITIONS:
+            raise AppError(
+                code="RASTER_PARTITION_LIMIT_EXCEEDED",
+                message="The selection intersects too many raster partitions",
+                details={
+                    "partition_count": len(candidates),
+                    "max_partitions": RasterPartitionAnalysisService.MAX_PARTITIONS,
+                },
+                status_code=422,
+            )
+        return candidates
+
+    @staticmethod
+    def select(
+        db,
+        project_id: UUID,
+        *,
+        source_name: str,
+        product_key: str,
+        selection_geometry_4326,
+        nodata: float,
+        max_pixels: int,
+    ) -> RasterPartitionSelection:
+        try:
+            import numpy as np
+            import rasterio
+            from rasterio.features import geometry_mask
+            from rasterio.merge import merge
+        except ImportError as exc:
+            raise AppError(
+                code="RASTER_PROCESSING_UNAVAILABLE",
+                message="Rasterio and numpy are required for partitioned raster analysis",
+                status_code=503,
+            ) from exc
+
+        bbox = tuple(float(value) for value in selection_geometry_4326.bounds)
+        datasets = RasterPartitionAnalysisService._candidate_datasets(
+            db,
+            project_id,
+            source_name=source_name,
+            product_key=product_key,
+            bbox=bbox,
+        )
+        transformer = Transformer.from_crs("EPSG:4326", "EPSG:31370", always_xy=True)
+        selection_metric = shapely_transform(transformer.transform, selection_geometry_4326)
+        min_x, min_y, max_x, max_y = selection_metric.bounds
+
+        try:
+            with ExitStack() as stack:
+                sources = [stack.enter_context(rasterio.open(dataset.storage_path)) for dataset in datasets]
+                invalid_sources = [
+                    index
+                    for index, source in enumerate(sources)
+                    if source.crs is None or source.crs.to_epsg() != 31370 or source.count != 1
+                ]
+                if invalid_sources:
+                    raise AppError(
+                        code="RASTER_PARTITION_MISMATCH",
+                        message="Raster partitions do not share the governed CRS and band layout",
+                        details={"invalid_partition_indexes": invalid_sources},
+                        status_code=409,
+                    )
+                target_resolution = max(abs(float(sources[0].res[0])), abs(float(sources[0].res[1])))
+                invalid_resolutions = [
+                    {
+                        "partition_index": index,
+                        "resolution": [abs(float(source.res[0])), abs(float(source.res[1]))],
+                    }
+                    for index, source in enumerate(sources)
+                    if not all(
+                        math.isclose(abs(float(value)), target_resolution, rel_tol=0.001, abs_tol=0.01)
+                        for value in source.res
+                    )
+                ]
+                if invalid_resolutions:
+                    raise AppError(
+                        code="RASTER_PARTITION_MISMATCH",
+                        message="Raster partitions do not share one analysis resolution",
+                        details={"invalid_resolutions": invalid_resolutions},
+                        status_code=409,
+                    )
+                width = max(1, math.ceil((max_x - min_x) / target_resolution))
+                height = max(1, math.ceil((max_y - min_y) / target_resolution))
+                if width * height > max_pixels:
+                    raise AppError(
+                        code="RASTER_PARTITION_SELECTION_TOO_LARGE",
+                        message="Select a smaller rectangle for regional raster analysis",
+                        details={"pixel_count": width * height, "max_pixels": max_pixels},
+                        status_code=422,
+                    )
+                mosaic, transform = merge(
+                    sources,
+                    bounds=(min_x, min_y, max_x, max_y),
+                    res=(target_resolution, target_resolution),
+                    nodata=nodata,
+                    dtype="float32",
+                )
+                values = np.asarray(mosaic[0], dtype="float64")
+                selected_cells = geometry_mask(
+                    [mapping(selection_metric)],
+                    out_shape=values.shape,
+                    transform=transform,
+                    invert=True,
+                )
+                return RasterPartitionSelection(
+                    datasets=datasets,
+                    values=values,
+                    selected_cells=selected_cells,
+                    resolution_x=target_resolution,
+                    resolution_y=target_resolution,
+                )
+        except AppError:
+            raise
+        except Exception as exc:
+            raise AppError(
+                code="RASTER_PARTITION_ANALYSIS_FAILED",
+                message="Persisted raster partitions could not be assembled for this selection",
+                details={"reason": str(exc)},
+                status_code=500,
+            ) from exc

backend/app/services/terrain_analysis_service.py

@@ -14,8 +14,15 @@ 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
-from app.schemas.dhmv import TerrainMetric, TerrainSelectionRequest, TerrainSelectionResponse, TerrainSelectionSummary
+from app.schemas.dhmv import (
+    TerrainMetric,
+    TerrainPartitionSelectionRequest,
+    TerrainSelectionRequest,
+    TerrainSelectionResponse,
+    TerrainSelectionSummary,
+)
 from app.services.dhmv_acquisition_service import DhmvAcquisitionService
+from app.services.raster_partition_analysis_service import RasterPartitionAnalysisService
 
 
 class TerrainAnalysisService:
@@ -177,6 +184,8 @@ class TerrainAnalysisService:
         selected_cell_count = int(selected_cells.sum())
         response = TerrainSelectionResponse(
             dataset_id=dataset.id,
+            dataset_ids=[dataset.id],
+            partition_count=1,
             product_key=product_key,
             surface_model=surface_model,
             selection_bbox=payload.bbox,
@@ -200,6 +209,139 @@ class TerrainAnalysisService:
         )
         return response.model_dump(mode="json")
 
+    @staticmethod
+    def analyze_partitions(
+        db,
+        project_id: UUID,
+        payload: TerrainPartitionSelectionRequest,
+        *,
+        settings: Settings | None = None,
+    ) -> dict:
+        resolved_settings = settings or get_settings()
+        product = DhmvAcquisitionService._products().get(payload.product_key.strip().lower())
+        if product is None:
+            raise AppError(
+                code="DHMV_PRODUCT_NOT_SUPPORTED",
+                message="Select a governed DHMV terrain or surface product",
+                details={"product_key": payload.product_key},
+                status_code=422,
+            )
+        selection_4326 = TerrainAnalysisService._selection_geometry(db, project_id, payload)
+        partition = RasterPartitionAnalysisService.select(
+            db,
+            project_id,
+            source_name=DhmvAcquisitionService.PROVIDER,
+            product_key=product.key,
+            selection_geometry_4326=selection_4326,
+            nodata=DhmvAcquisitionService.NODATA,
+            max_pixels=resolved_settings.dhmv_max_pixels,
+        )
+        surface_models = {
+            str((dataset.source_metadata or {}).get("surface_model") or "")
+            for dataset in partition.datasets
+        }
+        if surface_models != {product.surface_model}:
+            raise AppError(
+                code="INVALID_TERRAIN_METADATA",
+                message="DHMV partition provenance is incomplete",
+                details={"surface_models": sorted(surface_models)},
+                status_code=409,
+            )
+
+        try:
+            import numpy as np
+        except ImportError as exc:
+            raise AppError(
+                code="RASTER_PROCESSING_UNAVAILABLE",
+                message="Numpy is required for partitioned terrain analysis",
+                status_code=503,
+            ) from exc
+
+        raw = partition.values
+        invalid = ~np.isfinite(raw) | (raw == DhmvAcquisitionService.NODATA)
+        valid_mask = partition.selected_cells & ~invalid
+        values = raw[valid_mask]
+        if values.size == 0:
+            raise AppError(
+                code="TERRAIN_NO_VALID_DATA",
+                message="No valid DHMV height cells occur in this selection",
+                status_code=422,
+            )
+        slope_values = np.asarray([], dtype="float64")
+        if raw.shape[0] >= 2 and raw.shape[1] >= 2:
+            surface = np.where(valid_mask, raw, np.nan)
+            gradient_y, gradient_x = np.gradient(
+                surface,
+                partition.resolution_y,
+                partition.resolution_x,
+            )
+            slope = np.degrees(np.arctan(np.hypot(gradient_x, gradient_y)))
+            slope_values = slope[np.isfinite(slope) & valid_mask]
+
+        def metric(key: str, label: str, value: float, unit: str, method: str) -> TerrainMetric:
+            return TerrainMetric(
+                metric_key=key,
+                metric_label=label,
+                metric_value=round(float(value), 4),
+                metric_unit=unit,
+                aggregation_method=method,
+            )
+
+        prefix = "terrain" if product.surface_model == "terrain" else "surface"
+        elevation_label = (
+            "Gemiddelde maaiveldhoogte"
+            if product.surface_model == "terrain"
+            else "Gemiddelde oppervlaktehoogte"
+        )
+        metrics = [
+            metric(f"{prefix}_elevation_mean_m", elevation_label, values.mean(), "m TAW", "mean_valid_cells"),
+            metric(f"{prefix}_elevation_min_m", "Laagste hoogte", values.min(), "m TAW", "minimum_valid_cells"),
+            metric(f"{prefix}_elevation_max_m", "Hoogste hoogte", values.max(), "m TAW", "maximum_valid_cells"),
+            metric(f"{prefix}_elevation_p10_m", "10e percentiel hoogte", np.percentile(values, 10), "m TAW", "percentile_10_valid_cells"),
+            metric(f"{prefix}_elevation_p90_m", "90e percentiel hoogte", np.percentile(values, 90), "m TAW", "percentile_90_valid_cells"),
+            metric("relief_m", "Reliëfverschil", values.max() - values.min(), "m", "maximum_minus_minimum"),
+        ]
+        if slope_values.size:
+            metrics.extend(
+                [
+                    metric("slope_mean_deg", "Gemiddelde helling", slope_values.mean(), "°", "mean_finite_gradient"),
+                    metric("slope_p90_deg", "90e percentiel helling", np.percentile(slope_values, 90), "°", "percentile_90_finite_gradient"),
+                    metric("slope_max_deg", "Steilste helling", slope_values.max(), "°", "maximum_finite_gradient"),
+                ]
+            )
+        primary = metrics[0]
+        selected_cell_count = int(partition.selected_cells.sum())
+        first_dataset = partition.datasets[0]
+        response = TerrainSelectionResponse(
+            dataset_id=first_dataset.id,
+            dataset_ids=[dataset.id for dataset in partition.datasets],
+            partition_count=len(partition.datasets),
+            product_key=product.key,
+            surface_model=product.surface_model,
+            selection_bbox=payload.bbox,
+            selection_area_id=payload.area_id,
+            sample_count=int(values.size),
+            slope_sample_count=int(slope_values.size),
+            coverage_ratio=round(float(values.size / max(1, selected_cell_count)), 6),
+            resolution_m=round(max(partition.resolution_x, partition.resolution_y), 4),
+            vertical_reference=DhmvAcquisitionService.VERTICAL_REFERENCE,
+            summary=TerrainSelectionSummary(
+                metric_label=primary.metric_label,
+                metric_value=primary.metric_value,
+                metric_unit=primary.metric_unit,
+                aggregation_method=primary.aggregation_method,
+                primary_metric_key=primary.metric_key,
+                metrics=metrics,
+            ),
+            unsupported_metrics=TerrainAnalysisService.UNSUPPORTED_METRICS,
+            limitation_message=(
+                f"{TerrainAnalysisService.LIMITATION} De selectie werd exact berekend over "
+                f"{len(partition.datasets)} persistente gemeentelijke rasterpartities."
+            ),
+            generated_at=datetime.now(UTC).isoformat(),
+        )
+        return response.model_dump(mode="json")
+
     @staticmethod
     def render_png(db, project_id: UUID, dataset_id: UUID, *, max_dimension: int = 1800) -> bytes:
         dataset = TerrainAnalysisService._load_dataset(db, project_id, dataset_id)

