fix(map): make area analysis scale-aware

.gitattributes

@@ -1,4 +1,5 @@
 *.sh text eol=lf
+deploy/unraid/gosu-setpriv text eol=lf
 *.py text eol=lf
 *.yml text eol=lf
 *.yaml text eol=lf

backend/app/api/routes/selection_partitions.py

@@ -0,0 +1,87 @@
+from __future__ import annotations
+
+from uuid import UUID
+
+from fastapi import APIRouter, Depends
+from sqlalchemy.orm import Session
+
+from app.core.errors import AppError
+from app.db.session import get_db
+from app.models import Area, Dataset
+from app.schemas.common import Envelope
+from app.schemas.operations import VectorSelectionResponse
+from app.schemas.selection_partitions import VectorPartitionSelectionRequest
+from app.services.vector_feature_service import VectorFeatureService
+from app.utils.response import envelope
+
+
+router = APIRouter(prefix="/projects/{project_id}", tags=["selection-partitions"])
+
+
+def _product_identity(dataset: Dataset) -> str:
+    metadata = dataset.source_metadata if isinstance(dataset.source_metadata, dict) else {}
+    return str(metadata.get("product_key") or dataset.reference_layer_name or "")
+
+
+@router.post(
+    "/datasets/vector/partitions/select",
+    response_model=Envelope[VectorSelectionResponse],
+)
+def select_vector_partitions(
+    project_id: UUID,
+    payload: VectorPartitionSelectionRequest,
+    db: Session = Depends(get_db),
+):
+    datasets = db.query(Dataset).filter(Dataset.id.in_(payload.dataset_ids)).all()
+    by_id = {dataset.id: dataset for dataset in datasets}
+    ordered = [by_id.get(dataset_id) for dataset_id in payload.dataset_ids]
+    if any(dataset is None or dataset.project_id != project_id for dataset in ordered):
+        raise AppError(code="DATASET_NOT_FOUND", message="One or more selection partitions were not found", status_code=404)
+    typed_datasets = [dataset for dataset in ordered if dataset is not None]
+    if any(dataset.dataset_type not in {"vector", "geojson"} or dataset.status != "ready" for dataset in typed_datasets):
+        raise AppError(
+            code="INVALID_VECTOR_PARTITIONS",
+            message="Every selection partition must be a ready vector dataset",
+            status_code=409,
+        )
+    source_names = {dataset.source_name for dataset in typed_datasets}
+    product_keys = {_product_identity(dataset) for dataset in typed_datasets}
+    if len(source_names) != 1 or len(product_keys) != 1:
+        raise AppError(
+            code="VECTOR_PARTITION_SOURCE_MISMATCH",
+            message="Selection partitions must belong to one governed source product",
+            details={"source_names": sorted(str(value) for value in source_names), "product_keys": sorted(product_keys)},
+            status_code=409,
+        )
+
+    selection_geometry = None
+    selection_area_id = None
+    if payload.area_id is not None:
+        selection_area = db.get(Area, payload.area_id)
+        if selection_area is None or selection_area.project_id != project_id:
+            raise AppError(code="AREA_NOT_FOUND", message="Area not found", status_code=404)
+        selection_geometry, _covers_full_area = VectorFeatureService.constrain_bbox_to_area(
+            payload.bbox.model_dump(),
+            selection_area.geometry,
+        )
+        selection_area_id = selection_area.id
+
+    representative = typed_datasets[0]
+    dataset_ids = [dataset.id for dataset in typed_datasets]
+    result = VectorFeatureService.select_features_by_bbox(
+        db,
+        dataset_id=representative.id,
+        dataset_ids=dataset_ids,
+        bbox=payload.bbox.model_dump(),
+        limit=payload.limit,
+        dataset=representative,
+        selection_geometry=selection_geometry,
+        selection_area_id=selection_area_id,
+        deduplicate_source_features=True,
+    )
+    result.update(
+        partition_count=len(dataset_ids),
+        source_name=representative.source_name,
+        dataset_ids=dataset_ids,
+    )
+    return envelope(VectorSelectionResponse(**result).model_dump(exclude_none=True))

backend/app/main.py

@@ -11,7 +11,7 @@ from fastapi.exceptions import RequestValidationError
 from fastapi.middleware.cors import CORSMiddleware
 from fastapi.responses import JSONResponse
 
-from app.api.routes import analysis, areas, assistant, datasets, demo, detection, exports, external, health, jobs, projects, qa, quality_checks, segmentation, temporal
+from app.api.routes import analysis, areas, assistant, datasets, demo, detection, exports, external, health, jobs, projects, qa, quality_checks, segmentation, selection_partitions, temporal
 from app.core.config import get_settings
 from app.core.errors import AppError
 from app.core.logging import configure_logging
@@ -91,6 +91,7 @@ def create_app() -> FastAPI:
     app.include_router(qa.router, prefix=settings.api_prefix)
     app.include_router(detection.router, prefix=settings.api_prefix)
     app.include_router(segmentation.router, prefix=settings.api_prefix)
+    app.include_router(selection_partitions.router, prefix=settings.api_prefix)
     app.include_router(temporal.router, prefix=settings.api_prefix)
     app.include_router(assistant.router, prefix=settings.api_prefix)
 

backend/app/schemas/dhmv.py

@@ -58,6 +58,7 @@ class TerrainSelectionRequest(BaseModel):
 
 class TerrainPartitionSelectionRequest(TerrainSelectionRequest):
     product_key: str = "dtm_1m"
+    dataset_ids: list[UUID] | None = Field(default=None, min_length=1, max_length=16)
 
 
 class TerrainMetric(BaseModel):

backend/app/schemas/flood_hazard.py

@@ -61,6 +61,7 @@ class FloodHazardSelectionRequest(BaseModel):
 
 class FloodHazardPartitionSelectionRequest(FloodHazardSelectionRequest):
     product_key: str = "pluviaal_current_t100"
+    dataset_ids: list[UUID] | None = Field(default=None, min_length=1, max_length=16)
 
