Strict Pydantic Validation for Geometry

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Geospatial APIs sit at the boundary between loosely typed client payloads and a database engine that demands mathematically sound geometries. When coordinates, ring orientation, or coordinate reference systems drift from specification, PostGIS operations fail silently, corrupt spatial indexes, or trigger expensive rollback transactions. Enforcing strict geometry contracts at the API boundary — before payloads reach the database — eliminates these failure modes and gives clients actionable error messages instead of cryptic 500 responses.

This page walks through a production-ready Pydantic v2 validation pipeline for FastAPI, covering type normalization, structural and topological enforcement, coordinate-bounds guards, error serialization, and end-to-end integration with spatial query endpoints.

# Prerequisites & Environment

Before implementing strict validation, ensure your stack meets these baseline requirements:

• Python 3.10+ — required for Pydantic v2’s typing.Annotated and ParamSpec support.
• FastAPI 0.100+ — uses Pydantic v2 natively for request parsing and automatic OpenAPI schema generation.
• Pydantic 2.5+ — provides @field_validator, @model_validator, BeforeValidator, and AfterValidator.
• Shapely 2.0+ — optional but recommended for full topological checks (is_valid, make_valid) inside validators.
• PostGIS 3.3+ — target database; validation occurs upstream, but CRS assumptions follow PostGIS WGS84 conventions.

Install with strict version pinning to prevent silent breaking changes in spatial type handling:

pip install "fastapi>=0.100.0" "pydantic>=2.5.0" "uvicorn[standard]" "shapely>=2.0" "geojson-pydantic>=1.0"

# Decision Matrix: Where to Validate Geometry

Choose the right validation layer for each check type. Running expensive topology operations in Pydantic (application layer) is overkill; skipping coordinate-bound checks in the database is a silent data corruption risk.

Rule of thumb: validate structure and bounds in Pydantic; delegate deep topology to Shapely inside an AfterValidator; never rely on PostGIS as the first line of defense.

# Step-by-Step Implementation

# Step 1 — Type Normalization with BeforeValidator

Clients send geometry as raw dicts, JSON strings, or geojson-pydantic objects. A BeforeValidator normalizes all forms into a canonical dict before Pydantic touches any fields.

from typing import Any, Annotated
from pydantic import BeforeValidator
import json

def normalize_geometry(raw: Any) -> dict[str, Any]:
    """Coerce raw JSON dicts or JSON strings into a standardized GeoJSON-like dict."""
    if isinstance(raw, dict):
        return raw
    if isinstance(raw, str):
        try:
            parsed = json.loads(raw)
            if not isinstance(parsed, dict):
                raise ValueError("JSON string did not parse to a dict")
            return parsed
        except json.JSONDecodeError as exc:
            raise ValueError(f"Invalid JSON payload: {exc}") from exc
    raise TypeError(f"Expected dict or JSON string, got {type(raw).__name__}")

# Reusable annotated type — apply to any model field
GeometryInput = Annotated[dict[str, Any], BeforeValidator(normalize_geometry)]

This type alias can be reused across all your request models, keeping normalization logic in one place as detailed in Validating WKT and GeoJSON with Pydantic v2.

# Step 2 — Structural & Topological Enforcement

Once normalized, enforce mandatory GeoJSON structure, validate coordinate array depth, and check ring closure for polygons.

from typing import Any, Literal
from pydantic import BaseModel, field_validator, model_validator

VALID_GEOMETRY_TYPES = {
    "Point", "LineString", "Polygon",
    "MultiPoint", "MultiLineString", "MultiPolygon",
}

class StrictGeometry(BaseModel):
    type: Literal[
        "Point", "LineString", "Polygon",
        "MultiPoint", "MultiLineString", "MultiPolygon"
    ]
    coordinates: list[Any]

    @field_validator("type", mode="before")
    @classmethod
    def validate_geometry_type(cls, v: Any) -> str:
        if v not in VALID_GEOMETRY_TYPES:
            raise ValueError(
                f"Unsupported geometry type '{v}'. "
                f"Must be one of: {', '.join(sorted(VALID_GEOMETRY_TYPES))}"
            )
        return v