backend/tests/test_sprint205_dhmv_terrain.py

@@ -18,7 +18,7 @@ from app.core.errors import AppError
 from app.db.session import get_db
 from app.main import app
 from app.models import Area, Dataset, DatasetVersion, Job, Project
-from app.schemas.dhmv import DhmvAcquireRequest, TerrainSelectionRequest
+from app.schemas.dhmv import DhmvAcquireRequest, TerrainPartitionSelectionRequest, TerrainSelectionRequest
 from app.services.dhmv_acquisition_service import DhmvAcquisitionService
 from app.services.terrain_analysis_service import TerrainAnalysisService
 
@@ -39,6 +39,9 @@ class FakeQuery:
     def first(self):
         return self.result
 
+    def all(self):
+        return self.result if isinstance(self.result, list) else []
+
 
 class FakeSession:
     def __init__(self, rows=None, query_result=None):
@@ -115,6 +118,23 @@ def elevation_tiff(*, left: float, top: float, width: int, height: int, resoluti
         return memory.read()
 
 
+def constant_elevation_tiff(*, left: float, top: float, value: float) -> bytes:
+    values = np.full((20, 20), value, dtype="float32")
+    with MemoryFile() as memory:
+        with memory.open(
+            driver="GTiff",
+            width=20,
+            height=20,
+            count=1,
+            dtype="float32",
+            crs="EPSG:31370",
+            transform=from_origin(left, top, 5.0, 5.0),
+            nodata=-9999.0,
+        ) as output:
+            output.write(values, 1)
+        return memory.read()
+
+
 def edge_elevation_tiff(*, left: float, top: float, x_resolution: float, y_resolution: float = 5.0) -> bytes:
     rows, columns = np.indices((20, 20))
     values = (20.0 + columns * 0.5 + rows).astype("float32")
@@ -359,6 +379,61 @@ def test_terrain_analysis_returns_governed_elevation_relief_and_slope(tmp_path)
     assert "Waterdiepte" in result["limitation_message"]
 
 
+def test_partitioned_terrain_analysis_is_exact_across_municipality_boundaries(tmp_path) -> None:
+    project_id = uuid4()
+    transformer = Transformer.from_crs("EPSG:31370", "EPSG:4326", always_xy=True)
+    min_x, min_y = transformer.transform(200_000, 210_000)
+    middle_x, _ = transformer.transform(200_100, 210_000)
+    max_x, max_y = transformer.transform(200_200, 210_100)
+    paths = [tmp_path / "left-terrain.tif", tmp_path / "right-terrain.tif"]
+    paths[0].write_bytes(constant_elevation_tiff(left=200_000, top=210_100, value=10.0))
+    paths[1].write_bytes(constant_elevation_tiff(left=200_100, top=210_100, value=20.0))
+    datasets = [
+        Dataset(
+            id=uuid4(),
+            project_id=project_id,
+            area_id=uuid4(),
+            name=path.name,
+            dataset_type="raster",
+            source="official WCS",
+            source_name="digitaal_vlaanderen_dhmv",
+            source_metadata={
+                "product_key": "dtm_1m",
+                "surface_model": "terrain",
+                "bbox_epsg4326": [left, min_y, right, max_y],
+            },
+            status="ready",
+            storage_path=str(path),
+        )
+        for path, left, right in (
+            (paths[0], min_x, middle_x),
+            (paths[1], middle_x, max_x),
+        )
+    ]
+    db = FakeSession(query_result=datasets)
+    payload = TerrainPartitionSelectionRequest(
+        bbox={"min_x": min_x, "min_y": min_y, "max_x": max_x, "max_y": max_y, "crs": "EPSG:4326"},
+        product_key="dtm_1m",
+    )
+
+    result = TerrainAnalysisService.analyze_partitions(
+        db,
+        project_id,
+        payload,
+        settings=Settings(_env_file=None),
+    )
+    metrics = {item["metric_key"]: item["metric_value"] for item in result["summary"]["metrics"]}
+
+    assert result["partition_count"] == 2
+    assert set(result["dataset_ids"]) == {str(dataset.id) for dataset in datasets}
+    assert result["sample_count"] >= 790
+    assert metrics["terrain_elevation_mean_m"] == pytest.approx(15.0, abs=0.1)
+    assert metrics["terrain_elevation_min_m"] == 10.0
+    assert metrics["terrain_elevation_max_m"] == 20.0
+    assert metrics["terrain_elevation_p90_m"] == 20.0
+    assert "2 persistente gemeentelijke rasterpartities" in result["limitation_message"]
+
+
 def test_terrain_analysis_rejects_non_dhmv_raster(tmp_path) -> None:
     project_id = uuid4()
     dataset_id = uuid4()
@@ -425,6 +500,16 @@ def test_dhmv_endpoints_use_canonical_envelopes(monkeypatch) -> None:
             "unsupported_metrics": ["water_depth_m", "water_volume_m3"],
         },
     )
+    monkeypatch.setattr(
+        TerrainAnalysisService,
+        "analyze_partitions",
+        lambda *_args, **_kwargs: {
+            "dataset_id": str(output_dataset_id),
+            "dataset_ids": [str(output_dataset_id)],
+            "partition_count": 1,
+            "sample_count": 100,
+        },
+    )
     app.dependency_overrides[get_db] = lambda: db
     try:
         products = TestClient(app).get(f"/api/v1/projects/{project_id}/datasets/dhmv/products")
@@ -436,6 +521,10 @@ def test_dhmv_endpoints_use_canonical_envelopes(monkeypatch) -> None:
             f"/api/v1/projects/{project_id}/datasets/{output_dataset_id}/raster/terrain/select",
             json={"bbox": lambert_bbox_payload().bbox.model_dump()},
         )
+        regional_terrain = TestClient(app).post(
+            f"/api/v1/projects/{project_id}/datasets/raster/terrain/select",
+            json={"bbox": lambert_bbox_payload().bbox.model_dump(), "product_key": "dtm_1m"},
+        )
     finally:
         app.dependency_overrides.clear()
 
