Error Mapping for Spatial API Calls

Error Mapping for Spatial API Calls is a foundational discipline for production-grade geospatial systems where raw HTTP status codes rarely capture the…

Error Mapping for Spatial API Calls is a foundational discipline for production-grade geospatial systems where raw HTTP status codes rarely capture the semantic reality of spatial failures. Unlike conventional REST endpoints, spatial services frequently return 200 OK responses containing non-closed polygons, silent coordinate reference system (CRS) mismatches, or topology violations that only surface during downstream rendering or spatial joins. For AI/ML engineers, spatial data scientists, and platform teams, implementing a structured error taxonomy is non-negotiable. It intercepts, classifies, and routes spatial deterministically, preventing brittle LLM-driven pipelines from degrading into infinite retry loops when confronted with malformed coordinate arrays or constraint violations.

Architectural Foundations for Spatial Error Taxonomy

A resilient spatial error architecture begins by decoupling transport-layer failures from domain-level spatial failures. Network errors (timeouts, 5xx responses, TLS handshake failures) require exponential backoff and circuit breaking. Spatial errors (self-intersections, out-of-bounds coordinates, projection drift, invalid GeoJSON structures) require geometric validation, coordinate transformation, or query rewriting. The classification layer must parse both the HTTP envelope and the spatial payload, extracting diagnostic metadata such as ST_IsValidReason, RFC 7946 bbox violations, or OGC API conformance deviations.

When an LLM agent generates a spatial query, the execution environment must capture failures and map them to structured error objects before deciding whether to retry, fallback to a cached dataset, or prompt the agent for correction. This handoff is particularly relevant when integrating with Geospatial Prompt Engineering & Tool Routing, where error mapping serves as the deterministic translation layer between brittle external services and resilient agent workflows.

Step-by-Step Implementation: Parsing and Classification

The first implementation step involves wrapping spatial API clients with a unified response parser that normalizes error payloads into a consistent schema. Python developers should leverage Pydantic models to enforce strict typing on spatial error envelopes, capturing both the HTTP status and the spatial diagnostic payload. The parser must inspect Content-Type headers, decode GeoJSON or WKB payloads, and run immediate geometric validation before exposing data to downstream consumers.

import json
import logging
from enum import Enum
from typing import Optional, Any, Dict, List
from pydantic import BaseModel, Field, ValidationError
from shapely.geometry import shape, mapping, GeometryCollection
from shapely.validation import explain_validity
import requests

logger = logging.getLogger(__name__)

class SpatialErrorCode(str, Enum):
    NETWORK_TIMEOUT = "network.timeout"
    HTTP_SERVER_ERROR = "http.server_error"
    CRS_MISMATCH = "spatial.crs_mismatch"
    TOPOLOGY_INVALID = "spatial.topology_invalid"
    BOUNDS_VIOLATION = "spatial.bounds_violation"
    PAYLOAD_MALFORMED = "spatial.payload_malformed"

class SpatialErrorEnvelope(BaseModel):
    http_status: int
    error_code: SpatialErrorCode
    message: str
    raw_response: Optional[str] = None
    diagnostic: Optional[Dict[str, Any]] = None
    retryable: bool = Field(default=False)

class SpatialResponseParser:
    """Production-ready parser for spatial API responses with strict error mapping."""

    VALID_CRS = "http://www.opengis.net/def/crs/OGC/1.3/CRS84"  # RFC 7946 default
    GEOJSON_TYPE = "application/geo+json"

    @staticmethod
    def parse_response(response: requests.Response) -> SpatialErrorEnvelope:
        if response.status_code >= 500:
            return SpatialErrorEnvelope(
                http_status=response.status_code,
                error_code=SpatialErrorCode.HTTP_SERVER_ERROR,
                message=f"Server-side spatial service failure: {response.status_code}",
                retryable=True
            )

        if response.status_code == 408 or isinstance(response, requests.exceptions.Timeout):
            return SpatialErrorEnvelope(
                http_status=408,
                error_code=SpatialErrorCode.NETWORK_TIMEOUT,
                message="Spatial API request timed out",
                retryable=True
            )

        if response.status_code != 200:
            return SpatialErrorEnvelope(
                http_status=response.status_code,
                error_code=SpatialErrorCode.PAYLOAD_MALFORMED,
                message=f"Unexpected HTTP status: {response.status_code}",
                retryable=False
            )

        # Parse spatial payload
        try:
            payload = response.json()
        except json.JSONDecodeError as e:
            return SpatialErrorEnvelope(
                http_status=200,
                error_code=SpatialErrorCode.PAYLOAD_MALFORMED,
                message=f"Invalid JSON in spatial payload: {str(e)}",
                retryable=False
            )

