feat: operationalize Flemish land and nature themes
GeoIntel CI / docs-smoke (push) Canceled after 0s
GeoIntel CI / contract-smoke (push) Canceled after 0s

This commit is contained in:
Codex
2026-07-17 22:38:25 +02:00
parent 0718d0ebba
commit c787fb2184
27 changed files with 2010 additions and 12 deletions
+26
View File
@@ -32,6 +32,7 @@ from app.schemas import (
ThematicRasterAcquireRequest,
ThematicRasterSelectionRequest,
GrbAcquireRequest,
OfficialVectorAcquireRequest,
VectorBBoxResponse,
VectorBufferRequest,
VectorClipRequest,
@@ -51,6 +52,7 @@ from app.services.source_freshness_service import SourceFreshnessService
from app.services.source_catalog_probe_service import SourceCatalogProbeService
from app.services.grb_refresh_plan_service import GrbRefreshPlanService
from app.services.grb_acquisition_service import GrbAcquisitionService
from app.services.official_vector_acquisition_service import OfficialVectorAcquisitionService
from app.services.orthophoto_acquisition_service import OrthophotoAcquisitionService
from app.services.dhmv_acquisition_service import DhmvAcquisitionService
from app.services.terrain_analysis_service import TerrainAnalysisService
@@ -219,6 +221,30 @@ def list_grb_products(project_id: UUID, db: Session = Depends(get_db)):
return envelope({"items": items, "total": len(items)})
@router.post("/datasets/official-vector/acquire", response_model=dict)
def acquire_bounded_official_vector(
project_id: UUID,
payload: OfficialVectorAcquireRequest,
db: Session = Depends(get_db),
):
job = JobService.run_sync_job(
db=db,
project_id=project_id,
job_type="vector.official.acquire",
parameters=payload.model_dump(mode="json"),
operation=lambda: OfficialVectorAcquisitionService.acquire(db, project_id, payload),
)
return envelope(job)
@router.get("/datasets/official-vector/products", response_model=dict)
def list_official_vector_products(project_id: UUID, db: Session = Depends(get_db)):
if not db.get(Project, project_id):
raise AppError(code="PROJECT_NOT_FOUND", message="Project not found", status_code=404)
items = OfficialVectorAcquisitionService.list_products()
return envelope({"items": items, "total": len(items)})
@router.post("/datasets/flood-hazard/acquire", response_model=dict)
def acquire_bounded_flood_hazard(
project_id: UUID,
+60
View File
@@ -87,6 +87,66 @@ class Settings(BaseSettings):
validation_alias="GRB_MAX_TOTAL_RESPONSE_MB",
)
grb_cache_ttl_hours: int = Field(default=24, ge=0, le=8760, validation_alias="GRB_CACHE_TTL_HOURS")
official_vector_enabled: bool = Field(default=True, validation_alias="OFFICIAL_VECTOR_ENABLED")
bwk_wfs_url: str = Field(
default="https://geo.api.vlaanderen.be/BWK/wfs",
validation_alias="BWK_WFS_URL",
)
dov_soil_wfs_url: str = Field(
default="https://www.dov.vlaanderen.be/geoserver/wfs",
validation_alias="DOV_SOIL_WFS_URL",
)
official_vector_min_side_m: float = Field(
default=10.0,
gt=0,
validation_alias="OFFICIAL_VECTOR_MIN_SIDE_M",
)
official_vector_max_side_m: float = Field(
default=20_000.0,
gt=0,
validation_alias="OFFICIAL_VECTOR_MAX_SIDE_M",
)
official_vector_page_size: int = Field(
default=1000,
ge=1,
le=2000,
validation_alias="OFFICIAL_VECTOR_PAGE_SIZE",
)
official_vector_max_pages: int = Field(
default=200,
ge=1,
le=1000,
validation_alias="OFFICIAL_VECTOR_MAX_PAGES",
)
official_vector_max_features: int = Field(
default=100_000,
ge=1,
validation_alias="OFFICIAL_VECTOR_MAX_FEATURES",
)
official_vector_timeout_seconds: int = Field(
default=180,
ge=1,
le=600,
validation_alias="OFFICIAL_VECTOR_TIMEOUT_SECONDS",
)
official_vector_max_response_mb: int = Field(
default=20,
ge=1,
le=100,
validation_alias="OFFICIAL_VECTOR_MAX_RESPONSE_MB",
)
official_vector_max_total_response_mb: int = Field(
default=256,
ge=1,
le=2048,
validation_alias="OFFICIAL_VECTOR_MAX_TOTAL_RESPONSE_MB",
)
official_vector_cache_ttl_hours: int = Field(
default=24,
ge=0,
le=8760,
validation_alias="OFFICIAL_VECTOR_CACHE_TTL_HOURS",
)