@@ -450,6 +539,9 @@ def test_dhmv_endpoints_use_canonical_envelopes(monkeypatch) -> None:
     assert set(terrain.json()) == {"data"}
     assert terrain.json()["data"]["sample_count"] == 100
     assert terrain.json()["data"]["unsupported_metrics"] == ["water_depth_m", "water_volume_m3"]
+    assert regional_terrain.status_code == 200
+    assert set(regional_terrain.json()) == {"data"}
+    assert regional_terrain.json()["data"]["partition_count"] == 1
     assert any(isinstance(item, Job) for item in db.added)
 
 

backend/tests/test_sprint208_vmm_flood_hazard.py

@@ -16,7 +16,11 @@ from app.core.errors import AppError
 from app.db.session import get_db
 from app.main import app
 from app.models import Dataset, Job, Project
-from app.schemas.flood_hazard import FloodHazardAcquireRequest, FloodHazardSelectionRequest
+from app.schemas.flood_hazard import (
+    FloodHazardAcquireRequest,
+    FloodHazardPartitionSelectionRequest,
+    FloodHazardSelectionRequest,
+)
 from app.schemas.assistant import AssistantQueryRequest
 from app.services.geo_assistant_service import GeoAssistantService
 from app.services.flood_hazard_acquisition_service import FloodHazardAcquisitionService
@@ -120,6 +124,23 @@ def edge_depth_tiff(*, left: float, top: float, x_resolution: float, y_resolutio
         return memory.read()
 
 
+def normalized_depth_tiff(*, left: float, top: float, value: float) -> bytes:
+    values = np.full((20, 20), value, dtype="float32")
+    with MemoryFile() as memory:
+        with memory.open(
+            driver="GTiff",
+            width=20,
+            height=20,
+            count=1,
+            dtype="float32",
+            crs="EPSG:31370",
+            transform=from_origin(left, top, 5.0, 5.0),
+            nodata=-9999.0,
+        ) as output:
+            output.write(values, 1)
+        return memory.read()
+
+
 def test_flood_hazard_registry_is_complete_and_semantically_honest() -> None:
     products = FloodHazardAcquisitionService.list_products()
 
@@ -274,6 +295,61 @@ def test_flood_hazard_analysis_reports_scenario_metrics_without_claiming_waterbo
     assert "geen gelijktijdig" in result["limitation_message"]
 
 
+def test_partitioned_flood_analysis_is_exact_across_municipality_boundaries(tmp_path) -> None:
+    project_id = uuid4()
+    transformer = Transformer.from_crs("EPSG:31370", "EPSG:4326", always_xy=True)
+    min_x, min_y = transformer.transform(200_000, 210_000)
+    middle_x, _ = transformer.transform(200_100, 210_000)
+    max_x, max_y = transformer.transform(200_200, 210_100)
+    paths = [tmp_path / "left-flood.tif", tmp_path / "right-flood.tif"]
+    paths[0].write_bytes(normalized_depth_tiff(left=200_000, top=210_100, value=1.0))
+    paths[1].write_bytes(normalized_depth_tiff(left=200_100, top=210_100, value=2.0))
+    datasets = [
+        Dataset(
+            id=uuid4(),
+            project_id=project_id,
+            area_id=uuid4(),
+            name=path.name,
+            dataset_type="raster",
+            source="VMM",
+            source_name=FloodHazardAcquisitionService.PROVIDER,
+            source_metadata={
+                "product_key": "pluviaal_current_t100",
+                "normalized_value_unit": "m",
+                "bbox_epsg4326": [left, min_y, right, max_y],
+            },
+            status="ready",
+            storage_path=str(path),
+        )
+        for path, left, right in (
+            (paths[0], min_x, middle_x),
+            (paths[1], middle_x, max_x),
+        )
+    ]
+    db = FakeSession(query_result=datasets)
+    payload = FloodHazardPartitionSelectionRequest(
+        bbox={"min_x": min_x, "min_y": min_y, "max_x": max_x, "max_y": max_y, "crs": "EPSG:4326"},
+        product_key="pluviaal_current_t100",
+    )
+
+    result = FloodHazardAnalysisService.analyze_partitions(
+        db,
+        project_id,
+        payload,
+        settings=Settings(_env_file=None),
+    )
+    metrics = {item["metric_key"]: item["metric_value"] for item in result["summary"]["metrics"]}
+
+    assert result["partition_count"] == 2
+    assert set(result["dataset_ids"]) == {str(dataset.id) for dataset in datasets}
+    assert result["inundated_cell_count"] >= 790
+    assert result["inundated_fraction"] == pytest.approx(1.0)
+    assert metrics["modelled_depth_mean_m"] == pytest.approx(1.5, abs=0.01)
+    assert metrics["modelled_depth_p90_m"] == 2.0
+    assert metrics["modelled_inundated_area_ha"] == pytest.approx(2.0, abs=0.03)
+    assert "2 persistente gemeentelijke rasterpartities" in result["limitation_message"]
+
+
 def test_flood_hazard_renderer_returns_transparent_png(tmp_path) -> None:
     project_id = uuid4()
     dataset_id = uuid4()
@@ -314,6 +390,16 @@ def test_flood_hazard_api_uses_canonical_envelopes(monkeypatch) -> None:
             "unsupported_metrics": ["permanent_water_volume_m3"],
         },
     )
+    monkeypatch.setattr(
+        FloodHazardAnalysisService,
+        "analyze_partitions",
+        lambda *_args, **_kwargs: {
+            "dataset_id": str(output_dataset_id),
+            "dataset_ids": [str(output_dataset_id)],
+            "partition_count": 1,
+            "inundated_cell_count": 4,
+        },
+    )
     app.dependency_overrides[get_db] = lambda: db
     try:
         client = TestClient(app)
@@ -326,6 +412,10 @@ def test_flood_hazard_api_uses_canonical_envelopes(monkeypatch) -> None:
             f"/api/v1/projects/{project_id}/datasets/{output_dataset_id}/raster/flood-hazard/select",
             json={"bbox": flood_payload().bbox.model_dump()},
         )
+        regional_selection = client.post(
+            f"/api/v1/projects/{project_id}/datasets/raster/flood-hazard/select",
+            json={"bbox": flood_payload().bbox.model_dump(), "product_key": "pluviaal_current_t100"},
+        )
     finally:
         app.dependency_overrides.clear()
 
@@ -334,6 +424,8 @@ def test_flood_hazard_api_uses_canonical_envelopes(monkeypatch) -> None:
     assert acquisition.status_code == 200 and set(acquisition.json()) == {"data"}
     assert acquisition.json()["data"]["job_type"] == "raster.flood_hazard.acquire"
     assert selection.status_code == 200 and set(selection.json()) == {"data"}
+    assert regional_selection.status_code == 200 and set(regional_selection.json()) == {"data"}
+    assert regional_selection.json()["data"]["partition_count"] == 1
     assert any(isinstance(item, Job) for item in db.added)
 
 

backend/tests/test_sprint219_regional_raster_explorer.py

@@ -0,0 +1,52 @@
+from pathlib import Path
+
+
+ROOT = Path(__file__).resolve().parents[2]
+
+
+def test_partitioned_raster_routes_are_canonical_and_documented() -> None:
+    routes = (ROOT / "backend/app/api/routes/datasets.py").read_text(encoding="utf-8")
+    contracts = (ROOT / "docs/API_CONTRACTS.md").read_text(encoding="utf-8")
+
+    for path in (
+        "/datasets/raster/terrain/select",
+        "/datasets/raster/flood-hazard/select",
+    ):
+        assert f'@router.post("{path}", response_model=dict)' in routes
+        assert path in contracts
+    assert "envelope(TerrainAnalysisService.analyze_partitions" in routes
+    assert "envelope(FloodHazardAnalysisService.analyze_partitions" in routes
+
+
+def test_regional_map_uses_logical_partition_groups_and_exact_analysis() -> None:
+    workspace = (ROOT / "frontend/src/components/map/MapWorkspace.tsx").read_text(encoding="utf-8")
+    hook = (ROOT / "frontend/src/hooks/useMapThemeSelectionInsights.ts").read_text(encoding="utf-8")
+    api = (ROOT / "frontend/src/services/api/datasets.ts").read_text(encoding="utf-8")
+
+    assert "regionalScopeSelected" in workspace
+    assert "rasterPartitionsForDataset" in workspace
+    assert "imageOverlays={activeImageOverlays}" in workspace
+    assert "de juiste gemeentelijke rasters worden automatisch gecombineerd" in workspace
+    assert "selectTerrainPartitions" in hook
+    assert "selectFloodHazardPartitions" in hook
+    assert "/datasets/raster/terrain/select" in api
+    assert "/datasets/raster/flood-hazard/select" in api
+
+
+def test_maplibre_supports_multiple_persisted_raster_overlays() -> None:
+    map_source = (ROOT / "frontend/src/components/GeoMap.tsx").read_text(encoding="utf-8")
+
+    assert "imageOverlays?: MapImageOverlay[]" in map_source
+    assert "imageOverlayIdsRef" in map_source
+    assert "imageOverlays.forEach" in map_source
+    assert "bounded-raster-" in map_source
+
+
+def test_regional_analysis_does_not_create_an_authoritative_mosaic() -> None:
+    service = (ROOT / "backend/app/services/raster_partition_analysis_service.py").read_text(encoding="utf-8")
+    storage = (ROOT / "docs/STORAGE_ARCHITECTURE.md").read_text(encoding="utf-8")
+
+    assert "from rasterio.merge import merge" in service
+    assert "DatasetService" not in service
+    assert "12-million-cell limit" in storage
+    assert "does not create another authoritative raster" in storage

