fix(map): make area analysis scale-aware

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,