Add Sentinel-2 road freight trends with Analyze Here UI.

Port DrishX truck-motion detection as an opt-in slow layer: on-demand map-center analysis, preset corridors, layer panel toggle, and Docker road-corridor extras.

Co-authored-by: Cursor <cursoragent@cursor.com>

backend/Dockerfile

@@ -45,7 +45,7 @@ COPY uv.lock /workspace/uv.lock
 COPY backend/pyproject.toml /workspace/backend/pyproject.toml
 
 # Install Python dependencies using the lockfile
-RUN cd /workspace/backend && uv sync --frozen --no-dev \
+RUN cd /workspace/backend && uv sync --frozen --no-dev --extra road-corridor \
     && playwright install --with-deps chromium
 
 # Copy backend source code

backend/main.py

@@ -365,6 +365,7 @@ wormhole_router = _load_optional_router("routers.wormhole")
 ai_intel_router = _load_optional_router("routers.ai_intel")
 sar_router = _load_optional_router("routers.sar")
 infonet_router = _load_optional_router("routers.infonet")
+road_corridors_router = _load_optional_router("routers.road_corridors")
 
 
 # ---------------------------------------------------------------------------
@@ -3641,6 +3642,7 @@ app.include_router(wormhole_router)
 app.include_router(ai_intel_router)
 app.include_router(sar_router)
 app.include_router(infonet_router)
+app.include_router(road_corridors_router)
 
 from services.data_fetcher import update_all_data
 

backend/pyproject.toml

@@ -39,6 +39,17 @@ dependencies = [
     "yfinance==1.3.0",
 ]
 
+[project.optional-dependencies]
+road-corridor = [
+    "geopandas>=1.0.0",
+    "imageio>=2.34.0",
+    "osmnx>=2.0.0",
+    "rasterio>=1.4.0",
+    "scikit-learn>=1.5.0",
+    "sentinelhub>=3.10.0",
+    "shapely>=2.0.0",
+]
+
 [dependency-groups]
 dev = ["pytest>=9.0.3", "pytest-asyncio>=1.4.0", "ruff>=0.9.0", "black>=24.0.0"]
 

backend/routers/data.py

@@ -758,7 +758,7 @@ async def live_data_slow(
         "firms_fires", "datacenters", "military_bases", "power_plants", "viirs_change_nodes",
         "scanners", "weather_alerts", "ukraine_alerts", "air_quality", "volcanoes",
         "fishing_activity", "psk_reporter", "correlations", "uap_sightings", "wastewater",
-        "crowdthreat", "threat_level", "trending_markets",
+        "crowdthreat", "threat_level", "trending_markets", "road_corridor_trends",
     )
     freshness = get_source_timestamps_snapshot()
     payload = {
@@ -799,6 +799,11 @@ async def live_data_slow(
         "uap_sightings": (d.get("uap_sightings") or []) if active_layers.get("uap_sightings", True) else [],
         "wastewater": (d.get("wastewater") or []) if active_layers.get("wastewater", True) else [],
         "crowdthreat": (d.get("crowdthreat") or []) if active_layers.get("crowdthreat", True) else [],
+        "road_corridor_trends": (
+            d.get("road_corridor_trends") or {"updated_at": None, "corridors": []}
+        )
+        if active_layers.get("road_corridor_trends", False)
+        else {"updated_at": None, "corridors": []},
         "freshness": freshness,
     }
     # Issue #288: bbox filter heavy/dense layers only when all four bounds