docs/API_CONTRACTS.md

@@ -257,6 +257,17 @@ in degrees. Area geometry is an exact mask, not only a bounding box.
 The response always lists `water_depth_m` and `water_volume_m3` under
 `unsupported_metrics`. Drainage is not calculated by this endpoint.
 
+### POST `/api/v1/projects/{project_id}/datasets/raster/terrain/select`
+
+Runs the same exact terrain calculation over every persisted municipal DHMV
+partition intersecting one bounded EPSG:4326 rectangle. The request adds the
+governed `product_key` (`dtm_1m` or `dsm_1m`) to the ordinary selection bbox
+and optional Area id. The backend mosaics only the intersecting windows in
+EPSG:31370, enforces the existing 12-million-cell limit and calculates global
+cell statistics. The canonical response includes `dataset_ids` and
+`partition_count`; percentiles are calculated from the combined cells and are
+not averages of municipal summaries.
+
 ### GET `/api/v1/projects/{project_id}/datasets/{dataset_id}/raster/terrain/image`
 
 Returns a browser-safe PNG colour relief for the persisted governed DHMV
@@ -316,6 +327,16 @@ it is explicitly not concurrent flood storage, permanent waterbody content,
 current water level or bathymetry. These unsupported metrics remain listed in
 the response.
 
+### POST `/api/v1/projects/{project_id}/datasets/raster/flood-hazard/select`
+
+Runs exact bounded analysis over the persisted municipal VMM partitions for
+one governed `product_key`. Only partitions intersecting the selection are
+opened, the normalized metre grids are combined at their common 5 m analysis
+resolution and the global area/depth metrics are calculated from the combined
+cells. The canonical response includes every contributing Dataset id in
+`dataset_ids` plus `partition_count`. The existing flood-volume and bathymetry
+prohibitions are unchanged.
+
 ### GET `/api/v1/projects/{project_id}/datasets/{dataset_id}/raster/flood-hazard/image`
 
 Returns a constrained transparent PNG for a persisted governed VMM flood-depth

docs/CODEX_EXECUTION_LOG.md

@@ -1,3 +1,52 @@
+## Sprint 218 Regional terrain and flood completion (2026-07-16)
+
+Changed:
+- Added the resumable 28-municipality DHMV DTM/DSM operator and executed all
+  56 governed acquisitions through the canonical API.
+- Executed all 336 governed VMM mechanism/climate/return-period combinations
+  across the same 28 persisted municipality Areas.
+- Diagnosed Retie's official WCS integer-grid edge rounding in both providers.
+  Accepted only bounded 5%/0.25 m edge drift, harmonized accepted tiles to the
+  exact requested grid and retained source resolutions, tile indexes and
+  method in provenance. A 4.5 m regression fixture remains rejected.
+- Added exact partitioned terrain and flood selection routes for bounded
+  rectangles that cross municipality boundaries. They use only persisted
+  GeoTIFFs, calculate global statistics from combined cells and retain the
+  contributing Dataset ids in the response.
+- Made the complete Kempen Area expose DHMV/VMM as logical regional MapLibre
+  layers while retaining municipality-linked storage and twelve distinct VMM
+  scenario identities.
+
+Live evidence:
+- VMM: 336 ready Datasets, 28 Areas, 12 products, 336 DatasetVersions,
+  165,277,992 bytes, zero duplicate Area/product pairs and zero missing or
+  size-mismatched files.
+- DHMV: 56 ready Datasets, 28 Areas, two products, 56 DatasetVersions,
+  282,645,991 bytes, zero duplicate Area/product pairs and zero missing or
+  size-mismatched files.
+- Every VMM Dataset has null `observed_at` and explicit false flags for
+  bathymetry, permanent depth/volume and concurrent volume. A repeated Retie
+  run reused all twelve immutable Dataset ids.
+- Tower commit `153cff0` passed container health, PostGIS 3.6, Alembic head
+  `202607160001` and frontend/API/icon proxy checks before the regional UI
+  follow-up.
+
+Validation evidence:
+- Focused DHMV/VMM and regional explorer backend suites passed, including
+  exact adjacent-partition percentile and area calculations.
+- The pre-UI-fix release gate passed 746 tests, backend compilation, API
+  contract checks, one Alembic head, frontend typecheck and production build.
+- The final local release gate passed all 752 backend tests, backend
+  compilation, 107 documented API route checks, Alembic head `202607160001`,
+  frontend typecheck and the production build. Live deployment and browser
+  evidence are recorded after the Tower rollout.
+
+Next:
+- Use the now-complete regional current-state layers as the baseline for a
+  governed refresh/change scheduler. Keep source-specific publication dates
+  and scenario semantics; do not turn VMM scenarios into a historical water
+  level series.
+
 ## Sprint 217 Regional DOV soil coverage (2026-07-16)
 
 Changed:

docs/DATA_SOURCES.md

@@ -387,7 +387,7 @@ quality metrics.
 - Cache: canonical raster Dataset plus WCS request/response/output checksums
 - Operators: `scripts/provision_mol_dhmv.py`,
   `scripts/provision_regional_dhmv.py`
-- Prioriteit: P4 uitgevoerd voor Mol en operationeel regionaal uitbreidbaar
+- Prioriteit: P4 uitgevoerd voor alle 28 Kempen-gemeenten
 
 The operator requests a bounded 5 m analysis copy by default so a complete
 municipality remains operationally manageable while retaining the official
@@ -401,7 +401,10 @@ De regionale operator gebruikt exact de 28 persistente gemeente-Areas van de
 goedgekeurde Kempen-scope en plant twee outputs per gemeente. Die 56
 gemeentepartities vermijden een onnodig monolithisch hoogtebestand, blijven
 binnen WCS/pixelgrenzen en sluiten aan op de gebiedsgebonden datasetselectie in
-de kaart. Herhaalruns gebruiken de bestaande checksummed requestcache.
+de kaart. De live matrix bevat 56 geverifieerde Datasets en DatasetVersions.
+De regionale kaart combineert alleen de partities die een getekende selectie
+raken; globale statistieken worden uit de samengevoegde cellen berekend.
+Herhaalruns gebruiken de bestaande checksummed requestcache.
 
 ## VMM overstromingsgevaarkaarten
 
@@ -415,8 +418,9 @@ de kaart. Herhaalruns gebruiken de bestaande checksummed requestcache.
 - Publicatie: 2019/2021 afhankelijk van product; scenario-identiteit is
   leidend en wordt niet als observatiedatum opgeslagen
 - Cache: canonical raster Dataset plus request/response/output checksums
-- Operator: `scripts/provision_mol_flood_hazards.py`
+- Operators: `scripts/provision_mol_flood_hazards.py`,
-- Prioriteit: P5 scenariofundament uitgevoerd voor Mol
+  `scripts/provision_regional_flood_hazards.py`
+- Prioriteit: P5 scenariofundament uitgevoerd voor alle 28 Kempen-gemeenten
 
 VMM beschrijft deze lagen als maximale lokale waterdiepte tussen wateroppervlak
 en maaiveld voor een gekozen kans- en klimaatscenario. GeoIntel converteert
@@ -441,15 +445,17 @@ scenario uitsluitend de bestaande canonical API aan:
 - `POST /api/v1/projects/{project_id}/datasets/{dataset_id}/raster/flood-hazard/select`
 
 Deze gemeentepartities zijn bewust. Een volledig regionaal raster in een
-aanvraag zou de publieke WCS- en pixelgrenzen onnodig belasten. In de UI wordt
+aanvraag zou de publieke WCS- en pixelgrenzen onnodig belasten. In de UI blijft
-een VMM-dataset alleen als overstromingslaag getoond voor het actieve
+een gemeente gekoppeld aan haar eigen bestand. Op het volledige Kempen-gebied
-werkgebied waaraan die dataset gekoppeld is. Zo blijft Mol bij Mol, Geel bij
+worden de 28 partities als één logische scenario-laag getoond. Een getekende
-Geel, enzovoort.
+rechthoek opent alleen de rakende partities en berekent globale statistieken
+uit de werkelijk samengevoegde cellen.
 