 
 class FloodHazardMetric(BaseModel):

backend/app/schemas/selection_partitions.py

@@ -0,0 +1,14 @@
+from __future__ import annotations
+
+from uuid import UUID
+
+from pydantic import BaseModel, Field
+
+from .operations import VectorSelectionBBox
+
+
+class VectorPartitionSelectionRequest(BaseModel):
+    dataset_ids: list[UUID] = Field(min_length=1, max_length=16)
+    bbox: VectorSelectionBBox
+    area_id: UUID | None = None
+    limit: int = Field(default=1000, ge=1, le=1000)

backend/app/services/flood_hazard_analysis_service.py

@@ -225,6 +225,7 @@ class FloodHazardAnalysisService:
             selection_geometry_4326=selection_4326,
             nodata=FloodHazardAcquisitionService.NODATA,
             max_pixels=resolved_settings.flood_hazard_max_pixels,
+            dataset_ids=payload.dataset_ids,
         )
         try:
             import numpy as np

backend/app/services/raster_partition_analysis_service.py

@@ -51,17 +51,17 @@ class RasterPartitionAnalysisService:
         source_name: str,
         product_key: str,
         bbox: tuple[float, float, float, float],
+        dataset_ids: list[UUID] | None = None,
     ) -> 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()
+        query = db.query(Dataset).filter(
+            Dataset.project_id == project_id,
+            Dataset.source_name == source_name,
+            Dataset.dataset_type == "raster",
+            Dataset.status == "ready",
         )
+        if dataset_ids is not None:
+            query = query.filter(Dataset.id.in_(dataset_ids))
+        rows = query.all()
         candidates = [
             dataset
             for dataset in rows
@@ -78,6 +78,13 @@ class RasterPartitionAnalysisService:
                 details={"source_name": source_name, "product_key": product_key},
                 status_code=404,
             )
+        if dataset_ids is not None and {dataset.id for dataset in candidates} != set(dataset_ids):
+            raise AppError(
+                code="RASTER_PARTITION_SOURCE_MISMATCH",
+                message="Every requested raster partition must match the governed source product and selection",
+                details={"requested_count": len(dataset_ids), "eligible_count": len(candidates)},
+                status_code=409,
+            )
         if len(candidates) > RasterPartitionAnalysisService.MAX_PARTITIONS:
             raise AppError(
                 code="RASTER_PARTITION_LIMIT_EXCEEDED",
@@ -100,6 +107,7 @@ class RasterPartitionAnalysisService:
         selection_geometry_4326,
         nodata: float,
         max_pixels: int,
+        dataset_ids: list[UUID] | None = None,
     ) -> RasterPartitionSelection:
         try:
             import numpy as np
@@ -120,6 +128,7 @@ class RasterPartitionAnalysisService:
             source_name=source_name,
             product_key=product_key,
             bbox=bbox,
+            dataset_ids=dataset_ids,
         )
         transformer = Transformer.from_crs("EPSG:4326", "EPSG:31370", always_xy=True)
         selection_metric = shapely_transform(transformer.transform, selection_geometry_4326)

backend/app/services/terrain_analysis_service.py

@@ -235,6 +235,7 @@ class TerrainAnalysisService:
             selection_geometry_4326=selection_4326,
             nodata=DhmvAcquisitionService.NODATA,
             max_pixels=resolved_settings.dhmv_max_pixels,
+            dataset_ids=payload.dataset_ids,
         )
         surface_models = {
             str((dataset.source_metadata or {}).get("surface_model") or "")

backend/app/services/vector_feature_service.py

@@ -11,7 +11,7 @@ from geoalchemy2.shape import to_shape
 from shapely.geometry import box, mapping, shape
 from shapely.ops import transform as transform_geometry
 from shapely.validation import make_valid
-from sqlalchemy import Float, cast, func
+from sqlalchemy import Float, String, cast, func
 
 from app.core.errors import AppError
 from app.models import Dataset, VectorFeature
@@ -548,6 +548,8 @@ class VectorFeatureService:
         selection_area_id: UUID | None = None,
         full_dataset_area: bool = False,
         preclipped_partition_filter: tuple[str, str] | None = None,
+        dataset_ids: list[UUID] | None = None,
+        deduplicate_source_features: bool = False,
     ) -> dict[str, Any]:
         normalized_bbox = VectorFeatureService._normalize_selection_bbox(bbox)
         safe_limit = max(1, min(int(limit), 1000))
@@ -561,13 +563,19 @@ class VectorFeatureService:
                 4326,
             )
 
-        query = db.query(VectorFeature).filter(VectorFeature.dataset_id == dataset_id)
+        selected_dataset_ids = dataset_ids or [dataset_id]
+        query = db.query(VectorFeature).filter(VectorFeature.dataset_id.in_(selected_dataset_ids))
         if preclipped_partition_filter is not None:
             partition_property, partition_value = preclipped_partition_filter
             query = query.filter(VectorFeature.properties_json.op("->>")(partition_property) == partition_value)
         if not full_dataset_area:
             query = query.filter(ST_Intersects(VectorFeature.geometry, selection_shape))
-        if hasattr(query, "count"):
+        if deduplicate_source_features:
+            identity = func.coalesce(VectorFeature.source_feature_id, cast(VectorFeature.id, String))
+            total_feature_count = int(
+                query.with_entities(func.count(func.distinct(identity))).scalar() or 0
+            )
+        elif hasattr(query, "count"):
             total_feature_count = int(query.count())
         else:  # Lightweight unit-test sessions do not always implement Query.count().
             total_feature_count = len(query.all())
@@ -585,6 +593,7 @@ class VectorFeatureService:
             summary = VectorFeatureService.summarize_features_by_bbox(
                 db,
                 dataset=dataset,
+                dataset_ids=selected_dataset_ids,
                 bbox=normalized_bbox,
                 total_feature_count=total_feature_count,
                 selection_geometry=selection_geometry,