backend/routers/road_corridors.py

@@ -0,0 +1,105 @@
+"""Road corridor Sentinel-2 freight trend endpoints (opt-in slow layer)."""
+
+from fastapi import APIRouter, HTTPException, Query, Request
+from pydantic import BaseModel, Field
+
+from limiter import limiter
+from services.road_corridor_sat.config import optional_deps_available, road_corridor_sat_enabled
+from services.road_corridor_sat.credentials import sentinel_credentials_configured
+from services.road_corridor_sat.jobs import enqueue_analyze, get_job, get_latest_job, job_to_dict
+from services.road_corridor_sat.presets import CORRIDOR_PRESETS, get_preset
+from services.road_corridor_sat.storage import build_trends_payload, preset_metadata
+
+router = APIRouter()
+
+
+def _status_payload() -> dict:
+    latest = get_latest_job()
+    return {
+        "enabled": road_corridor_sat_enabled(),
+        "deps_installed": optional_deps_available(),
+        "credentials_configured": sentinel_credentials_configured(),
+        "preset_count": len(CORRIDOR_PRESETS),
+        "attribution": "backend/third_party/drishx/NOTICE.md",
+        "active_job": job_to_dict(latest) if latest and latest.status in {"queued", "running"} else None,
+    }
+
+
+def _require_analyze_ready() -> None:
+    if not optional_deps_available():
+        raise HTTPException(
+            status_code=503,
+            detail="Install optional road-corridor dependencies (uv sync --extra road-corridor)",
+        )
+    if not sentinel_credentials_configured():
+        raise HTTPException(
+            status_code=503,
+            detail="Set SENTINEL_CLIENT_ID and SENTINEL_CLIENT_SECRET in Imagery settings",
+        )
+
+
+class AnalyzeRequest(BaseModel):
+    lat: float = Field(ge=-90, le=90)
+    lon: float = Field(ge=-180, le=180)
+    label: str | None = Field(default=None, max_length=120)
+
+
+@router.get("/api/road-corridors/status")
+@limiter.limit("60/minute")
+async def road_corridors_status(request: Request) -> dict:
+    return {"ok": True, **_status_payload()}
+
+
+@router.get("/api/road-corridors")
+@limiter.limit("60/minute")
+async def list_road_corridors(request: Request) -> dict:
+    return {
+        "ok": True,
+        "status": _status_payload(),
+        "presets": CORRIDOR_PRESETS,
+        "trends": build_trends_payload(),
+    }
+
+
+@router.post("/api/road-corridors/analyze")
+@limiter.limit("6/minute")
+async def analyze_road_corridor_here(request: Request, payload: AnalyzeRequest) -> dict:
+    """Start an on-demand Sentinel-2 corridor analysis at map center."""
+    _require_analyze_ready()
+    try:
+        job = enqueue_analyze(payload.lat, payload.lon, payload.label)
+    except RuntimeError as exc:
+        if str(exc) == "analysis_already_running":
+            active = get_latest_job()
+            raise HTTPException(
+                status_code=409,
+                detail="Analysis already in progress",
+                headers={"X-Job-Id": active.job_id if active else ""},
+            ) from exc
+        raise
+    return {"ok": True, **job_to_dict(job)}
+
+
+@router.get("/api/road-corridors/analyze/status")
+@limiter.limit("120/minute")
+async def analyze_road_corridor_status(
+    request: Request,
+    job_id: str | None = Query(default=None),
+) -> dict:
+    job = get_job(job_id) if job_id else get_latest_job()
+    if job is None:
+        return {"ok": True, "job": None}
+    return {"ok": True, "job": job_to_dict(job)}
+
+
+@router.get("/api/road-corridors/{preset_id}")
+@limiter.limit("60/minute")
+async def get_road_corridor(preset_id: str, request: Request) -> dict:
+    meta = preset_metadata(preset_id)
+    if meta is None:
+        raise HTTPException(status_code=404, detail="Unknown corridor preset")
+    preset = get_preset(preset_id)
+    if preset is None:
+        # Ad-hoc viewport runs are stored on disk but not in CORRIDOR_PRESETS.
+        return {"ok": True, "preset": None, "result": meta, "status": _status_payload()}
+    return {"ok": True, "preset": preset, "result": meta, "status": _status_payload()}

backend/services/data_fetcher.py

@@ -76,6 +76,7 @@ from services.fetchers.infrastructure import (  # noqa: F401
     fetch_tinygs,
     fetch_psk_reporter,
 )
+from services.fetchers.road_corridor_sat import fetch_road_corridor_trends  # noqa: F401
 from services.fetchers.geo import (  # noqa: F401
     fetch_ships,
     fetch_airports,
@@ -1060,6 +1061,16 @@ def start_scheduler():
         misfire_grace_time=600,
     )
 
+    # Sentinel-2 road corridor freight trends — daily (opt-in, heavy CDSE usage)
+    _scheduler.add_job(
+        lambda: _run_task_with_health(fetch_road_corridor_trends, "fetch_road_corridor_trends"),
+        "interval",
+        hours=24,
+        id="road_corridor_trends",
+        max_instances=1,
+        misfire_grace_time=3600,
+    )
+
     # FIMI disinformation index — every 12 hours (weekly editorial feed)
     _scheduler.add_job(
         lambda: _run_task_with_health(fetch_fimi, "fetch_fimi"),

backend/services/fetchers/_store.py

@@ -69,6 +69,7 @@ class DashboardData(TypedDict, total=False):
     sar_scenes: List[Dict[str, Any]]
     sar_anomalies: List[Dict[str, Any]]
     sar_aoi_coverage: List[Dict[str, Any]]
+    road_corridor_trends: Dict[str, Any]
 