 De regionale operator ondersteunt `--dry-run`, `--members` en `--products`.
-Een volledige scope met alle twaalf scenario's plant 336 gecontroleerde
+De uitgevoerde volledige scope bevat 336 gecontroleerde Datasets en 336
-acquisities. Herhaalruns gebruiken bestaande checksummed Datasets via de
+DatasetVersions zonder ontbrekende gemeente/scenario-combinaties. Herhaalruns
-backend-cache zolang de requestidentiteit niet verandert.
+gebruiken bestaande checksummed Datasets via de backend-cache zolang de
+requestidentiteit niet verandert.
 
 Ook regionaal blijft de semantiek onveranderd: VMM-waterdiepte is een
 gemodelleerde maximale lokale diepte per kans- en klimaatscenario. GeoIntel kan

docs/STORAGE_ARCHITECTURE.md

@@ -134,6 +134,14 @@ URLs, response/coverage/normalized checksums, EPSG:31370 bounds, scenario
 metadata and explicit unsupported-volume flags. Repeat runs reuse matching
 ready Datasets through the acquisition service cache.
 
+The regional Map workspace does not create another authoritative raster or
+copy pixels into PostgreSQL. Its partition-selection endpoints read only the
+municipal GeoTIFF windows intersecting a bounded selection, mosaic those
+windows in memory at the governed analysis resolution and return metrics plus
+the complete contributing `dataset_ids`. The 12-million-cell limit applies to
+the combined window. Persisted files, checksums and DatasetVersions remain the
+only authoritative artifacts.
+
 BWK/Natura 2000 evidence lives under
 `storage/operator-evidence/bwk-natura2000-2025/mol/`. The `raw/` directory
 contains immutable WFS pages; the adjacent manifest records their URLs,

docs/TODO.md

@@ -36,7 +36,8 @@
 - [x] Persist one bounded whole-region snapshot per thematic raster so drawn selections can cross municipality boundaries without changing source semantics.
 - [x] Generalize the DOV soil-map operator to all 28 approved Kempen municipality partitions with one regional snapshot manifest.
 - [x] Add a resumable regional DHMV DTM/DSM operator for all 28 approved Kempen municipality Areas.
-- [ ] Execute and audit the complete 336-product VMM and 56-product DHMV regional runtime matrices.
+- [x] Execute and audit the complete 336-product VMM and 56-product DHMV regional runtime matrices.
+- [x] Present municipal DHMV/VMM partitions as logical regional layers and analyse cross-boundary rectangles without a municipality prerequisite.
 
 ## Governed source expansion backlog
 

frontend/README.md

@@ -519,12 +519,14 @@ GeoJSON export stays disabled. The UI never labels the maximum-depth area
 integral as current, permanent or concurrent water volume.
 
 Regional VMM provisioning creates one scenario raster per municipality Area.
-The explorer therefore shows only the flood scenarios whose `area_id` matches
+For a municipality the explorer still uses only that exact Area-linked file.
-the active work area. This avoids presenting a Mol scenario while the map is
+For the complete Kempen Area it presents the 28 VMM and DHMV partitions as one
-focused on another municipality. The region-wide Area remains the navigation
+logical map layer, deduplicates VMM into twelve scenario choices and renders
-context; municipality Areas are the analysis scope for flood rasters because
+every matching MapLibre image partition. A drawn rectangle is sent to the
-the public WCS and raster cell limits make one monolithic Kempen raster
+partition endpoint, which opens only intersecting files and calculates exact
-operationally unsafe.
+combined cell statistics. A monolithic full-region 5 m calculation remains
+disabled because it exceeds the governed raster-cell limit; users draw a
+bounded rectangle without first choosing a municipality.
 
 ## Useful repository scripts
 

frontend/src/components/GeoMap.tsx

@@ -13,7 +13,7 @@ interface GeoMapProps {
   selectedFeature?: GeoJSON.Feature | null
   selectionData?: GeoJSON.FeatureCollection | null
   qaEvidenceData?: GeoJSON.FeatureCollection | null
-  imageOverlay?: MapImageOverlay | null
+  imageOverlays?: MapImageOverlay[]
   selectionBbox?: { min_x: number; min_y: number; max_x: number; max_y: number } | null
   bboxSelectionMode?: boolean
   visible?: boolean
@@ -154,7 +154,7 @@ function GeoMap({
   selectedFeature = null,
   selectionData = null,
   qaEvidenceData = null,
-  imageOverlay = null,
+  imageOverlays = [],
   selectionBbox = null,
   bboxSelectionMode = false,
   visible = true,
@@ -180,6 +180,7 @@ function GeoMap({
   const dataRef = useRef<GeoJSON.FeatureCollection | null>(data)
   const fitDataOnChangeRef = useRef(fitDataOnChange)
   const lastFittedAreaRef = useRef<GeoJSON.FeatureCollection | null>(null)
+  const imageOverlayIdsRef = useRef<string[]>([])
   const [mapStyleReady, setMapStyleReady] = useState(false)
 
   areaDataRef.current = areaData
@@ -353,17 +354,20 @@ function GeoMap({
     if (!map || !mapStyleReady || !map.isStyleLoaded()) {
       return
     }
-    if (map.getLayer('bounded-orthophoto')) {
+    for (const overlayId of [...imageOverlayIdsRef.current].reverse()) {
-      map.removeLayer('bounded-orthophoto')
+      if (map.getLayer(overlayId)) {
+        map.removeLayer(overlayId)
       }
-    if (map.getSource('bounded-orthophoto')) {
+      if (map.getSource(overlayId)) {
-      map.removeSource('bounded-orthophoto')
+        map.removeSource(overlayId)
       }
-    if (!imageOverlay) {
-      return
     }
+    imageOverlayIdsRef.current = []
+    const beforeLayer = ['area-fill', 'dataset-fill', 'selection-bbox-fill'].find((layerId) => map.getLayer(layerId))
+    imageOverlays.forEach((imageOverlay, index) => {
+      const overlayId = `bounded-raster-${index}`
       const [minX, minY, maxX, maxY] = imageOverlay.bbox
-    map.addSource('bounded-orthophoto', {
+      map.addSource(overlayId, {
         type: 'image',
         url: imageOverlay.url,
         coordinates: [
@@ -373,17 +377,15 @@ function GeoMap({
           [minX, minY],
         ],
       })
-    const beforeLayer = ['area-fill', 'dataset-fill', 'selection-bbox-fill'].find((layerId) => map.getLayer(layerId))
+      map.addLayer({
-    map.addLayer(
+        id: overlayId,
-      {
-        id: 'bounded-orthophoto',
         type: 'raster',
-        source: 'bounded-orthophoto',
+        source: overlayId,
         paint: { 'raster-opacity': imageOverlay.opacity ?? 0.88 },
-      },
+      }, beforeLayer)
-      beforeLayer,
+      imageOverlayIdsRef.current.push(overlayId)
-    )
+    })
-  }, [imageOverlay, mapStyleReady])
+  }, [imageOverlays, mapStyleReady])
 
   useEffect(() => {
     const map = mapRef.current

frontend/src/components/map/MapWorkspace.tsx

@@ -158,14 +158,15 @@ const DATA_THEME_MAP_STYLES: Record<DataThemeId, { fill: string; line: string }>
   parcels: { fill: '#a7792f', line: '#7d571f' },
 }
 
-function datasetAvailabilityLabel(dataset: DatasetCreateResponse): string {
+function datasetAvailabilityLabel(dataset: DatasetCreateResponse, partitionCount = 1): string {
+  const regionalSuffix = partitionCount > 1 ? ` · ${partitionCount} gemeenten` : ''
   if (dataset.dataset_type === 'raster' && dataset.source_name === 'digitaal_vlaanderen_dhmv') {
     const resolution = Number(dataset.source_metadata?.['analysis_resolution_m'])
-    return `${Number.isFinite(resolution) ? `${resolution.toLocaleString('nl-BE')} m` : 'Raster'} hoogtegrid beschikbaar`
+    return `${Number.isFinite(resolution) ? `${resolution.toLocaleString('nl-BE')} m` : 'Raster'} hoogtegrid${regionalSuffix}`
   }
   if (dataset.dataset_type === 'raster' && dataset.source_name === 'vmm_flood_hazard') {
     const resolution = Number(dataset.source_metadata?.['analysis_resolution_m'])
-    return `${Number.isFinite(resolution) ? `${resolution.toLocaleString('nl-BE')} m` : 'Raster'} overstromingsscenario`
+    return `${Number.isFinite(resolution) ? `${resolution.toLocaleString('nl-BE')} m` : 'Raster'} overstromingsscenario${regionalSuffix}`
   }
   if (dataset.dataset_type === 'raster' && dataset.source_name === 'department_omgeving_thematic_raster') {
     const resolution = Number(dataset.source_metadata?.['analysis_resolution_m'])
@@ -213,21 +214,69 @@ function datasetMatchesTheme(dataset: DatasetCreateResponse, theme: DataTheme):
   return theme.tokens.some((token) => searchText.includes(token))
 }
 