    @field_validator("coordinates", mode="after")
    @classmethod
    def validate_coordinate_structure(cls, v: list[Any], info) -> list[Any]:
        geom_type = info.data.get("type")

        if geom_type == "Point":
            if not isinstance(v, list) or len(v) < 2:
                raise ValueError(
                    "Point coordinates must contain at least [longitude, latitude]"
                )
            if len(v) > 3:
                raise ValueError(
                    "Point coordinates must be 2D [lon, lat] or 3D [lon, lat, alt]"
                )

        elif geom_type == "LineString":
            if not isinstance(v, list) or len(v) < 2:
                raise ValueError("LineString must contain at least 2 positions")

        elif geom_type == "Polygon":
            if not isinstance(v, list) or len(v) == 0:
                raise ValueError("Polygon must contain at least one linear ring")
            exterior = v[0]
            if not isinstance(exterior, list) or len(exterior) < 4:
                raise ValueError(
                    "Polygon exterior ring must contain at least 4 positions"
                )
            if exterior[0] != exterior[-1]:
                raise ValueError(
                    "Polygon exterior ring must be closed: "
                    "first coordinate must equal last coordinate"
                )
            # Validate hole rings
            for idx, hole in enumerate(v[1:], start=1):
                if not isinstance(hole, list) or len(hole) < 4:
                    raise ValueError(
                        f"Polygon hole ring {idx} must contain at least 4 positions"
                    )
                if hole[0] != hole[-1]:
                    raise ValueError(
                        f"Polygon hole ring {idx} must be closed"
                    )

        return v

# Step 3 — Coordinate Bounds & Precision Guards

Coordinates that exceed WGS84 ranges or carry excessive floating-point precision break spatial index lookups and cause subtle rounding errors in ST_DWithin distance calculations.

class StrictGeometryWithBounds(StrictGeometry):
    """Extends StrictGeometry with WGS84 bounds checking and 6 dp precision clamping."""

    @field_validator("coordinates", mode="before")
    @classmethod
    def enforce_bounds_and_precision(cls, v: Any) -> Any:

        def clamp_and_round(coord: Any) -> Any:
            """Recursively round all numeric values to 6 decimal places."""
            if isinstance(coord, list):
                return [clamp_and_round(c) for c in coord]
            if isinstance(coord, (int, float)):
                return round(float(coord), 6)
            return coord

        def validate_wgs84(coord: Any) -> None:
            """Recursively find [lon, lat, ?alt] leaf arrays and range-check them."""
            if not isinstance(coord, list):
                return
            # Leaf position: list of numbers
            if len(coord) >= 2 and isinstance(coord[0], (int, float)):
                lon, lat = coord[0], coord[1]
                if not (-180.0 <= lon <= 180.0):
                    raise ValueError(
                        f"Longitude {lon} is outside WGS84 range [-180, 180]"
                    )
                if not (-90.0 <= lat <= 90.0):
                    raise ValueError(
                        f"Latitude {lat} is outside WGS84 range [-90, 90]"
                    )
            else:
                for c in coord:
                    validate_wgs84(c)

        rounded = clamp_and_round(v)
        validate_wgs84(rounded)
        return rounded

# Step 4 — Optional Shapely Topology Check

For endpoints that accept polygons from untrusted sources, add a full topology check using Shapely’s is_valid inside an AfterValidator. This catches self-intersecting rings that ring-closure checks alone cannot detect.

from pydantic import AfterValidator
from shapely import from_geojson, is_valid, is_valid_reason
import json

def check_topology(geom_dict: dict[str, Any]) -> dict[str, Any]:
    """Run Shapely's full topology validation on an already-normalized geometry dict."""
    try:
        shape = from_geojson(json.dumps(geom_dict))
    except Exception as exc:
        raise ValueError(f"Shapely could not parse geometry: {exc}") from exc
    if not is_valid(shape):
        reason = is_valid_reason(shape)
        raise ValueError(f"Geometry is topologically invalid: {reason}")
    return geom_dict

# Apply as a composed annotated type for high-trust polygon endpoints
ValidatedPolygonInput = Annotated[
    dict[str, Any],
    BeforeValidator(normalize_geometry),
    AfterValidator(check_topology),
]

# Step 5 — Error Serialization

Pydantic’s raw ValidationError output is detailed but unsuitable for direct API responses. Map failures to RFC 7807 problem details so all spatial endpoints return consistent error structures. These same errors appear in Bounding Box & Spatial Index Queries endpoints, making uniform error taxonomy essential across the API.

from fastapi import FastAPI, Request
from fastapi.responses import JSONResponse
from pydantic import ValidationError

app = FastAPI()

@app.exception_handler(ValidationError)
async def spatial_validation_handler(
    request: Request, exc: ValidationError
) -> JSONResponse:
    errors = [
        {
            "field": ".".join(str(loc) for loc in err["loc"]),
            "message": err["msg"],
            "type": err["type"],
        }
        for err in exc.errors(include_url=False)
    ]
    return JSONResponse(
        status_code=422,
        content={
            "type": "https://tools.ietf.org/html/rfc7807",
            "title": "Spatial Validation Failed",
            "detail": "One or more geometry constraints were violated.",
            "errors": errors,
        },
    )