 
 # In-memory store
@@ -119,6 +120,7 @@ latest_data: DashboardData = {
     "sar_scenes": [],
     "sar_anomalies": [],
     "sar_aoi_coverage": [],
+    "road_corridor_trends": {"updated_at": None, "corridors": []},
 }
 
 # Per-source freshness timestamps
@@ -328,6 +330,7 @@ active_layers: dict[str, bool] = {
     "ai_intel": True,
     "crowdthreat": False,
     "sar": True,
+    "road_corridor_trends": False,
 }
 
 

backend/services/fetchers/road_corridor_sat.py

@@ -0,0 +1,84 @@
+"""Scheduled Sentinel-2 road corridor freight trend fetcher (opt-in, slow tier)."""
+from __future__ import annotations
+
+import logging
+import os
+from datetime import datetime, timezone
+
+from services.fetchers._store import _data_lock, _mark_fresh, is_any_active, latest_data
+
+logger = logging.getLogger(__name__)
+
+_REFRESH_HOURS = float(os.environ.get("ROAD_CORRIDOR_REFRESH_HOURS", "24"))
+
+
+def _hours_since(iso_ts: str) -> float | None:
+    try:
+        dt = datetime.fromisoformat(iso_ts.replace("Z", "+00:00"))
+        if dt.tzinfo is None:
+            dt = dt.replace(tzinfo=timezone.utc)
+        return (datetime.now(timezone.utc) - dt).total_seconds() / 3600.0
+    except ValueError:
+        return None
+
+
+def _feature_ready() -> bool:
+    from services.road_corridor_sat.config import optional_deps_available, road_corridor_sat_enabled
+    from services.road_corridor_sat.credentials import sentinel_credentials_configured
+
+    if not road_corridor_sat_enabled():
+        return False
+    if not optional_deps_available():
+        logger.debug("road_corridor_trends skipped — optional deps not installed")
+        return False
+    if not sentinel_credentials_configured():
+        logger.debug("road_corridor_trends skipped — Sentinel credentials missing")
+        return False
+    return True
+
+
+def refresh_road_corridor_store() -> None:
+    from services.road_corridor_sat.storage import build_trends_payload
+
+    payload = build_trends_payload()
+    with _data_lock:
+        latest_data["road_corridor_trends"] = payload
+    _mark_fresh("road_corridor_trends")
+
+
+def fetch_road_corridor_trends(force: bool = False) -> None:
+    """Refresh scheduled corridor presets (default: laredo_i35 every 24h)."""
+    if not is_any_active("road_corridor_trends"):
+        return
+    if not _feature_ready():
+        return
+
+    from services.road_corridor_sat.config import SCHEDULED_PRESET_IDS
+    from services.road_corridor_sat.pipeline import analyze_preset
+    from services.road_corridor_sat.presets import get_preset
+    from services.road_corridor_sat.storage import load_refresh_state
+
+    state = load_refresh_state()
+    for preset_id in SCHEDULED_PRESET_IDS:
+        preset = get_preset(preset_id)
+        if preset is None:
+            logger.warning("Unknown scheduled road corridor preset: %s", preset_id)
+            continue
+        last = state.get(preset_id)
+        if last and not force:
+            age_h = _hours_since(last)
+            if age_h is not None and age_h < _REFRESH_HOURS:
+                logger.info(
+                    "road_corridor %s fresh (%.1fh < %.1fh) — skipping",
+                    preset_id,
+                    age_h,
+                    _REFRESH_HOURS,
+                )
+                continue
+        try:
+            logger.info("road_corridor analysis starting for %s", preset_id)
+            analyze_preset(preset_id)
+        except Exception as exc:
+            logger.exception("road_corridor analysis failed for %s: %s", preset_id, exc)
+
+    refresh_road_corridor_store()