backend/tests/test_selection_partition_analysis.py

@@ -0,0 +1,103 @@
+from __future__ import annotations
+
+from uuid import uuid4
+
+import pytest
+from pydantic import ValidationError
+
+from app.api.routes.selection_partitions import select_vector_partitions
+from app.core.errors import AppError
+from app.models import Dataset
+from app.schemas.selection_partitions import VectorPartitionSelectionRequest
+from app.services.vector_feature_service import VectorFeatureService
+
+
+class DatasetQuery:
+    def __init__(self, datasets):
+        self.datasets = datasets
+
+    def filter(self, *_args):
+        return self
+
+    def all(self):
+        return self.datasets
+
+
+class DatasetSession:
+    def __init__(self, datasets):
+        self.datasets = datasets
+
+    def query(self, model):
+        assert model is Dataset
+        return DatasetQuery(self.datasets)
+
+
+def make_dataset(project_id, dataset_id, *, source_name="grb", product_key="buildings"):
+    return Dataset(
+        id=dataset_id,
+        project_id=project_id,
+        name=f"{source_name}-{product_key}",
+        dataset_type="vector",
+        source="official",
+        dataset_role="reference",
+        source_name=source_name,
+        reference_layer_name=product_key,
+        source_metadata={"product_key": product_key, "theme": product_key},
+        provenance_metadata={},
+        metadata_json={},
+        status="ready",
+    )
+
+
+def test_vector_partition_route_combines_one_governed_product(monkeypatch) -> None:
+    project_id = uuid4()
+    dataset_ids = [uuid4(), uuid4()]
+    db = DatasetSession([make_dataset(project_id, dataset_id) for dataset_id in dataset_ids])
+    captured = {}
+
+    def select_features(_db, **kwargs):
+        captured.update(kwargs)
+        return {
+            "selection_bbox": kwargs["bbox"],
+            "feature_count": 1,
+            "total_feature_count": 3,
+            "limit": kwargs["limit"],
+            "truncated": False,
+            "geojson": {"type": "FeatureCollection", "features": []},
+            "summary": None,
+        }
+
+    monkeypatch.setattr(VectorFeatureService, "select_features_by_bbox", select_features)
+    payload = VectorPartitionSelectionRequest(
+        dataset_ids=dataset_ids,
+        bbox={"min_x": 5.0, "min_y": 51.0, "max_x": 5.3, "max_y": 51.2},
+    )
+    response = select_vector_partitions(project_id, payload, db)
+
+    assert captured["dataset_ids"] == dataset_ids
+    assert captured["deduplicate_source_features"] is True
+    assert response["data"]["partition_count"] == 2
+    assert response["data"]["dataset_ids"] == dataset_ids
+
+
+def test_vector_partition_request_has_a_bounded_fan_out() -> None:
+    with pytest.raises(ValidationError):
+        VectorPartitionSelectionRequest(
+            dataset_ids=[uuid4() for _ in range(17)],
+            bbox={"min_x": 5.0, "min_y": 51.0, "max_x": 5.3, "max_y": 51.2},
+        )
+
+
+def test_vector_partition_route_rejects_mixed_source_products() -> None:
+    project_id = uuid4()
+    datasets = [
+        make_dataset(project_id, uuid4(), source_name="grb", product_key="buildings"),
+        make_dataset(project_id, uuid4(), source_name="spw_picc", product_key="picc_buildings"),
+    ]
+    payload = VectorPartitionSelectionRequest(
+        dataset_ids=[dataset.id for dataset in datasets],
+        bbox={"min_x": 5.0, "min_y": 51.0, "max_x": 5.3, "max_y": 51.2},
+    )
+    with pytest.raises(AppError) as exc_info:
+        select_vector_partitions(project_id, payload, DatasetSession(datasets))
+    assert getattr(exc_info.value, "code", None) == "VECTOR_PARTITION_SOURCE_MISMATCH"

docs/API_CONTRACTS.md

@@ -881,6 +881,24 @@ Rules:
 - `limit` is bounded to `1..1000`. Municipality-scale clients must page spatially by viewport instead of requesting an unbounded municipality FeatureCollection.
 - The Map workspace uses this existing endpoint for vector datasets above 5,000 features. It starts delivery at zoom level 14, debounces `moveend` requests and explicitly reports `truncated=true` as a request to zoom further in. This is a client delivery policy, not a second API or persistence path.
 
+### POST `/api/v1/projects/{project_id}/datasets/vector/partitions/select`
+
+Combines `1..16` bounded vector acquisitions of one governed source product
+into one read-only selection result. The request uses the same `bbox`, optional
+`area_id` and `limit` contract as vector selection plus `dataset_ids`.
+
+The backend rejects mixed projects, non-ready vector datasets and partitions
+whose `source_name` or `product_key` differs. PostGIS calculates area and length
+metrics across all persisted tile geometries. `total_feature_count` is
+deduplicated by provider `source_feature_id` where available so a source object
+crossing a tile edge is not presented as two objects. The response includes
+`partition_count`, `source_name` and the exact `dataset_ids` used.
+
+The Map workbench uses this route only for regional selections up to 50 by 50
+kilometres. Provider calls remain individually bounded below 20 kilometres;
+larger overview selections do not fan out into unbounded high-resolution
+downloads.
+
 ### POST `/api/v1/projects/{project_id}/datasets/{dataset_id}/vector/select/derive`
 
