Complete temporal detection safety gate

backend/app/services/detection_service.py

@@ -2,6 +2,7 @@ from __future__ import annotations
 
 import uuid
 import json
+import logging
 from datetime import UTC, datetime
 from pathlib import Path
 from typing import Any
@@ -13,6 +14,7 @@ from sqlalchemy import func
 
 from app.core.config import Settings, get_settings
 from app.core.errors import AppError
+from app.core.request_context import get_request_id
 from app.models import AnalysisRun, Dataset, Detection, Job, Project, VectorFeature
 from app.schemas.detection import DetectionListResponse, DetectionRead, DetectionRunListResponse, DetectionRunRead, DetectionRunResponse
 from app.services.detection_georeferencing import pixel_bbox_to_epsg4326_polygon
@@ -21,9 +23,13 @@ from app.services.model_asset_catalog_service import ModelAssetCatalogService
 from app.services.model_registry_service import ModelRegistryService
 from app.services.qa_service import QaService
 from app.services.quality_service import QualityService
+from app.services.temporal_compatibility_service import TemporalCompatibilityService
 from app.services.yolo_adapter import YoloDetectionAdapter
 
 
+logger = logging.getLogger("geointel.detection")
+
+
 class DetectionService:
     @staticmethod
     def _now() -> datetime:
@@ -58,6 +64,7 @@ class DetectionService:
                 details={"dataset_type": dataset.dataset_type},
                 status_code=400,
             )
+        TemporalCompatibilityService.ensure_detection_source_supported(dataset)
 
         selected_model_asset = None
         if model_id == resolved_settings.yolo_model_id and model_asset_id:
@@ -96,6 +103,15 @@ class DetectionService:
         }
         job = DetectionService._create_job(db, project_id, dataset_id, run_parameters)
         analysis_run = DetectionService._create_analysis_run(db, project_id, dataset_id, job.id, model, run_parameters)
+        logger.info(
+            "detection_started request_id=%s project_id=%s dataset_id=%s job_id=%s analysis_run_id=%s model_id=%s",
+            get_request_id(),
+            project_id,
+            dataset_id,
+            job.id,
+            analysis_run.id,
+            model.model_id,
+        )
 
         if not model.configured:
             message = model.limitation_message
@@ -281,6 +297,13 @@ class DetectionService:
             raise AppError(code="INVALID_DATASET_SCOPE", message="Reference dataset does not belong to detection project", status_code=400)
         if reference_dataset.dataset_type not in {"vector", "geojson"}:
             raise AppError(code="INVALID_DATASET_TYPE", message="Reference dataset must be vector data", status_code=400)
+        candidate_dataset = db.get(Dataset, run.dataset_id)
+        if not candidate_dataset:
+            raise AppError(code="DATASET_NOT_FOUND", message="Detection source dataset not found", status_code=404)
+        temporal_compatibility = TemporalCompatibilityService.assess_detection_qa(
+            candidate_dataset,
+            reference_dataset,
+        )
 
         detections = DetectionService._query_detection_rows(
             db,
@@ -421,6 +444,7 @@ class DetectionService:
                 "class_name": class_name,
                 "min_confidence": min_confidence,
                 "coverage_policy": coverage_summary["mode"],
+                "temporal_compatibility": temporal_compatibility,
             },
             findings={
                 "matches": evidence.matches,
@@ -429,6 +453,7 @@ class DetectionService:
                 "warnings": coverage_warnings + evidence.warnings,
                 "unsupported_geometry": evidence.unsupported,
                 "coverage": coverage_summary,
+                "temporal_compatibility": temporal_compatibility,
                 "box_to_footprint_diagnostics": box_to_footprint_diagnostics,
                 "match_evidence": evidence.match_evidence,
                 "false_positive_evidence": evidence.false_positive_evidence,
@@ -443,6 +468,17 @@ class DetectionService:
                 "false_negative_count": evidence.false_negatives,
             },
         )
+        logger.info(
+            "detection_qa_completed request_id=%s job_id=%s analysis_run_id=%s quality_check_id=%s "
+            "candidate_dataset_id=%s reference_dataset_id=%s status=%s",
+            get_request_id(),
+            run.job_id,
+            analysis_run_id,
+            quality_check.id,
+            run.dataset_id,
+            reference_dataset_id,
+            status,
+        )
         return {
             "status": status,
             "quality_check_id": str(quality_check.id),
@@ -462,6 +498,7 @@ class DetectionService:
             "iou_threshold": iou_threshold,
             "warnings": coverage_warnings + evidence.warnings,
             "coverage": coverage_summary,
+            "temporal_compatibility": temporal_compatibility,
             "box_to_footprint_diagnostics": box_to_footprint_diagnostics,
             "match_evidence": evidence.match_evidence,
             "false_positive_evidence": evidence.false_positive_evidence,