backend/services/road_corridor_sat/__init__.py

@@ -0,0 +1,5 @@
+"""Sentinel-2 road corridor freight trend analysis (DrishX engine port)."""
+
+from .config import optional_deps_available, road_corridor_sat_enabled
+
+__all__ = ["optional_deps_available", "road_corridor_sat_enabled"]

backend/services/road_corridor_sat/__main__.py

@@ -0,0 +1,4 @@
+from .cli import main
+
+if __name__ == "__main__":
+    raise SystemExit(main())

backend/services/road_corridor_sat/cli.py

@@ -0,0 +1,53 @@
+"""CLI for manual road corridor analysis runs."""
+from __future__ import annotations
+
+import argparse
+import logging
+import sys
+
+from .config import optional_deps_available, road_corridor_sat_enabled
+from .credentials import sentinel_credentials_configured
+from .pipeline import analyze_preset
+from .presets import CORRIDOR_PRESETS
+
+
+def main(argv: list[str] | None = None) -> int:
+    parser = argparse.ArgumentParser(description="Run Sentinel-2 road corridor truck trend analysis")
+    parser.add_argument("--preset", required=True, help="Preset id (e.g. laredo_i35)")
+    parser.add_argument("-v", "--verbose", action="store_true")
+    args = parser.parse_args(argv)
+
+    logging.basicConfig(level=logging.DEBUG if args.verbose else logging.INFO)
+
+    if not optional_deps_available():
+        print(
+            "Install optional deps: uv sync --extra road-corridor "
+            "(geopandas, osmnx, rasterio, sentinelhub, scikit-learn, imageio)",
+            file=sys.stderr,
+        )
+        return 2
+    if not road_corridor_sat_enabled() and not args.verbose:
+        print("Note: ROAD_CORRIDOR_SAT_ENABLED is off — CLI still runs for manual analysis.")
+    if not sentinel_credentials_configured():
+        print("Set SENTINEL_CLIENT_ID and SENTINEL_CLIENT_SECRET first.", file=sys.stderr)
+        return 2
+
+    valid = {p["id"] for p in CORRIDOR_PRESETS}
+    if args.preset not in valid:
+        print(f"Unknown preset {args.preset!r}. Choose from: {', '.join(sorted(valid))}", file=sys.stderr)
+        return 2
+
+    def progress(msg: str, pct: int | None = None) -> None:
+        suffix = f" ({pct}%)" if pct is not None else ""
+        print(f"{msg}{suffix}")
+
+    result = analyze_preset(args.preset, progress_cb=progress)
+    print(
+        f"Done: {result.get('total_detections', 0)} detections across "
+        f"{len(result.get('daily_counts') or [])} days — status={result.get('status')}"
+    )
+    return 0 if result.get("status") == "ok" else 1
+
+
+if __name__ == "__main__":
+    raise SystemExit(main())

backend/services/road_corridor_sat/config.py

@@ -0,0 +1,41 @@
+"""Configuration for Sentinel-2 road corridor trend analysis."""
+from __future__ import annotations
+
+import os
+from pathlib import Path
+
+_BACKEND_ROOT = Path(__file__).resolve().parents[2]
+DATA_ROOT = Path(os.environ.get("ROAD_CORRIDOR_DATA_DIR", str(_BACKEND_ROOT / "data" / "road_corridors")))
+CACHE_DIR = DATA_ROOT / "cache"
+DETECTION_CROP_DIR = DATA_ROOT / "detection_crops"
+STATE_PATH = DATA_ROOT / "_refresh_state.json"
+
+DEFAULT_MONTHS = int(os.environ.get("ROAD_CORRIDOR_MONTHS", "2"))
+DEFAULT_MAX_FRAMES = int(os.environ.get("ROAD_CORRIDOR_MAX_FRAMES", "6"))
+SCHEDULED_PRESET_IDS = [
+    s.strip()
+    for s in os.environ.get("ROAD_CORRIDOR_SCHEDULED_PRESETS", "laredo_i35").split(",")
+    if s.strip()
+]
+
+
+def road_corridor_sat_enabled() -> bool:
+    return os.environ.get("ROAD_CORRIDOR_SAT_ENABLED", "").strip().lower() in {
+        "1",
+        "true",
+        "yes",
+        "on",
+    }
+
+
+def optional_deps_available() -> bool:
+    try:
+        import geopandas  # noqa: F401
+        import osmnx  # noqa: F401
+        import rasterio  # noqa: F401
+        import sentinelhub  # noqa: F401
+        import sklearn  # noqa: F401
+
+        return True
+    except ImportError:
+        return False