# Production Code Example

A complete, copy-runnable FastAPI route that accepts a GeoJSON geometry payload, runs the full validation pipeline, and returns a PostGIS-ready WKB hex string. Pre-validated bounding boxes fed into ST_Within or ST_Intersects via this route eliminate redundant ST_MakeValid calls in Bounding Box & Spatial Index Queries.

from typing import Any, Annotated, Literal
from fastapi import FastAPI, Depends
from pydantic import BaseModel, BeforeValidator, AfterValidator, field_validator
import json
import asyncpg

# --- Validators (defined in earlier steps) ---

def normalize_geometry(raw: Any) -> dict[str, Any]:
    if isinstance(raw, dict):
        return raw
    if isinstance(raw, str):
        parsed = json.loads(raw)
        if not isinstance(parsed, dict):
            raise ValueError("Expected a GeoJSON object")
        return parsed
    raise TypeError(f"Expected dict or JSON string, got {type(raw).__name__}")

def validate_wgs84_bounds(geom: dict[str, Any]) -> dict[str, Any]:
    def _check(coord: Any) -> None:
        if not isinstance(coord, list):
            return
        if len(coord) >= 2 and isinstance(coord[0], (int, float)):
            lon, lat = coord[0], coord[1]
            if not (-180.0 <= lon <= 180.0):
                raise ValueError(f"Longitude {lon} out of range")
            if not (-90.0 <= lat <= 90.0):
                raise ValueError(f"Latitude {lat} out of range")
        else:
            for c in coord:
                _check(c)
    _check(geom.get("coordinates", []))
    return geom

GeometryInput = Annotated[
    dict[str, Any],
    BeforeValidator(normalize_geometry),
    AfterValidator(validate_wgs84_bounds),
]

# --- Request & Response Models ---

class GeometryIngestRequest(BaseModel):
    geometry: GeometryInput
    srid: int = 4326

class GeometryIngestResponse(BaseModel):
    wkb_hex: str
    geom_type: str
    srid: int

# --- Database helper ---

async def get_db() -> asyncpg.Connection:
    return await asyncpg.connect("postgresql://user:pass@localhost/spatial_db")

# --- Route ---

app = FastAPI()

@app.post("/geometry/ingest", response_model=GeometryIngestResponse)
async def ingest_geometry(
    payload: GeometryIngestRequest,
    conn: asyncpg.Connection = Depends(get_db),
) -> GeometryIngestResponse:
    geojson_str = json.dumps(payload.geometry)
    row = await conn.fetchrow(
        """
        SELECT
            ST_AsEWKB(
                ST_SetSRID(ST_GeomFromGeoJSON($1), $2)
            )::text AS wkb_hex,
            ST_GeometryType(ST_GeomFromGeoJSON($1)) AS geom_type
        """,
        geojson_str,
        payload.srid,
    )
    return GeometryIngestResponse(
        wkb_hex=row["wkb_hex"],
        geom_type=row["geom_type"],
        srid=payload.srid,
    )

# Verification & Testing

# curl smoke test

# Valid polygon — expect 200
curl -s -X POST http://localhost:8000/geometry/ingest \
  -H "Content-Type: application/json" \
  -d '{
    "geometry": {
      "type": "Polygon",
      "coordinates": [[[0,0],[1,0],[1,1],[0,1],[0,0]]]
    }
  }' | python3 -m json.tool

# Unclosed ring — expect 422
curl -s -X POST http://localhost:8000/geometry/ingest \
  -H "Content-Type: application/json" \
  -d '{
    "geometry": {
      "type": "Polygon",
      "coordinates": [[[0,0],[1,0],[1,1],[0,1]]]
    }
  }' | python3 -m json.tool

Expected error response for the unclosed ring:

{
  "type": "https://tools.ietf.org/html/rfc7807",
  "title": "Spatial Validation Failed",
  "detail": "One or more geometry constraints were violated.",
  "errors": [
    {
      "field": "geometry.coordinates",
      "message": "Value error, Polygon exterior ring must be closed: first coordinate must equal last coordinate",
      "type": "value_error"
    }
  ]
}

# Unit test skeleton

import pytest
from pydantic import ValidationError
from your_app.models import StrictGeometryWithBounds

def test_valid_point():
    g = StrictGeometryWithBounds(type="Point", coordinates=[13.404954, 52.520008])
    assert g.coordinates == [13.404954, 52.520008]

def test_longitude_out_of_range():
    with pytest.raises(ValidationError, match="WGS84 range"):
        StrictGeometryWithBounds(type="Point", coordinates=[200.0, 52.5])

def test_unclosed_polygon():
    with pytest.raises(ValidationError, match="must be closed"):
        StrictGeometryWithBounds(
            type="Polygon",
            coordinates=[[[0,0],[1,0],[1,1],[0,1]]]  # missing closing vertex
        )

def test_precision_clamped():
    g = StrictGeometryWithBounds(
        type="Point",
        coordinates=[13.4049540001234, 52.5200080009876]
    )
    # Rounded to 6 dp
    assert g.coordinates[0] == 13.404954
    assert g.coordinates[1] == 52.520008

# Failure Modes & Edge Cases

1. type field missing from payload — Pydantic raises field required before any geometry validator runs. Ensure type is listed before coordinates in model field order; Pydantic v2 processes fields in declaration order, and info.data.get("type") in coordinates validator returns None if type failed validation.