 Persists a bbox selection as a new derived vector dataset and indexes the

docs/CODEX_EXECUTION_LOG.md

@@ -10953,3 +10953,40 @@ Validation:
   themes through acquisition or persisted national data and semantic
   selection metrics;
 - browser verification follows against the deployed commit on port 1202.
+
+## 2026-07-21 - Scale-aware rectangle analysis
+
+Implemented:
+
+- reproduced the reported provider failure with an approximately 11,468 km2
+  cross-region rectangle: twenty high-resolution adapters received one unsafe
+  bbox and returned their governed size limits;
+- added explicit detail, regional and overview selection tiers before any
+  provider acquisition starts;
+- regional selections up to 50 by 50 km now split compatible vector and point
+  sources into at most sixteen 18 km tiles, resolve the authority zone per tile
+  and combine each provider product as one persisted PostGIS result;
+- added a canonical multi-partition vector selection route with project,
+  readiness and source-product consistency guards and source-feature count
+  deduplication;
+- allowed terrain and flood partition analysis to receive an explicit set of
+  freshly acquired Dataset ids, avoiding accidental reuse of overlapping old
+  bounded rasters;
+- kept 5 m raster analysis behind its real pixel budget and 10 m thematic
+  analysis behind its 50 km scale budget;
+- changed overview selections to query only national or already provisioned
+  scale-compatible datasets and explain the scale choice once, instead of
+  presenting one provider error for every unavailable detail source.
+
+Validation:
+
+- backend import/compile and frontend TypeScript passed after the contract
+  change;
+- focused selection-partition, DHMV and VMM tests passed (30 tests);
+- focused frontend scale, tiling and concurrency tests passed (12 tests).
+
+Remaining in this pass:
+
+- run the complete readiness gate, deploy the immutable revision and repeat
+  both the large overview rectangle and a regional partitioned rectangle in
+  the live browser.

docs/TODO.md

@@ -835,3 +835,14 @@ This file now starts with the current implementation status. Older preparation/b
   uncertainty tests exist; never merge TAW, LAT and mDNG implicitly.
 - [ ] Add water volume only when bed and water-surface inputs share a governed
   time, datum and coverage contract.
+
+# Scale-aware map selection
+
+- [x] Prevent country-scale rectangles from fan-out querying every local
+  provider and replace repeated source-limit errors with one overview status.
+- [x] Partition compatible 20-50 km regional vector acquisitions into bounded
+  source requests and combine their persisted PostGIS metrics.
+- [x] Keep terrain, flood and thematic rasters behind explicit pixel and
+  selection-size budgets.
+- [ ] Provision additional national-scale baseline datasets before exposing
+  more overview themes; do not synthesize regional detail at national scale.

frontend/src/components/map/MapWorkspace.tsx

@@ -41,8 +41,11 @@ import {
   safeFileStem,
   selectedAreaCoverageZones,
   selectedFeatureCollection,
+  selectionAnalysisScale,
   selectionAreaSquareMetres,
+  selectionDimensions,
   selectionMetricLabel,
+  splitSelectionBbox,
 } from './mapWorkspaceUtils'
 
 const DEFAULT_SELECTED_FEATURE_FILENAME = 'selected-feature.geojson'
@@ -91,6 +94,25 @@ interface OnDemandMapProduct extends MapThemeAcquisition {
   limitationMessage: string
 }
 
+interface PlannedOnDemandMapProduct extends OnDemandMapProduct {
+  acquisitionBboxes: VectorSelectionBBox[]
+}
+
+function productSupportsSelection(product: OnDemandMapProduct, bbox: VectorSelectionBBox): boolean {
+  const scale = selectionAnalysisScale(bbox)
+  if (scale === 'overview') return false
+  const dimensions = selectionDimensions(bbox)
+  if (product.kind === 'dhmv' || product.kind === 'flood_hazard') {
+    return dimensions.areaSquareMetres <= 280_000_000
+  }
+  if (product.kind === 'thematic_raster') {
+    return dimensions.widthMetres <= 50_000
+      && dimensions.heightMetres <= 50_000
+      && dimensions.areaSquareMetres <= 2_800_000_000
+  }
+  return true
+}
+
 const DATA_THEMES: DataTheme[] = [
   {
     id: 'administrative',
@@ -836,6 +858,7 @@ export function MapWorkspace({
     loading: officialMapProductsLoading,
     error: officialMapProductsError,
     resolveCoverage,
+    resolveCoveragePartitions,
   } = useOfficialMapProducts(selectedProjectId)
   const {
     temporalComparison,
@@ -1114,12 +1137,18 @@ export function MapWorkspace({
     }
     return result
   }, [onDemandProductsForZones, selectedCoverageZones])
+  const mapSelectionScale = mapSelectionBbox ? selectionAnalysisScale(mapSelectionBbox) : null
   const selectionRelevantThemes = useMemo(() => {
     if (!mapSelectionBbox || !coverage) {
       return DATA_THEMES
     }
     const boundedThemes = new Set(
-      onDemandProductsForZones(coverage.intersected_zones).map((product) => product.theme),
+      (mapSelectionBbox
+        ? onDemandProductsForZones(coverage.intersected_zones).filter(
+            (product) => productSupportsSelection(product, mapSelectionBbox),
+          )
+        : [])
+        .map((product) => product.theme),
     )
     return DATA_THEMES.filter((theme) => {
       if (boundedThemes.has(theme.id)) {
@@ -1133,9 +1162,9 @@ export function MapWorkspace({
         (item) => item.theme === coverageTheme && item.status === 'operational',
       )
     })
-  }, [coverage, mapSelectionBbox, onDemandProductsForZones, themeDatasetMap])
+  }, [coverage, mapSelectionBbox, mapSelectionScale, onDemandProductsForZones, themeDatasetMap])
   const unavailableSelectionThemeCount = Math.max(DATA_THEMES.length - selectionRelevantThemes.length, 0)
-  const activeOnDemandMapProduct = themeDatasetMap[activeTheme.id]
+  const activeOnDemandMapProduct = mapSelectionScale === 'overview' || themeDatasetMap[activeTheme.id]
     ? null
     : onDemandProductMap.get(activeTheme.id) ?? null
   const activeThemeMapStyle = DATA_THEME_MAP_STYLES[activeTheme.id]
@@ -1366,6 +1395,20 @@ export function MapWorkspace({
         : selectionAreaSquareMetres(mapSelectionBbox),
     [mapSelectionBbox, selectedAreaBbox, selectedMapArea?.area_m2],
   )
+  const selectionScaleNotice = useMemo(() => {
+    if (!mapSelectionBbox || !mapSelectionScale) return null
+    const dimensions = selectionDimensions(mapSelectionBbox)
+    const widthKm = dimensions.widthMetres / 1000
+    const heightKm = dimensions.heightMetres / 1000
+    if (mapSelectionScale === 'regional') {
+      const partitionCount = splitSelectionBbox(mapSelectionBbox).length
+      return `Regionale analyse van ${widthKm.toLocaleString('nl-BE', { maximumFractionDigits: 1 })} × ${heightKm.toLocaleString('nl-BE', { maximumFractionDigits: 1 })} km. Geschikte detailbronnen worden automatisch over ${partitionCount} begrensde bronpartities verwerkt; 5 m-rasters worden alleen meegenomen wanneer het veilige pixelbudget volstaat.`
+    }
+    if (mapSelectionScale === 'overview') {
+      return `Overzichtsanalyse van ${widthKm.toLocaleString('nl-BE', { maximumFractionDigits: 1 })} × ${heightKm.toLocaleString('nl-BE', { maximumFractionDigits: 1 })} km. Alleen landelijke en vooraf ingeladen bronnen die deze schaal betrouwbaar ondersteunen worden bevraagd. Teken maximaal 50 × 50 km voor regionale thema's of 20 × 20 km voor alle detailbronnen.`
+    }
+    return null
+  }, [mapSelectionBbox, mapSelectionScale])
   const selectedResultTotal = activeSelectionResult?.total_feature_count ?? activeSelectionResult?.feature_count ?? 0
   const selectedDensity = selectedAreaSquareMetres && selectedAreaSquareMetres > 0
     ? selectedResultTotal / (selectedAreaSquareMetres / 1_000_000)
@@ -1727,6 +1770,7 @@ export function MapWorkspace({
 