backend/services/road_corridor_sat/credentials.py

@@ -0,0 +1,37 @@
+"""Reuse Shadowbroker Sentinel Hub / Copernicus CDSE credentials."""
+from __future__ import annotations
+
+import os
+
+from .config import CACHE_DIR
+
+
+def resolve_sentinel_credentials() -> tuple[str, str]:
+    client_id = (os.environ.get("SENTINEL_CLIENT_ID") or "").strip()
+    client_secret = (os.environ.get("SENTINEL_CLIENT_SECRET") or "").strip()
+    return client_id, client_secret
+
+
+def sentinel_credentials_configured() -> bool:
+    client_id, client_secret = resolve_sentinel_credentials()
+    return bool(client_id and client_secret)
+
+
+def build_sh_config():
+    from sentinelhub import SHConfig
+
+    client_id, client_secret = resolve_sentinel_credentials()
+    if not client_id or not client_secret:
+        raise RuntimeError(
+            "SENTINEL_CLIENT_ID and SENTINEL_CLIENT_SECRET are required for road corridor analysis"
+        )
+    config = SHConfig()
+    config.sh_client_id = client_id
+    config.sh_client_secret = client_secret
+    config.sh_base_url = "https://sh.dataspace.copernicus.eu"
+    config.sh_token_url = (
+        "https://identity.dataspace.copernicus.eu/auth/realms/CDSE/protocol/openid-connect/token"
+    )
+    CACHE_DIR.mkdir(parents=True, exist_ok=True)
+    config.cache_dir = str(CACHE_DIR / "sentinelhub")
+    return config

backend/services/road_corridor_sat/jobs.py

@@ -0,0 +1,149 @@
+"""In-memory job queue for on-demand Analyze Here runs."""
+from __future__ import annotations
+
+import logging
+import threading
+import uuid
+from dataclasses import dataclass
+from typing import Any
+
+logger = logging.getLogger(__name__)
+
+_lock = threading.Lock()
+_jobs: dict[str, AnalyzeJob] = {}
+
+
+@dataclass
+class AnalyzeJob:
+    job_id: str
+    lat: float
+    lon: float
+    status: str = "queued"
+    message: str = "Queued"
+    progress: int = 0
+    result: dict[str, Any] | None = None
+    error: str | None = None
+
+
+def get_job(job_id: str) -> AnalyzeJob | None:
+    with _lock:
+        return _jobs.get(job_id)
+
+
+def get_latest_job() -> AnalyzeJob | None:
+    with _lock:
+        if not _jobs:
+            return None
+        return max(_jobs.values(), key=lambda j: j.job_id)
+
+
+def _running_job() -> AnalyzeJob | None:
+    with _lock:
+        for job in _jobs.values():
+            if job.status in {"queued", "running"}:
+                return job
+    return None
+
+
+def _prune_jobs(max_keep: int = 8) -> None:
+    with _lock:
+        if len(_jobs) <= max_keep:
+            return
+        ordered = sorted(_jobs.items(), key=lambda item: item[0], reverse=True)
+        for job_id, _ in ordered[max_keep:]:
+            _jobs.pop(job_id, None)
+
+
+def _worker(job_id: str, lat: float, lon: float, label: str | None) -> None:
+    from services.fetchers.road_corridor_sat import refresh_road_corridor_store
+
+    from .pipeline import analyze_corridor
+    from .viewport import adhoc_preset_id, bbox_around_point, default_label_for_point
+
+    job = get_job(job_id)
+    if job is None:
+        return
+
+    def progress(msg: str, pct: int | None = None) -> None:
+        with _lock:
+            current = _jobs.get(job_id)
+            if current is None:
+                return
+            current.message = msg
+            if pct is not None:
+                current.progress = pct
+
+    with _lock:
+        job.status = "running"
+        job.message = "Starting road corridor analysis"
+        job.progress = 0
+
+    try:
+        bbox = bbox_around_point(lat, lon)
+        preset_id = adhoc_preset_id(lat, lon)
+        corridor_label = label or default_label_for_point(lat, lon)
+        result = analyze_corridor(
+            preset_id=preset_id,
+            label=corridor_label,
+            bbox=bbox,
+            country="adhoc",
+            category="viewport",
+            progress_cb=progress,
+        )
+        refresh_road_corridor_store()
+        with _lock:
+            current = _jobs.get(job_id)
+            if current is None:
+                return
+            current.status = "ok" if result.get("status") == "ok" else "error"
+            current.result = result
+            current.error = result.get("error")
+            current.message = (
+                f"{result.get('total_detections', 0)} signatures · "
+                f"{len(result.get('daily_counts') or [])} days"
+            )
+            current.progress = 100
+    except Exception as exc:
+        logger.exception("road corridor analyze job %s failed", job_id)
+        with _lock:
+            current = _jobs.get(job_id)
+            if current is None:
+                return
+            current.status = "error"
+            current.error = str(exc)
+            current.message = "Analysis failed"
+            current.progress = 100
+
+
+def enqueue_analyze(lat: float, lon: float, label: str | None = None) -> AnalyzeJob:
+    running = _running_job()
+    if running is not None:
+        raise RuntimeError("analysis_already_running")
+
+    job_id = uuid.uuid4().hex[:12]
+    job = AnalyzeJob(job_id=job_id, lat=lat, lon=lon)
+    with _lock:
+        _jobs[job_id] = job
+        _prune_jobs()
+
+    thread = threading.Thread(
+        target=_worker,
+        args=(job_id, lat, lon, label),
+        name=f"road-corridor-analyze-{job_id}",
+        daemon=True,
+    )
+    thread.start()
+    return job
+
+
+def job_to_dict(job: AnalyzeJob) -> dict[str, Any]:
+    return {
+        "job_id": job.job_id,
+        "lat": job.lat,
+        "lon": job.lon,
+        "status": job.status,
+        "message": job.message,
+        "progress": job.progress,
+        "result": job.result,
+        "error": job.error,
+    }