dhmv_enabled: bool = Field(default=True, validation_alias="DHMV_ENABLED")
dhmv_wcs_url: str = Field(
default="https://geo.api.vlaanderen.be/DHMV/wcs",
+8
View File
@@ -18,6 +18,11 @@ from .source_catalog import (
)
from .grb_refresh import GrbRefreshLayerPlan, GrbRefreshPlan, GrbRefreshPlanSummary
from .grb import GrbAcquireRequest, GrbAcquisitionResult, GrbProductRead
from .official_vector import (
OfficialVectorAcquireRequest,
OfficialVectorAcquisitionResult,
OfficialVectorProductRead,
)
from .detection import (
DetectionListResponse,
DetectionModelCapability,
@@ -165,6 +170,9 @@ __all__ = [
"GrbAcquireRequest",
"GrbAcquisitionResult",
"GrbProductRead",
"OfficialVectorAcquireRequest",
"OfficialVectorAcquisitionResult",
"OfficialVectorProductRead",
"DetectionListResponse",
"DetectionModelCapability",
"DetectionModelsResponse",
+54
View File
@@ -0,0 +1,54 @@
from __future__ import annotations
from uuid import UUID
from pydantic import BaseModel
from .operations import VectorSelectionBBox
class OfficialVectorAcquireRequest(BaseModel):
bbox: VectorSelectionBBox
area_id: UUID | None = None
product_key: str
force_refresh: bool = False
class OfficialVectorProductRead(BaseModel):
key: str
display_name: str
theme: str
provider: str
source_name: str
reference_layer_name: str
service_type: str
collection: str
geometry_types: list[str]
source_crs: str
source_version: str
observation_label: str
authority_level: str
catalog_url: str
attribution: str
license_note: str
limitation_message: str
class OfficialVectorAcquisitionResult(BaseModel):
output_dataset_id: UUID
reused: bool
product_key: str
display_name: str
theme: str
provider: str
source_name: str
reference_layer_name: str
service_type: str
collection: str
feature_count: int
candidate_feature_count: int
page_count: int
bbox_epsg4326: list[float]
source_version: str
attribution: str
limitation_message: str
+1
View File
@@ -30,6 +30,7 @@ class ThematicRasterProductRead(BaseModel):
license_note: str
legend_min_label: str
legend_max_label: str
included_source_values: list[int]
limitation_message: str
File diff suppressed because it is too large Load Diff
@@ -45,6 +45,7 @@ class ThematicRasterProduct:
legend_min_label: str
legend_max_label: str
limitation_message: str
included_source_values: tuple[int, ...] = ()
class ThematicRasterAcquisitionService:
@@ -100,6 +101,44 @@ class ThematicRasterAcquisitionService:
"synoniem met natuur, bos, publieke toegankelijkheid of planologische bestemming."
),
),
ThematicRasterProduct(
key="forest_land_use_2025",
display_name="Bos volgens Landgebruik Vlaanderen 2025",
theme="forest",
metric_kind="binary_area",
coverage_id="lu:lu_landgebruik_vlaa_2025_v3",
native_resolution_m=10.0,
source_value_unit="class_0_1",
observation_year=2025,
source_version="Toestand 2025 versie 3",
catalog_url="https://www.vlaanderen.be/datavindplaats/catalogus/landgebruik-vlaanderen-toestand-2025",
legend_min_label="Geen bosklasse",
legend_max_label="Bos",
limitation_message=(
"10 m-afleiding van bronklasse 12 (bos) uit Landgebruik Vlaanderen 2025. De oppervlakte is "
"resolutiegebonden en vormt geen juridische bosgrens, boomtelling, kroonbedekking of houtvolume."
),
included_source_values=(12,),
),
ThematicRasterProduct(
key="agricultural_land_use_2025",
display_name="Akker en landbouwgrasland 2025",
theme="agriculture",
metric_kind="binary_area",
coverage_id="lu:lu_landgebruik_vlaa_2025_v3",
native_resolution_m=10.0,
source_value_unit="class_0_1",
observation_year=2025,
source_version="Toestand 2025 versie 3",
catalog_url="https://www.vlaanderen.be/datavindplaats/catalogus/landgebruik-vlaanderen-toestand-2025",
legend_min_label="Ander landgebruik",
legend_max_label="Akker of landbouwgrasland",
limitation_message=(
"10 m-afleiding van bronklassen 13 (akker) en 14 (grasland in landbouwgebruik). Dit is werkelijk "
"landgebruik en geen ALZ-perceelaangifte, eigendomsgrens, teeltregister of juridische bestemming."