   const loadAllThemeResults = async (bbox: VectorSelectionBBox, areaId?: string) => {
     let resolvedZones = selectedCoverageZones
+    const scale = selectionAnalysisScale(bbox)
     if (analysisMode === 'current' && selectedProjectId) {
       const resolvedCoverage = await resolveCoverage({
         minx: bbox.min_x,
@@ -1740,9 +1784,49 @@ export function MapWorkspace({
       }
       resolvedZones = resolvedCoverage.intersected_zones
     }
-    const resolvedProducts = analysisMode === 'current'
-      ? onDemandProductsForZones(resolvedZones)
-      : []
+    let resolvedProducts: PlannedOnDemandMapProduct[] = []
+    if (analysisMode === 'current' && scale !== 'overview') {
+      const zoneProducts = resolvedZones
+        ? onDemandProductsForZones(resolvedZones)
+        : []
+      if (scale === 'detail' || !selectedProjectId) {
+        resolvedProducts = zoneProducts
+          .filter((product) => productSupportsSelection(product, bbox))
+          .map((product) => ({
+            ...product,
+            acquisitionBboxes: [bbox],
+          }))
+      } else {
+        const detailTiles = splitSelectionBbox(bbox)
+        const tileCoverage = await resolveCoveragePartitions(detailTiles)
+        if (!tileCoverage) {
+          clearThemeInsights()
+          return
+        }
+        const grouped = new Map<string, PlannedOnDemandMapProduct>()
+        for (const item of tileCoverage) {
+          for (const product of onDemandProductsForZones(item.coverage.intersected_zones)) {
+            if (product.kind === 'thematic_raster' || !productSupportsSelection(product, bbox)) continue
+            const key = `${product.kind}:${product.productKey}`
+            const existing = grouped.get(key)
+            if (existing) {
+              existing.acquisitionBboxes.push(item.bbox)
+            } else {
+              grouped.set(key, { ...product, acquisitionBboxes: [item.bbox] })
+            }
+          }
+        }
+        for (const product of zoneProducts.filter(
+          (candidate) => candidate.kind === 'thematic_raster' && productSupportsSelection(candidate, bbox),
+        )) {
+          grouped.set(`${product.kind}:${product.productKey}`, {
+            ...product,
+            acquisitionBboxes: [bbox],
+          })
+        }
+        resolvedProducts = [...grouped.values()]
+      }
+    }
     const availableThemes: Array<MapThemeQuery<DataThemeId>> = []
     for (const theme of DATA_THEMES) {
       const dataset = themeDatasetMap[theme.id]
@@ -1765,6 +1849,7 @@ export function MapWorkspace({
               productKey: onDemandProduct.productKey,
               displayName: onDemandProduct.displayName,
             },
+            acquisitionBboxes: onDemandProduct.acquisitionBboxes,
           })
         }
         continue
@@ -2560,9 +2645,14 @@ export function MapWorkspace({
                           )
                         })
                       })}
+                      {selectionScaleNotice ? (
+                        <p className="geo-data-notice">{selectionScaleNotice}</p>
+                      ) : null}
                       {unavailableSelectionThemeCount > 0 ? (
                         <p className="geo-data-notice">
-                          {unavailableSelectionThemeCount} thema’s zijn voor deze zone niet van toepassing of hebben nog geen gevalideerde operationele koppeling.
+                          {mapSelectionScale === 'overview'
+                            ? `${unavailableSelectionThemeCount} detailthema's zijn op deze overzichtsschaal bewust niet bevraagd.`
+                            : `${unavailableSelectionThemeCount} thema's zijn voor deze zone niet van toepassing, niet operationeel gekoppeld of te fijnmazig voor deze selectieschaal.`}
                         </p>
                       ) : null}
                     </div>

frontend/src/components/map/mapWorkspaceUtils.test.ts

@@ -8,9 +8,12 @@ import {
   productCoversZones,
   resultMetricLabel,
   selectedAreaCoverageZones,
+  selectionAnalysisScale,
   selectionAreaSquareMetres,
+  selectionDimensions,
+  splitSelectionBbox,
 } from './mapWorkspaceUtils'
-import type { VectorSelectionResponse } from '../../types'
+import type { VectorSelectionBBox, VectorSelectionResponse } from '../../types'
 