backend/services/road_corridor_sat/pipeline.py

@@ -0,0 +1,216 @@
+"""Run Sentinel-2 road-corridor truck trend analysis for a bbox preset."""
+from __future__ import annotations
+
+import logging
+from collections.abc import Callable
+from concurrent.futures import ThreadPoolExecutor, as_completed
+from datetime import datetime, timedelta
+from typing import Any
+
+from .config import CACHE_DIR, DEFAULT_MAX_FRAMES, DEFAULT_MONTHS, DETECTION_CROP_DIR
+from .storage import store_analysis_result
+
+logger = logging.getLogger(__name__)
+
+ProgressCb = Callable[[str, int | None], None]
+
+_EVALSCRIPT = """//VERSION=3
+function setup() {
+  return {
+    input: ["B02", "B03", "B04", "B08", "CLM"],
+    output: { id: "default", bands: 5, sampleType: "FLOAT32" }
+  };
+}
+function evaluatePixel(s) {
+  return [s.B04, s.B03, s.B02, s.B08, s.CLM];
+}"""
+
+
+def _noop_progress(_msg: str, _pct: int | None = None) -> None:
+    return None
+
+
+def analyze_corridor(
+    *,
+    preset_id: str,
+    label: str,
+    bbox: list[float],
+    country: str = "",
+    category: str = "",
+    months: int = DEFAULT_MONTHS,
+    max_frames: int = DEFAULT_MAX_FRAMES,
+    progress_cb: ProgressCb | None = None,
+) -> dict[str, Any]:
+    """Synchronously analyze one corridor bbox and persist daily truck-count trends."""
+    from rasterio import features as rio_features
+    from rasterio import transform as rio_transform
+    from sentinelhub import BBox, CRS, DataCollection, MimeType, SentinelHubCatalog, SentinelHubRequest
+
+    from .credentials import build_sh_config
+    from .s2_truck_detect import S2TruckEngine
+
+    progress = progress_cb or _noop_progress
+    min_lat, min_lon, max_lat, max_lon = bbox
+    if abs(max_lat - min_lat) > 0.5 or abs(max_lon - min_lon) > 0.5:
+        raise ValueError("AOI too large. Max strategic sector is ~55 km x 55 km.")
+
+    CACHE_DIR.mkdir(parents=True, exist_ok=True)
+    engine = S2TruckEngine(
+        cache_dir=str(CACHE_DIR),
+        detection_dir=str(DETECTION_CROP_DIR),
+    )
+    config = build_sh_config()
+
+    progress(f"Road discovery for {label}", 10)
+    roads = engine.fetch_roads(bbox)
+    if roads.empty:
+        return store_analysis_result(
+            preset_id,
+            label=label,
+            bbox=bbox,
+            country=country,
+            category=category,
+            road_count=0,
+            frame_count=0,
+            detections=[],
+            status="error",
+            error="No major roads found in AOI.",
+        )
+
+    progress(f"Found {len(roads)} road segments — querying Copernicus catalog", 25)
+    sh_bbox = BBox(bbox=[min_lon, min_lat, max_lon, max_lat], crs=CRS.WGS84)
+    catalog = SentinelHubCatalog(config=config)
+    end_date = datetime.utcnow()
+    start_date = end_date - timedelta(days=max(1, months) * 30)
+    cdse_collection = DataCollection.SENTINEL2_L2A.define_from(
+        "s2l2a",
+        service_url=config.sh_base_url,
+    )
+    search_results = list(
+        catalog.search(
+            cdse_collection,
+            bbox=sh_bbox,
+            datetime=(
+                f"{start_date.strftime('%Y-%m-%dT00:00:00Z')}/"
+                f"{end_date.strftime('%Y-%m-%dT23:59:59Z')}"
+            ),