-function datasetCoversSelectedArea(dataset: DatasetCreateResponse, selectedAreaId: string | null): boolean {
+function isMunicipalityAreaName(name: string | null | undefined): boolean {
+  return /^Gemeente\s/i.test(name ?? '')
+}
+
+function isPartitionedRaster(dataset: DatasetCreateResponse | null | undefined): boolean {
+  return Boolean(
+    dataset?.dataset_type === 'raster'
+      && ['digitaal_vlaanderen_dhmv', 'vmm_flood_hazard'].includes(dataset.source_name ?? ''),
+  )
+}
+
+function datasetProductKey(dataset: DatasetCreateResponse): string {
+  return String(dataset.source_metadata?.['product_key'] ?? '')
+}
+
+function datasetCoversSelectedArea(
+  dataset: DatasetCreateResponse,
+  selectedAreaId: string | null,
+  regionalScope = false,
+): boolean {
   const coverageScope = String(dataset.source_metadata?.['coverage_scope'] ?? '')
   if (coverageScope !== 'municipality' || !dataset.area_id) {
     return true
   }
+  if (regionalScope) {
+    return true
+  }
   return Boolean(selectedAreaId) && dataset.area_id === selectedAreaId
 }
 
+function rasterPartitionsForDataset(
+  datasets: DatasetCreateResponse[],
+  representative: DatasetCreateResponse | null,
+  selectedAreaId: string | null,
+  regionalScope: boolean,
+): DatasetCreateResponse[] {
+  if (!representative) {
+    return []
+  }
+  if (!regionalScope || !isPartitionedRaster(representative)) {
+    return [representative]
+  }
+  const productKey = datasetProductKey(representative)
+  return datasets
+    .filter(
+      (dataset) =>
+        dataset.source_name === representative.source_name
+        && datasetProductKey(dataset) === productKey
+        && datasetCoversSelectedArea(dataset, selectedAreaId, true),
+    )
+    .sort((left, right) => String(left.area_id ?? '').localeCompare(String(right.area_id ?? '')))
+}
+
 function pickThemeDataset(
   datasets: DatasetCreateResponse[],
   theme: DataTheme,
   selectedAreaId: string | null,
+  regionalScope = false,
 ): DatasetCreateResponse | null {
   const candidates = datasets.filter(
-    (dataset) => datasetMatchesTheme(dataset, theme) && datasetCoversSelectedArea(dataset, selectedAreaId),
+    (dataset) =>
+      datasetMatchesTheme(dataset, theme)
+      && datasetCoversSelectedArea(dataset, selectedAreaId, regionalScope),
   )
   candidates.sort((left, right) => {
     const score = (dataset: DatasetCreateResponse) =>
@@ -745,6 +794,7 @@ export function MapWorkspace({
   const [fullWorkflowError, setFullWorkflowError] = useState<string | null>(null)
   const [fullWorkflowMode, setFullWorkflowMode] = useState<'new' | 'reuse'>('new')
   const selectedMapArea = areas.find((area) => area.id === selectedMapAreaId)
+  const regionalScopeSelected = Boolean(selectedMapArea && !isMunicipalityAreaName(selectedMapArea.name))
   const featureProperties = selectedMapFeature?.properties ?? null
   const featureSummaryEntries = featureProperties
     ? Object.entries(featureProperties)
@@ -767,70 +817,138 @@ export function MapWorkspace({
   const selectedMapDataset = availableMapDatasets.find((dataset) => dataset.id === selectedMapDatasetId) ?? null
   const usesDefaultOsmBasemap = !import.meta.env.VITE_MAP_STYLE_URL
   const floodHazardDatasets = useMemo(
-    () => availableMapDatasets
+    () => {
-      .filter((dataset) => dataset.source_name === 'vmm_flood_hazard' && datasetCoversSelectedArea(dataset, selectedMapAreaId))
+      const scoped = availableMapDatasets
-      .sort((left, right) => floodScenarioLabel(left).localeCompare(floodScenarioLabel(right), 'nl')),
+        .filter(
-    [availableMapDatasets, selectedMapAreaId],
+          (dataset) =>
+            dataset.source_name === 'vmm_flood_hazard'
+            && datasetCoversSelectedArea(dataset, selectedMapAreaId, regionalScopeSelected),
+        )
+        .sort((left, right) => floodScenarioLabel(left).localeCompare(floodScenarioLabel(right), 'nl'))
+      if (!regionalScopeSelected) {
+        return scoped
+      }
+      const products = new Map<string, DatasetCreateResponse>()
+      for (const dataset of scoped) {
+        const key = datasetProductKey(dataset)
+        if (key && !products.has(key)) {
+          products.set(key, dataset)
+        }
+      }
+      return Array.from(products.values())
+    },
+    [availableMapDatasets, regionalScopeSelected, selectedMapAreaId],
   )
   const themeDatasetMap = useMemo(() => {
     const result = Object.fromEntries(
-      DATA_THEMES.map((theme) => [theme.id, pickThemeDataset(availableMapDatasets, theme, selectedMapAreaId)]),
+      DATA_THEMES.map((theme) => [
+        theme.id,
+        pickThemeDataset(availableMapDatasets, theme, selectedMapAreaId, regionalScopeSelected),
+      ]),
     ) as Record<DataThemeId, DatasetCreateResponse | null>
     const selectedFloodHazard = floodHazardDatasets.find((dataset) => dataset.id === selectedFloodHazardDatasetId)
     if (selectedFloodHazard) {
       result.flood_hazard = selectedFloodHazard
     }
     return result
-  }, [availableMapDatasets, floodHazardDatasets, selectedFloodHazardDatasetId, selectedMapAreaId])
+  }, [availableMapDatasets, floodHazardDatasets, regionalScopeSelected, selectedFloodHazardDatasetId, selectedMapAreaId])
+  const themePartitionMap = useMemo(
+    () =>
+      Object.fromEntries(
+        DATA_THEMES.map((theme) => [
+          theme.id,
+          rasterPartitionsForDataset(
+            availableMapDatasets,
+            themeDatasetMap[theme.id],
+            selectedMapAreaId,
+            regionalScopeSelected,
+          ),
+        ]),
+      ) as Record<DataThemeId, DatasetCreateResponse[]>,
+    [availableMapDatasets, regionalScopeSelected, selectedMapAreaId, themeDatasetMap],
+  )
   const activeTheme = DATA_THEMES.find((theme) => theme.id === activeThemeId) ?? DATA_THEMES[0]
   const activeThemeMapStyle = DATA_THEME_MAP_STYLES[activeTheme.id]
   const analysisOverlayActive = mapContentMode === 'analysis' && analysisLayerAvailable && Boolean(mapFeatureCollection)
   const selectedOrthophotoProduct = orthophotoProducts.find((item) => item.key === selectedOrthophotoProductKey) ?? null
-  const orthophotoImageOverlay = orthophotoResult && orthophotoImageUrl && orthophotoResult.bbox_epsg4326.length === 4
+  const orthophotoImageOverlay = useMemo(
+    () => orthophotoResult && orthophotoImageUrl && orthophotoResult.bbox_epsg4326.length === 4
       ? {
           url: orthophotoImageUrl,
           bbox: orthophotoResult.bbox_epsg4326 as [number, number, number, number],
           label: orthophotoResult.display_name,
           opacity: 0.9,
         }
-    : null
+      : null,
+    [orthophotoImageUrl, orthophotoResult],
+  )
   const activeThemeDataset = themeDatasetMap[activeTheme.id]
-  const terrainBounds = activeThemeDataset?.source_name === 'digitaal_vlaanderen_dhmv'
+  const activeThemePartitions = themePartitionMap[activeTheme.id]
-    ? activeThemeDataset.source_metadata?.['bbox_epsg4326']
+  const regionalRasterThemeActive = regionalScopeSelected && isPartitionedRaster(activeThemeDataset)
-    : null
+  const terrainImageOverlays = useMemo(
-  const terrainImageOverlay = activeTheme.id === 'elevation' && activeThemeDataset && selectedProjectId && Array.isArray(terrainBounds) && terrainBounds.length === 4
+    () =>
-    ? {
+      activeTheme.id === 'elevation' && selectedProjectId
-        url: terrainImageUrl(selectedProjectId, activeThemeDataset.id),
+        ? activeThemePartitions.flatMap((dataset) => {
-        bbox: terrainBounds.map(Number) as [number, number, number, number],
+            const bounds = dataset.source_metadata?.['bbox_epsg4326']
-        label: getDatasetDisplayName(activeThemeDataset),
+            return dataset.source_name === 'digitaal_vlaanderen_dhmv'
+              && Array.isArray(bounds)
+              && bounds.length === 4
+              ? [{
+                  url: terrainImageUrl(selectedProjectId, dataset.id),
+                  bbox: bounds.map(Number) as [number, number, number, number],
+                  label: getDatasetDisplayName(dataset),
                   opacity: 0.82,
-      }
+                }]
-    : null
+              : []
-  const floodHazardBounds = activeThemeDataset?.source_name === 'vmm_flood_hazard'
+          })
-    ? activeThemeDataset.source_metadata?.['bbox_epsg4326']
+        : [],
-    : null
+    [activeTheme.id, activeThemePartitions, selectedProjectId],
-  const floodHazardImageOverlay = activeTheme.id === 'flood_hazard' && activeThemeDataset && selectedProjectId && Array.isArray(floodHazardBounds) && floodHazardBounds.length === 4
+  )
-    ? {
+  const floodHazardImageOverlays = useMemo(
-        url: floodHazardImageUrl(selectedProjectId, activeThemeDataset.id),
+    () =>
-        bbox: floodHazardBounds.map(Number) as [number, number, number, number],
+      activeTheme.id === 'flood_hazard' && selectedProjectId
-        label: floodScenarioLabel(activeThemeDataset),
+        ? activeThemePartitions.flatMap((dataset) => {
+            const bounds = dataset.source_metadata?.['bbox_epsg4326']
+            return dataset.source_name === 'vmm_flood_hazard'
+              && Array.isArray(bounds)
+              && bounds.length === 4
+              ? [{
+                  url: floodHazardImageUrl(selectedProjectId, dataset.id),
+                  bbox: bounds.map(Number) as [number, number, number, number],
+                  label: floodScenarioLabel(dataset),
                   opacity: 0.82,
-      }
+                }]
-    : null
+              : []
+          })
+        : [],
+    [activeTheme.id, activeThemePartitions, selectedProjectId],
+  )
   const thematicRasterBounds = activeThemeDataset?.source_name === 'department_omgeving_thematic_raster'
     ? activeThemeDataset.source_metadata?.['bbox_epsg4326']
     : null
-  const thematicRasterImageOverlay = activeThemeDataset?.source_name === 'department_omgeving_thematic_raster' && selectedProjectId && Array.isArray(thematicRasterBounds) && thematicRasterBounds.length === 4
+  const thematicRasterImageOverlays = useMemo(
-    ? {
+    () => activeThemeDataset?.source_name === 'department_omgeving_thematic_raster' && selectedProjectId && Array.isArray(thematicRasterBounds) && thematicRasterBounds.length === 4
+      ? [{
           url: thematicRasterImageUrl(selectedProjectId, activeThemeDataset.id),
           bbox: thematicRasterBounds.map(Number) as [number, number, number, number],
           label: getDatasetDisplayName(activeThemeDataset),
           opacity: 0.78,
-      }
+        }]
-    : null
+      : [],
+    [activeThemeDataset, selectedProjectId, thematicRasterBounds],
+  )
   const thematicLegendMin = String(activeThemeDataset?.source_metadata?.['legend_min_label'] ?? 'Lagere waarde')
   const thematicLegendMax = String(activeThemeDataset?.source_metadata?.['legend_max_label'] ?? 'Hogere waarde')
-  const activeImageOverlay = thematicRasterImageOverlay ?? floodHazardImageOverlay ?? terrainImageOverlay ?? orthophotoImageOverlay
+  const activeImageOverlays = useMemo(
+    () => thematicRasterImageOverlays.length > 0
+      ? thematicRasterImageOverlays
+      : floodHazardImageOverlays.length > 0
+        ? floodHazardImageOverlays
+        : terrainImageOverlays.length > 0
+          ? terrainImageOverlays
+          : orthophotoImageOverlay ? [orthophotoImageOverlay] : [],
+    [floodHazardImageOverlays, orthophotoImageOverlay, terrainImageOverlays, thematicRasterImageOverlays],
+  )
   const activeScopeProject = projects.find((project) => project.id === selectedProjectId) ?? null
   const activeScopeLabel = activeScopeProject ? operationalScopeProjectLabel(activeScopeProject) : 'Werkgebied'
   const municipalityAreaCount = areas.filter((area) => /^Gemeente\s/i.test(area.name)).length
@@ -865,7 +983,7 @@ export function MapWorkspace({
     [themeInsights],
   )
   const activeSelectionResult = themeResults.find((item) => item.theme.id === activeThemeId)?.result
-    ?? (selectedMapDataset?.id === activeThemeDataset?.id ? mapSelectionResult : null)
+    ?? (!regionalRasterThemeActive && selectedMapDataset?.id === activeThemeDataset?.id ? mapSelectionResult : null)
   const selectedAreaSquareMetres = useMemo(
     () =>
       bboxesEqual(mapSelectionBbox, selectedAreaBbox) && selectedMapArea?.area_m2
@@ -1106,14 +1224,23 @@ export function MapWorkspace({
   const loadAllThemeResults = async (bbox: VectorSelectionBBox, areaId?: string) => {
     const availableThemes = DATA_THEMES.flatMap((theme) => {
       const dataset = themeDatasetMap[theme.id]
-      return dataset ? [{ themeId: theme.id, dataset }] : []
+      return dataset
+        ? [{
+            themeId: theme.id,
+            dataset,
+            partitioned: regionalScopeSelected && isPartitionedRaster(dataset),
+          }]
+        : []
     })
     await loadThemeInsights(bbox, availableThemes, areaId)
   }
 