 describe('map workspace selection guards', () => {
   it('normalizes drag corners into an EPSG:4326 bbox', () => {
@@ -90,4 +93,49 @@ describe('map workspace selection guards', () => {
       geometry_clipped_to_selection: true,
     })).toBe(false)
   })
+
+  it('classifies local, regional and overview selections before provider calls', () => {
+    const bbox = (widthDegrees: number, heightDegrees: number): VectorSelectionBBox => ({
+      min_x: 5,
+      min_y: 51,
+      max_x: 5 + widthDegrees,
+      max_y: 51 + heightDegrees,
+      crs: 'EPSG:4326',
+    })
+    expect(selectionAnalysisScale(bbox(0.1, 0.1))).toBe('detail')
+    expect(selectionAnalysisScale(bbox(0.35, 0.25))).toBe('regional')
+    expect(selectionAnalysisScale(bbox(1, 1))).toBe('overview')
+  })
+
+  it('splits regional selections into provider-safe tiles without changing the outer bounds', () => {
+    const selection: VectorSelectionBBox = {
+      min_x: 5,
+      min_y: 51,
+      max_x: 5.5,
+      max_y: 51.35,
+      crs: 'EPSG:4326',
+    }
+    const tiles = splitSelectionBbox(selection)
+    expect(tiles.length).toBeGreaterThan(1)
+    expect(Math.min(...tiles.map((tile) => tile.min_x))).toBe(selection.min_x)
+    expect(Math.min(...tiles.map((tile) => tile.min_y))).toBe(selection.min_y)
+    expect(Math.max(...tiles.map((tile) => tile.max_x))).toBe(selection.max_x)
+    expect(Math.max(...tiles.map((tile) => tile.max_y))).toBe(selection.max_y)
+    for (const tile of tiles) {
+      const dimensions = selectionDimensions(tile)
+      expect(dimensions.widthMetres).toBeLessThanOrEqual(18_100)
+      expect(dimensions.heightMetres).toBeLessThanOrEqual(18_100)
+    }
+  })
+
+  it('refuses an unbounded detail fan-out', () => {
+    const selection: VectorSelectionBBox = {
+      min_x: 4,
+      min_y: 50,
+      max_x: 6,
+      max_y: 52,
+      crs: 'EPSG:4326',
+    }
+    expect(() => splitSelectionBbox(selection)).toThrow('detailpartities')
+  })
 })

frontend/src/components/map/mapWorkspaceUtils.ts

@@ -73,6 +73,63 @@ export function selectionAreaSquareMetres(bbox: VectorSelectionBBox | null): num
   return Math.max(0, widthMetres * heightMetres)
 }
 
+export interface SelectionDimensions {
+  widthMetres: number
+  heightMetres: number
+  areaSquareMetres: number
+}
+
+export type SelectionAnalysisScale = 'detail' | 'regional' | 'overview'
+
+export function selectionDimensions(bbox: VectorSelectionBBox): SelectionDimensions {
+  const middleLatitudeRadians = ((bbox.min_y + bbox.max_y) / 2) * (Math.PI / 180)
+  const widthMetres = Math.max(0, (bbox.max_x - bbox.min_x) * 111_320 * Math.cos(middleLatitudeRadians))
+  const heightMetres = Math.max(0, (bbox.max_y - bbox.min_y) * 110_574)
+  return {
+    widthMetres,
+    heightMetres,
+    areaSquareMetres: widthMetres * heightMetres,
+  }
+}
+
+export function selectionAnalysisScale(bbox: VectorSelectionBBox): SelectionAnalysisScale {
+  const { widthMetres, heightMetres } = selectionDimensions(bbox)
+  const longestSide = Math.max(widthMetres, heightMetres)
+  if (longestSide <= 20_000) return 'detail'
+  if (longestSide <= 50_000) return 'regional'
+  return 'overview'
+}
+
+export function splitSelectionBbox(
+  bbox: VectorSelectionBBox,
+  maxTileSideMetres = 18_000,
+  maxTiles = 16,
+): VectorSelectionBBox[] {
+  const { widthMetres, heightMetres } = selectionDimensions(bbox)
+  const columns = Math.max(1, Math.ceil(widthMetres / maxTileSideMetres))
+  const rows = Math.max(1, Math.ceil(heightMetres / maxTileSideMetres))
+  if (columns * rows > maxTiles) {
+    throw new Error(
+      `De selectie vereist ${columns * rows} detailpartities; maximaal ${maxTiles} zijn toegestaan.`,
+    )
+  }
+  const longitudeStep = (bbox.max_x - bbox.min_x) / columns
+  const latitudeStep = (bbox.max_y - bbox.min_y) / rows
+  const tiles: VectorSelectionBBox[] = []
+  for (let row = 0; row < rows; row += 1) {
+    for (let column = 0; column < columns; column += 1) {
+      tiles.push({
+        min_x: bbox.min_x + longitudeStep * column,
+        min_y: bbox.min_y + latitudeStep * row,
+        max_x: column === columns - 1 ? bbox.max_x : bbox.min_x + longitudeStep * (column + 1),
+        max_y: row === rows - 1 ? bbox.max_y : bbox.min_y + latitudeStep * (row + 1),
+        crs: 'EPSG:4326',
+      })
+    }
+  }
+  return tiles
+}
+
 export function bboxesEqual(left: VectorSelectionBBox | null, right: VectorSelectionBBox | null): boolean {
   if (!left || !right) {
     return false

frontend/src/hooks/useMapThemeSelectionInsights.ts

@@ -26,6 +26,7 @@ export interface MapThemeQuery<TThemeId extends string> {
   dataset?: DatasetCreateResponse
   partitioned?: boolean
   acquisition?: MapThemeAcquisition
+  acquisitionBboxes?: VectorSelectionBBox[]
 }
 