),
included_source_values=(13, 14),
),
ThematicRasterProduct(
key="population_density_2019",
display_name="Inwonersdichtheid per hectare 2019",
@@ -176,6 +215,7 @@ class ThematicRasterAcquisitionService:
license_note=ThematicRasterAcquisitionService.LICENSE_NOTE,
legend_min_label=product.legend_min_label,
legend_max_label=product.legend_max_label,
included_source_values=list(product.included_source_values),
limitation_message=product.limitation_message,
).model_dump()
for product in ThematicRasterAcquisitionService._products().values()
@@ -461,7 +501,29 @@ class ThematicRasterAcquisitionService:
invalid = np.ma.getmaskarray(band) | ~np.isfinite(raw)
if source.nodata is not None:
invalid |= np.isclose(raw, float(source.nodata))
normalized = np.ma.array(raw, mask=invalid)
source_values = np.ma.array(raw, mask=invalid).compressed().astype("float64")
if product.included_source_values:
rounded = np.rint(source_values)
if not np.allclose(source_values, rounded, atol=0.0001):
raise AppError(
code="THEMATIC_RASTER_INVALID_VALUES",
message="Categorical land-use coverage contains non-integer source classes",
status_code=502,
)
if source_values.size and (
float(source_values.min()) < 0
or float(source_values.max()) > 255
):
raise AppError(
code="THEMATIC_RASTER_INVALID_VALUES",
message="Categorical land-use coverage contains source classes outside the governed range",
status_code=502,
)
source_classes = np.where(invalid, 0, np.rint(raw)).astype("int16")
binary = np.isin(source_classes, product.included_source_values).astype("float32")
normalized = np.ma.array(binary, mask=invalid)
else:
normalized = np.ma.array(raw, mask=invalid)
values = normalized.compressed().astype("float64")
ThematicRasterAcquisitionService._validate_values(values, product)
profile = source.profile.copy()
@@ -482,6 +544,9 @@ class ThematicRasterAcquisitionService:
"maximum_value": float(values.max()),
"p02_value": float(np.percentile(values, 2)),
"p98_value": float(np.percentile(values, 98)),
"included_source_values": list(product.included_source_values),
"source_minimum_value": float(source_values.min()),
"source_maximum_value": float(source_values.max()),
}
except AppError:
raise
@@ -563,6 +628,7 @@ class ThematicRasterAcquisitionService:
"analysis_resolution_m": product.native_resolution_m,
"source_crs": ThematicRasterAcquisitionService.SOURCE_CRS,
"source_value_unit": product.source_value_unit,
"included_source_values": list(product.included_source_values),
"observation_year": product.observation_year,
"observation_date_precision": "year",
"valid_pixel_count": validation["valid_pixel_count"],
@@ -68,6 +68,10 @@ class ThematicRasterAnalysisService:
@staticmethod
def _unsupported_metrics(product: ThematicRasterProduct) -> list[str]:
if product.metric_kind == "binary_area":
if product.theme == "forest":
return ["tree_count", "canopy_cover", "timber_volume", "legal_forest_boundary"]
if product.theme == "agriculture":
return ["declared_parcel_area", "crop_declaration", "ownership", "cadastral_area"]
return ["object_count", "parcel_area", "current_land_use"]
if product.metric_kind == "population_density":
return ["current_population", "household_count", "address_level_population"]
@@ -152,7 +156,12 @@ class ThematicRasterAnalysisService:
positive_count = int(np.count_nonzero(values >= 0.5))
positive_area_ha = positive_count * cell_area_m2 / 10_000.0
positive_share = positive_count / max(1, valid_cell_count) * 100.0
label = "Ruimtebeslag" if product.theme == "space_occupation" else "Open ruimte"
label = {
"space_occupation": "Ruimtebeslag",
"open_space": "Open ruimte",
"forest": "Bos",
"agriculture": "Akker en landbouwgrasland",
}[product.theme]
metrics = [
metric(f"{product.theme}_area_ha", f"{label} in selectie", positive_area_ha, "ha", "positive_source_cells_times_cell_area"),
metric(f"{product.theme}_share_pct", f"Aandeel {label.lower()}", positive_share, "%", "positive_source_cells_divided_by_valid_selected_cells"),
@@ -216,6 +225,8 @@ class ThematicRasterAnalysisService:
palettes = {
"space_occupation": np.asarray([[251, 231, 211], [190, 62, 51]], dtype="float64"),
"open_space": np.asarray([[221, 238, 219], [38, 122, 70]], dtype="float64"),
"forest": np.asarray([[223, 237, 226], [43, 117, 72]], dtype="float64"),
"agriculture": np.asarray([[245, 237, 204], [166, 122, 35]], dtype="float64"),
"population": np.asarray([[238, 231, 246], [103, 58, 151]], dtype="float64"),
"accessibility": np.asarray([[233, 241, 244], [15, 118, 110]], dtype="float64"),
"services": np.asarray([[255, 244, 191], [182, 109, 22]], dtype="float64"),