   const analyzeSelection = async (bbox: VectorSelectionBBox, areaId?: string) => {
     setSelectionBbox(bbox)
-    const tasks: Array<Promise<unknown>> = [onRunMapSelectionExtract(bbox, areaId), loadAllThemeResults(bbox, areaId)]
+    const tasks: Array<Promise<unknown>> = [loadAllThemeResults(bbox, areaId)]
+    if (!regionalRasterThemeActive) {
+      tasks.push(onRunMapSelectionExtract(bbox, areaId))
+    }
     if (analysisMode === 'evolution' && earlierDatasetId && laterDatasetId) {
       tasks.push(compareTemporalSnapshots(earlierDatasetId, laterDatasetId, bbox, areaId))
     }
@@ -1267,6 +1394,7 @@ export function MapWorkspace({
             <div className="geo-theme-list">
               {DATA_THEMES.map((theme) => {
                 const dataset = themeDatasetMap[theme.id]
+                const partitionCount = themePartitionMap[theme.id].length
                 const temporalGroups = themeTemporalSeriesMap[theme.id]
                 const temporalGroup = temporalGroups[0]
                 const evolutionAvailable = temporalGroups.some((group) => group.items.length >= 2)
@@ -1296,7 +1424,7 @@ export function MapWorkspace({
                               ? 'Alleen huidige toestand'
                               : 'Bron nog niet ingeladen'
                           : dataset
-                            ? datasetAvailabilityLabel(dataset)
+                            ? datasetAvailabilityLabel(dataset, partitionCount)
                             : 'Bron nog niet ingeladen'}
                       </small>
                     </span>
@@ -1416,7 +1544,13 @@ export function MapWorkspace({
                 <span>2</span>
                 <div>
                   <h3>Selecteer een gebied</h3>
-                  <p>{bboxSelectionMode ? 'Sleep nu een rechthoek op de kaart.' : 'Sleep een rechthoek of analyseer het volledige werkgebied.'}</p>
+                  <p>
+                    {bboxSelectionMode
+                      ? 'Sleep nu een rechthoek op de kaart.'
+                      : regionalRasterThemeActive
+                        ? 'Teken een rechthoek; de juiste gemeentelijke rasters worden automatisch gecombineerd.'
+                        : 'Sleep een rechthoek of analyseer het volledige werkgebied.'}
+                  </p>
                 </div>
               </div>
               <div className="geo-map-actions">
@@ -1430,11 +1564,12 @@ export function MapWorkspace({
                 </button>
                 <button
                   className="secondary-action"
-                  disabled={!activeThemeDataset || (analysisMode === 'evolution' && activeTemporalSeries.length < 2) || !selectedAreaBbox || mapSelectionLoading || themeResultsLoading}
+                  disabled={!activeThemeDataset || regionalRasterThemeActive || (analysisMode === 'evolution' && activeTemporalSeries.length < 2) || !selectedAreaBbox || mapSelectionLoading || themeResultsLoading}
                   type="button"
+                  title={regionalRasterThemeActive ? 'Teken een begrensde rechthoek voor een regionale rasteranalyse.' : undefined}
                   onClick={() => selectedAreaBbox && void analyzeSelection(selectedAreaBbox, selectedMapArea?.id)}
                 >
-                  Volledig werkgebied
+                  {regionalRasterThemeActive ? 'Selecteer een deelgebied' : 'Volledig werkgebied'}
                 </button>
                 <button className="secondary-action" disabled={!mapSelectionBbox} type="button" onClick={clearAreaSelection}>
                   Wis selectie
@@ -1450,7 +1585,7 @@ export function MapWorkspace({
                 areaData={areaFeatureCollection}
                 selectedFeature={selectedFeature}
                 selectionData={analysisMode === 'current' ? mapSelectionResult?.geojson ?? null : null}
-                imageOverlay={activeImageOverlay}
+                imageOverlays={activeImageOverlays}
                 selectionBbox={mapSelectionBbox}
                 bboxSelectionMode={bboxSelectionMode}
                 visible={mapLayerVisible}
@@ -1466,12 +1601,17 @@ export function MapWorkspace({
               />
               <div className="geo-map-legend" aria-label="Kaartlegende">
                 <span><i className="geo-legend-area" /> Werkgebied</span>
-                {thematicRasterImageOverlay ? (
+                {thematicRasterImageOverlays.length > 0 ? (
                   <span className="geo-legend-thematic">
                     <i className={`geo-legend-ramp geo-legend-ramp-${activeTheme.id}`} />
                     <small>{thematicLegendMin} → {thematicLegendMax}</small>
                   </span>
-                ) : activeImageOverlay ? <span><i className="geo-legend-imagery" /> {activeImageOverlay.label}</span> : null}
+                ) : activeImageOverlays.length > 0 ? (
+                  <span>
+                    <i className="geo-legend-imagery" /> {activeImageOverlays[0].label}
+                    {activeImageOverlays.length > 1 ? ` · ${activeImageOverlays.length} gemeenten` : ''}
+                  </span>
+                ) : null}
                 {analysisOverlayActive ? (
                   <>
                     <span><i className="geo-legend-layer geo-legend-layer-buildings" /> AI-kandidaten</span>