 export interface MapThemeInsight<TThemeId extends string> {
@@ -103,51 +104,63 @@ export function useMapThemeSelectionInsights<TThemeId extends string>(
       const settled = await settleWithConcurrency(
         queries,
         3,
-        async ({ themeId, dataset: existingDataset, partitioned, acquisition }) => {
+        async ({ themeId, dataset: existingDataset, partitioned, acquisition, acquisitionBboxes }) => {
           let dataset = existingDataset
+          let acquiredDatasets: DatasetCreateResponse[] = []
           if (acquisition) {
-            const commonPayload = {
-              bbox,
-              area_id: areaId,
-              force_refresh: false,
-            }
-            const acquisitionJob = acquisition.kind === 'thematic_raster'
-              ? await datasetsApi.acquireThematicRaster(selectedProjectId, {
-                  ...commonPayload,
-                  product_key: acquisition.productKey,
-                })
-              : acquisition.kind === 'dhmv'
-                ? await datasetsApi.acquireDhmv(selectedProjectId, {
+            const requestedBboxes = acquisitionBboxes?.length ? acquisitionBboxes : [bbox]
+            const acquisitionResults = await settleWithConcurrency(requestedBboxes, 1, async (acquisitionBbox) => {
+              const commonPayload = {
+                bbox: acquisitionBbox,
+                area_id: areaId,
+                force_refresh: false,
+              }
+              const acquisitionJob = acquisition.kind === 'thematic_raster'
+                ? await datasetsApi.acquireThematicRaster(selectedProjectId, {
                     ...commonPayload,
-                    product_key: acquisition.productKey as 'dtm_1m' | 'dsm_1m',
+                    product_key: acquisition.productKey,
                   })
-                : acquisition.kind === 'flood_hazard'
-                  ? await datasetsApi.acquireFloodHazard(selectedProjectId, {
+                : acquisition.kind === 'dhmv'
+                  ? await datasetsApi.acquireDhmv(selectedProjectId, {
                       ...commonPayload,
-                      product_key: acquisition.productKey,
+                      product_key: acquisition.productKey as 'dtm_1m' | 'dsm_1m',
                     })
-                  : acquisition.kind === 'grb'
-                    ? await datasetsApi.acquireGrb(selectedProjectId, {
+                  : acquisition.kind === 'flood_hazard'
+                    ? await datasetsApi.acquireFloodHazard(selectedProjectId, {
                         ...commonPayload,
-                        product_key: acquisition.productKey as 'buildings' | 'roads' | 'water' | 'parcels',
+                        product_key: acquisition.productKey,
                       })
-                    : acquisition.kind === 'bathymetry_profiles'
-                      ? await datasetsApi.acquireBathymetryProfiles(selectedProjectId, commonPayload)
-                      : await datasetsApi.acquireOfficialVector(selectedProjectId, {
+                    : acquisition.kind === 'grb'
+                      ? await datasetsApi.acquireGrb(selectedProjectId, {
                           ...commonPayload,
-                          product_key: acquisition.productKey,
+                          product_key: acquisition.productKey as 'buildings' | 'roads' | 'water' | 'parcels',
                         })
-            if (acquisitionJob.status !== 'success' || !acquisitionJob.output_dataset_id) {
-              throw new Error(
-                acquisitionJob.error_message
-                  || `De officiële kaartbron ${acquisition.displayName} kon niet worden ingeladen.`,
-              )
+                      : acquisition.kind === 'bathymetry_profiles'
+                        ? await datasetsApi.acquireBathymetryProfiles(selectedProjectId, commonPayload)
+                        : await datasetsApi.acquireOfficialVector(selectedProjectId, {
+                            ...commonPayload,
+                            product_key: acquisition.productKey,
+                          })
+              if (acquisitionJob.status !== 'success' || !acquisitionJob.output_dataset_id) {
+                throw new Error(
+                  acquisitionJob.error_message
+                    || `De officiële kaartbron ${acquisition.displayName} kon niet worden ingeladen.`,
+                )
+              }
+              return datasetsApi.get(selectedProjectId, acquisitionJob.output_dataset_id)
+            })
+            const failedAcquisition = acquisitionResults.find((item) => item.status === 'rejected')
+            if (failedAcquisition?.status === 'rejected') {
+              throw failedAcquisition.reason
             }
-            dataset = await datasetsApi.get(selectedProjectId, acquisitionJob.output_dataset_id)
+            acquiredDatasets = acquisitionResults.flatMap((item) => item.status === 'fulfilled' ? [item.value] : [])
+            dataset = acquiredDatasets[0]
           }
           if (!dataset) {
             throw new Error(`Geen persistente databron beschikbaar voor thema ${themeId}.`)
           }
+          const acquiredDatasetIds = acquiredDatasets.map((item) => item.id)
+          const acquiredAsPartitions = acquiredDatasetIds.length > 1
           return {
             themeId,
             dataset,
@@ -155,11 +168,12 @@ export function useMapThemeSelectionInsights<TThemeId extends string>(
             acquisition,
             result: dataset.dataset_type === 'raster' && dataset.source_name === 'digitaal_vlaanderen_dhmv'
             ? terrainSelectionToMapSelection(
-                partitioned
+                partitioned || acquiredAsPartitions
                   ? await datasetsApi.selectTerrainPartitions(selectedProjectId, {
                       bbox,
                       area_id: areaId,
                       product_key: String(dataset.source_metadata?.['product_key'] ?? 'dtm_1m'),
+                      ...(acquiredAsPartitions ? { dataset_ids: acquiredDatasetIds } : {}),
                     })
                   : await datasetsApi.selectTerrain(selectedProjectId, dataset.id, {
                       bbox,
@@ -168,11 +182,12 @@ export function useMapThemeSelectionInsights<TThemeId extends string>(
               )
             : dataset.dataset_type === 'raster' && dataset.source_name === 'vmm_flood_hazard'
               ? floodHazardSelectionToMapSelection(
-                  partitioned
+                  partitioned || acquiredAsPartitions
                     ? await datasetsApi.selectFloodHazardPartitions(selectedProjectId, {
                         bbox,
                         area_id: areaId,
                         product_key: String(dataset.source_metadata?.['product_key'] ?? 'pluviaal_current_t100'),
+                        ...(acquiredAsPartitions ? { dataset_ids: acquiredDatasetIds } : {}),
                       })
                     : await datasetsApi.selectFloodHazard(selectedProjectId, dataset.id, {
                         bbox,
@@ -189,6 +204,13 @@ export function useMapThemeSelectionInsights<TThemeId extends string>(
                     bbox,
                     area_id: areaId,
                   }))
+            : acquiredAsPartitions && dataset.dataset_type !== 'raster'
+              ? await datasetsApi.selectVectorFeaturePartitions(selectedProjectId, {
+                  dataset_ids: acquiredDatasetIds,
+                  bbox,
+                  area_id: areaId,
+                  limit: 1000,
+                })
             : dataset.source_name === 'vmm_vha_bathymetry_profiles' && partitioned
               ? await datasetsApi.selectBathymetryProfilePartitions(selectedProjectId, {
                   bbox,