2. WKT strings passed where GeoJSON is expected — normalize_geometry will raise Invalid JSON payload because WKT is not valid JSON. For endpoints that must accept both formats, detect POINT(, POLYGON( prefixes in normalize_geometry and convert via shapely.from_wkt before returning a GeoJSON dict.

3. Coordinate arrays as strings — Some clients serialize coordinates as "[13.4, 52.5]" (a string). BeforeValidator receives the outer dict correctly but the inner coordinate field is a string. Add a secondary normalizer on the coordinates field itself to JSON-parse string arrays.

4. Z-coordinate (altitude) triggers false latitude check — If a 3D point [lon, lat, alt] is passed with a large altitude value, the recursive validate_wgs84 function correctly identifies coord[0] as longitude because len(coord) >= 2 and isinstance(coord[0], float) is true at the leaf level. The altitude value at index 2 is never checked against lat/lon bounds.

5. ST_MakeValid silent repair masking validation gaps — If your PostGIS insert query wraps geometry with ST_MakeValid, you will never see topology errors in production logs. Remove that wrapper after adding Pydantic validators; let the validation layer surface the actual client error.

6. Duplicate ring vertices inflate index size — Rings with repeated consecutive vertices (e.g., [0,0],[0,0],[1,0],...) are structurally valid but degrade GIST index performance. Add an optional deduplication pass, or flag these in a warning log rather than rejecting outright.

7. Empty geometry collections — GeometryCollection with zero members is valid GeoJSON but creates null entries in PostGIS. Add a model_validator that rejects empty GeometryCollection.geometries arrays if your schema does not permit them.

# Performance Notes

• Validator overhead — The normalization + bounds check pipeline adds roughly 0.05–0.2 ms per request on CPython 3.11, measured against a 100-coordinate polygon. This is negligible compared to a PostGIS round-trip (typically 1–10 ms).
• Shapely topology check — from_geojson + is_valid adds 0.5–2 ms for complex polygons with holes. Gate it behind a feature flag or apply it only to Polygon / MultiPolygon types, not Point or LineString.
• Pydantic model instantiation — Constructing StrictGeometryWithBounds inside a FastAPI dependency (rather than directly in the route body) enables caching of the model schema via model_validate and avoids repeated __init__ overhead at high request rates.
• Async compatibility — All validators shown here are synchronous. @field_validator and BeforeValidator must remain sync; async validation logic (e.g., checking geometry existence in the database) belongs in a FastAPI dependency, not a Pydantic validator.
• ST_IsValid in PostGIS — If you skip the Shapely check, a SELECT ST_IsValid(ST_GeomFromGeoJSON($1)) query adds one extra database round-trip per request. Reserve this for high-risk ingestion pipelines (e.g., Async Bulk Uploads with Celery) rather than per-request endpoints.

# Frequently Asked Questions

# Why validate geometry in Pydantic rather than in PostGIS?

PostGIS will accept geometrically invalid inputs and store them silently, corrupting spatial indexes and causing hard-to-debug failures later. Validating at the API boundary returns structured errors to clients immediately, avoids unnecessary database round-trips, and keeps PostGIS GIST indexes clean.

# What is the difference between BeforeValidator and AfterValidator in Pydantic v2?

BeforeValidator runs before Pydantic’s own type coercion, making it ideal for normalizing raw inputs (e.g., converting JSON strings to dicts). AfterValidator runs after the field has its final typed value, making it better for semantic checks like topology validation on an already-structured geometry object.

# Does this validation replace ST_IsValid in PostGIS?

For common cases — ring closure, coordinate bounds, nesting depth — yes. Full topological checks such as self-intersecting rings still require Shapely’s is_valid property or PostGIS ST_IsValid(), which you can call inside an AfterValidator at acceptable overhead.

# Related

• Validating WKT and GeoJSON with Pydantic v2 — format-specific coercion patterns that plug into the pipeline above
• Bounding Box & Spatial Index Queries — how pre-validated geometries accelerate ST_Within and ST_Intersects queries
• K-Nearest Neighbor Routing Algorithms — routing endpoints that benefit from guaranteed coordinate precision
• Async Bulk Uploads with Celery — batch ingestion pipelines where topology checks run as background tasks
• GeoJSON vs GeoParquet Serialization — choosing the right serialization format for validated geometry responses

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