frontend/src/hooks/useMapThemeSelectionInsights.ts

@@ -9,6 +9,7 @@ import { thematicRasterSelectionToMapSelection } from '../lib/thematicRaster'
 export interface MapThemeQuery<TThemeId extends string> {
   themeId: TThemeId
   dataset: DatasetCreateResponse
+  partitioned?: boolean
 }
 
 export interface MapThemeInsight<TThemeId extends string> extends MapThemeQuery<TThemeId> {
@@ -54,19 +55,35 @@ export function useMapThemeSelectionInsights<TThemeId extends string>(
     setThemeInsightsError(null)
     try {
       const settled = await Promise.allSettled(
-        queries.map(async ({ themeId, dataset }) => ({
+        queries.map(async ({ themeId, dataset, partitioned }) => ({
           themeId,
           dataset,
           result: dataset.dataset_type === 'raster' && dataset.source_name === 'digitaal_vlaanderen_dhmv'
-            ? terrainSelectionToMapSelection(await datasetsApi.selectTerrain(selectedProjectId, dataset.id, {
+            ? terrainSelectionToMapSelection(
+                partitioned
+                  ? await datasetsApi.selectTerrainPartitions(selectedProjectId, {
                       bbox,
                       area_id: areaId,
-              }))
+                      product_key: String(dataset.source_metadata?.['product_key'] ?? 'dtm_1m'),
+                    })
+                  : await datasetsApi.selectTerrain(selectedProjectId, dataset.id, {
+                      bbox,
+                      area_id: areaId,
+                    }),
+              )
             : dataset.dataset_type === 'raster' && dataset.source_name === 'vmm_flood_hazard'
-              ? floodHazardSelectionToMapSelection(await datasetsApi.selectFloodHazard(selectedProjectId, dataset.id, {
+              ? floodHazardSelectionToMapSelection(
+                  partitioned
+                    ? await datasetsApi.selectFloodHazardPartitions(selectedProjectId, {
                         bbox,
                         area_id: areaId,
-                }))
+                        product_key: String(dataset.source_metadata?.['product_key'] ?? 'pluviaal_current_t100'),
+                      })
+                    : await datasetsApi.selectFloodHazard(selectedProjectId, dataset.id, {
+                        bbox,
+                        area_id: areaId,
+                      }),
+                )
               : dataset.dataset_type === 'raster' && dataset.source_name === 'department_omgeving_thematic_raster'
                 ? thematicRasterSelectionToMapSelection(await datasetsApi.selectThematicRaster(selectedProjectId, dataset.id, {
                     bbox,

frontend/src/services/api/datasets.ts

@@ -134,6 +134,11 @@ export const datasetsApi = {
     payload: { bbox: VectorSelectionRequest['bbox']; area_id?: string },
   ): Promise<TerrainSelectionResponse> =>
     apiPost<TerrainSelectionResponse>(`/api/v1/projects/${projectId}/datasets/${datasetId}/raster/terrain/select`, payload),
+  selectTerrainPartitions: (
+    projectId: string,
+    payload: { bbox: VectorSelectionRequest['bbox']; area_id?: string; product_key: string },
+  ): Promise<TerrainSelectionResponse> =>
+    apiPost<TerrainSelectionResponse>(`/api/v1/projects/${projectId}/datasets/raster/terrain/select`, payload),
   acquireFloodHazard: (projectId: string, payload: FloodHazardAcquireRequest): Promise<JobRead> =>
     apiPost<JobRead>(`/api/v1/projects/${projectId}/datasets/flood-hazard/acquire`, payload),
   listFloodHazardProducts: (projectId: string): Promise<{ items: FloodHazardProductRead[]; total: number }> =>
@@ -144,6 +149,11 @@ export const datasetsApi = {
     payload: { bbox: VectorSelectionRequest['bbox']; area_id?: string },
   ): Promise<FloodHazardSelectionResponse> =>
     apiPost<FloodHazardSelectionResponse>(`/api/v1/projects/${projectId}/datasets/${datasetId}/raster/flood-hazard/select`, payload),
+  selectFloodHazardPartitions: (
+    projectId: string,
+    payload: { bbox: VectorSelectionRequest['bbox']; area_id?: string; product_key: string },
+  ): Promise<FloodHazardSelectionResponse> =>
+    apiPost<FloodHazardSelectionResponse>(`/api/v1/projects/${projectId}/datasets/raster/flood-hazard/select`, payload),
   acquireThematicRaster: (projectId: string, payload: ThematicRasterAcquireRequest): Promise<JobRead> =>
     apiPost<JobRead>(`/api/v1/projects/${projectId}/datasets/thematic-raster/acquire`, payload),
   listThematicRasterProducts: (projectId: string): Promise<{ items: ThematicRasterProductRead[]; total: number }> =>

frontend/src/types.ts

@@ -360,6 +360,8 @@ export interface DhmvProductRead {
 
 export interface TerrainSelectionResponse {
   dataset_id: string
+  dataset_ids: string[]
+  partition_count: number
   product_key: string
   surface_model: 'terrain' | 'surface'
   selection_bbox: VectorSelectionBBox
@@ -410,6 +412,8 @@ export interface FloodHazardProductRead {
 
 export interface FloodHazardSelectionResponse {
   dataset_id: string
+  dataset_ids: string[]
+  partition_count: number
   product_key: string
   mechanism: 'pluviaal' | 'fluviaal'
   climate_context: string

scripts/README.md

@@ -1582,6 +1582,10 @@ not assemble a monolithic Kempen height raster, does not claim annual terrain
 change and rejects any terrain-analysis response that stops listing water
 depth and water volume as unsupported.
 
+The live completed matrix contains 56/56 ready Dataset/DatasetVersion pairs.
+The regional Map workspace reads intersecting partitions through the bounded
+partition-selection endpoint; operator storage remains unchanged.
+
 ## Mol VMM flood-hazard scenarios
 
 Acquire and validate all twelve official VMM fluvial/pluvial flood-depth
@@ -1639,6 +1643,10 @@ take a long time because every VMM WCS tile is bounded, rate-limited and
 validated. This is expected operator work; the app never fetches these rasters
 on page load or map click.
 
+The live completed matrix contains 336/336 ready Dataset/DatasetVersion pairs.
+A repeat run reuses the existing request identities. Regional map selections
+analyse the persisted files and never trigger the public WCS.
+
 ## Cross-domain Mol profile
 
 Load the five official policy rasters for the exact Mol municipality Area and