frontend/src/hooks/useOfficialMapProducts.ts

@@ -3,11 +3,13 @@ import { datasetsApi, externalApi } from '../services/api'
 import { formatError } from '../lib/formatError'
 import type {
   BathymetrySourceRead,
+  CoverageResolveResponse,
   DhmvProductRead,
   FloodHazardProductRead,
   GrbProductRead,
   OfficialVectorProductRead,
   ThematicRasterProductRead,
+  VectorSelectionBBox,
 } from '../types'
 
 export interface OfficialMapProducts {
@@ -104,5 +106,47 @@ export function useOfficialMapProducts(selectedProjectId: string | null) {
     [selectedProjectId],
   )
 
-  return { products, loading, error, resolveCoverage }
+  const resolveCoveragePartitions = useCallback(
+    async (bboxes: VectorSelectionBBox[]): Promise<Array<{
+      bbox: VectorSelectionBBox
+      coverage: CoverageResolveResponse
+    }> | null> => {
+      if (!selectedProjectId) {
+        setError('Selecteer eerst een werkruimte.')
+        return null
+      }
+      try {
+        const resolved: Array<{
+          bbox: VectorSelectionBBox
+          coverage: CoverageResolveResponse
+        }> = []
+        for (let offset = 0; offset < bboxes.length; offset += 4) {
+          const batch = bboxes.slice(offset, offset + 4)
+          resolved.push(...await Promise.all(batch.map(async (bbox) => ({
+            bbox,
+            coverage: await externalApi.resolveCoverage({
+              projectId: selectedProjectId,
+              bbox: {
+                minx: bbox.min_x,
+                miny: bbox.min_y,
+                maxx: bbox.max_x,
+                maxy: bbox.max_y,
+              },
+            }),
+          }))))
+        }
+        setError(null)
+        return resolved
+      } catch (requestError) {
+        setError(formatError(
+          requestError,
+          'De regionale dekking kon niet voor alle bronpartities worden bepaald.',
+        ))
+        return null
+      }
+    },
+    [selectedProjectId],
+  )
+
+  return { products, loading, error, resolveCoverage, resolveCoveragePartitions }
 }

frontend/src/services/api/datasets.ts

@@ -171,7 +171,7 @@ export const datasetsApi = {
     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 },
+    payload: { bbox: VectorSelectionRequest['bbox']; area_id?: string; product_key: string; dataset_ids?: string[] },
   ): Promise<TerrainSelectionResponse> =>
     apiPost<TerrainSelectionResponse>(`/api/v1/projects/${projectId}/datasets/raster/terrain/select`, payload),
   acquireFloodHazard: (projectId: string, payload: FloodHazardAcquireRequest): Promise<JobRead> =>
@@ -186,7 +186,7 @@ export const datasetsApi = {
     apiPost<FloodHazardSelectionResponse>(`/api/v1/projects/${projectId}/datasets/${datasetId}/raster/flood-hazard/select`, payload),
   selectFloodHazardPartitions: (
     projectId: string,
-    payload: { bbox: VectorSelectionRequest['bbox']; area_id?: string; product_key: string },
+    payload: { bbox: VectorSelectionRequest['bbox']; area_id?: string; product_key: string; dataset_ids?: string[] },
   ): Promise<FloodHazardSelectionResponse> =>
     apiPost<FloodHazardSelectionResponse>(`/api/v1/projects/${projectId}/datasets/raster/flood-hazard/select`, payload),
   listBathymetrySources: (projectId: string): Promise<{ items: BathymetrySourceRead[]; total: number }> =>
@@ -244,6 +244,14 @@ export const datasetsApi = {
     apiGet<VectorStatsResponse>(`/api/v1/projects/${projectId}/datasets/${datasetId}/vector/stats`),
   selectVectorFeatures: (projectId: string, datasetId: string, payload: VectorSelectionRequest): Promise<VectorSelectionResponse> =>
     apiPost<VectorSelectionResponse>(`/api/v1/projects/${projectId}/datasets/${datasetId}/vector/select`, payload),
+  selectVectorFeaturePartitions: (
+    projectId: string,
+    payload: VectorSelectionRequest & { dataset_ids: string[] },
+  ): Promise<VectorSelectionResponse> =>
+    apiPost<VectorSelectionResponse>(
+      `/api/v1/projects/${projectId}/datasets/vector/partitions/select`,
+      payload,
+    ),
   deriveVectorSelection: (projectId: string, datasetId: string, payload: VectorSelectionDeriveRequest): Promise<DatasetCreateResponse> =>
     apiPost<DatasetCreateResponse>(`/api/v1/projects/${projectId}/datasets/${datasetId}/vector/select/derive`, payload),
   vectorClip: (projectId: string, datasetId: string, payload: { area_id: string; output_name?: string }) =>