        # Validate CRS
        crs = payload.get("crs", {}).get("properties", {}).get("name", SpatialResponseParser.VALID_CRS)
        if crs != SpatialResponseParser.VALID_CRS:
            return SpatialErrorEnvelope(
                http_status=200,
                error_code=SpatialErrorCode.CRS_MISMATCH,
                message=f"CRS mismatch detected: {crs}. Expected {SpatialResponseParser.VALID_CRS}",
                diagnostic={"found_crs": crs, "expected_crs": SpatialResponseParser.VALID_CRS},
                retryable=False
            )

        # Validate topology
        if "features" in payload:
            for idx, feature in enumerate(payload["features"]):
                geom = feature.get("geometry")
                if geom:
                    try:
                        shp = shape(geom)
                        if not shp.is_valid:
                            reason = explain_validity(shp)
                            return SpatialErrorEnvelope(
                                http_status=200,
                                error_code=SpatialErrorCode.TOPOLOGY_INVALID,
                                message=f"Invalid geometry at feature index {idx}: {reason}",
                                diagnostic={"feature_index": idx, "validity_reason": reason},
                                retryable=False
                            )
                    except Exception as e:
                        return SpatialErrorEnvelope(
                            http_status=200,
                            error_code=SpatialErrorCode.TOPOLOGY_INVALID,
                            message=f"Failed to parse geometry at index {idx}: {str(e)}",
                            retryable=False
                        )

        return SpatialErrorEnvelope(
            http_status=200,
            error_code=SpatialErrorCode.PAYLOAD_MALFORMED,
            message="Response parsed successfully",
            retryable=False
        )

Enforcing CRS and Topology Constraints

Silent spatial failures often propagate through pipelines until they trigger hard crashes in visualization layers or spatial join operations. The parser above enforces RFC 7946 compliance by defaulting to CRS84 (EPSG:4326 longitude/latitude) and explicitly rejecting payloads that declare alternative projections without explicit transformation metadata. For production systems, coordinate bounds validation should also be applied to catch NaN, Infinity, or coordinates exceeding [-180, 180] and [-90, 90] when operating in WGS84.

Topology enforcement relies on rigorous validation of geometric primitives. Self-intersecting polygons, duplicate vertices, and unclosed rings are common artifacts of automated digitization or LLM-generated coordinate arrays. By leveraging shapely.validation.explain_validity, the system extracts human-readable diagnostic strings (e.g., "Self-intersection[12.45 45.67]") that can be fed directly into agent correction loops. This approach aligns with Topology Rule Enforcement via LLMs, where structured validity reports replace generic exception traces, enabling models to autonomously rewrite malformed spatial predicates or apply automated snapping buffers.

For authoritative validation standards, refer to the OGC API Features specification, which defines conformance classes for spatial data exchange and explicitly addresses error reporting for invalid feature geometries.

Routing Failures in LLM-Driven Pipelines

Once an error is classified, the routing layer determines the next action. Network timeouts and 5xx errors trigger exponential backoff with jitter. Payload or topology errors trigger deterministic fallbacks:

  1. Query Rewriting: Strip invalid predicates, apply ST_Buffer(geom, 0) to auto-heal minor topology violations, or switch to a simplified bounding-box query.
  2. Agent Correction: Pass the diagnostic payload to the LLM with a structured system prompt requesting coordinate correction or predicate refinement.
  3. Graceful Degradation: Return a cached, lower-resolution tile or feature set while logging the failure for model fine-tuning.

This routing logic is critical when integrating with Prompt-to-Spatial-SQL Generation, where LLMs frequently generate syntactically valid but semantically broken spatial predicates (e.g., ST_Intersects on mismatched CRS or invalid polygon rings). By intercepting the resulting SpatialErrorEnvelope, the pipeline avoids cascading PostGIS planner failures and instead routes the diagnostic context back to the generation step for self-correction.

To translate these technical envelopes into actionable agent instructions, see Mapping Spatial API Errors to User-Friendly Prompts, which details how to convert explain_validity outputs and CRS mismatch codes into constrained natural language prompts that guide LLMs toward valid spatial outputs.

Production Hardening and Observability

In asynchronous geoprocessing workflows, error mapping must be thread-safe and non-blocking. Implement a centralized error registry that aggregates SpatialErrorEnvelope instances, exposing metrics for Prometheus/Grafana dashboards. Key observability signals include:

  • spatial_error_rate by error_code
  • crs_mismatch_frequency by upstream provider
  • llm_correction_success_rate after topology routing

Use connection pooling with strict timeouts, and wrap all spatial API calls in a circuit breaker pattern. When the error rate exceeds a defined threshold (e.g., 15% topology failures in a 5-minute window), the circuit should open, routing all subsequent requests to a fallback provider or cache until validation metrics stabilize. For implementation details on async spatial validation, consult the official Shapely validation documentation, which outlines thread-safe geometry parsing and validation best practices.

Error mapping is not merely a defensive programming practice; it is the control plane for autonomous geospatial AI. By standardizing how spatial failures are captured, classified, and routed, platform teams transform brittle API integrations into resilient, self-healing pipelines capable of supporting complex LLM-driven spatial reasoning at scale.