+            filter="eo:cloud_cover < 60",
+            fields={"include": ["properties.datetime", "id"], "exclude": []},
+        )
+    )
+    unique_scenes: dict[str, Any] = {}
+    for res in search_results:
+        date_key = res["properties"]["datetime"][:10]
+        if date_key not in unique_scenes:
+            unique_scenes[date_key] = res
+    final_obs = [unique_scenes[d] for d in sorted(unique_scenes.keys(), reverse=True)]
+    final_obs = final_obs[: max(1, max_frames)]
+    if not final_obs:
+        return store_analysis_result(
+            preset_id,
+            label=label,
+            bbox=bbox,
+            country=country,
+            category=category,
+            road_count=len(roads),
+            frame_count=0,
+            detections=[],
+            status="error",
+            error=f"No clear imagery found in the last {months} months.",
+        )
+
+    def _fetch_frame(idx: int, res_obs: dict[str, Any]):
+        try:
+            date_str = res_obs["properties"]["datetime"]
+            req_sh = SentinelHubRequest(
+                evalscript=_EVALSCRIPT,
+                input_data=[
+                    SentinelHubRequest.input_data(
+                        data_collection=cdse_collection,
+                        time_interval=(date_str, date_str),
+                    )
+                ],
+                responses=[SentinelHubRequest.output_response("default", MimeType.TIFF)],
+                bbox=sh_bbox,
+                config=config,
+            )
+            data_list = req_sh.get_data()
+            if not data_list:
+                return idx, date_str, None
+            return idx, date_str, data_list[0]
+        except Exception as exc:
+            logger.error("Sentinel frame %s failed: %s", idx, exc)
+            return idx, None, None
+
+    progress(f"Seed frame 1/{len(final_obs)}", 35)
+    _, seed_ts, seed_data = _fetch_frame(0, final_obs[0])
+    if seed_data is None:
+        return store_analysis_result(
+            preset_id,
+            label=label,
+            bbox=bbox,
+            country=country,
+            category=category,
+            road_count=len(roads),
+            frame_count=0,
+            detections=[],
+            status="error",
+            error="Failed to acquire seed spectral data.",
+        )
+
+    roads_buf = roads.to_crs(epsg=3857).buffer(20).to_crs(epsg=4326)
+    h, w = seed_data.shape[:2]
+    trans = rio_transform.from_bounds(min_lon, min_lat, max_lon, max_lat, w, h)
+    road_mask = rio_features.rasterize(
+        [(geom.__geo_interface__, 1) for geom in roads_buf.geometry],
+        out_shape=(h, w),
+        transform=trans,
+        fill=0,
+        all_touched=True,
+    )
+
+    detections: list[dict[str, Any]] = []
+    detections.extend(engine.detect_trucks(seed_data, bbox, final_obs[0]["properties"]["datetime"], road_mask))
+
+    if len(final_obs) > 1:
+        progress(f"Parallel frames ({len(final_obs) - 1} remaining)", 45)
+        with ThreadPoolExecutor(max_workers=3, thread_name_prefix="road-corridor") as executor:
+            futures = {
+                executor.submit(_fetch_frame, i, final_obs[i]): i for i in range(1, len(final_obs))
+            }
+            done = 1
+            for future in as_completed(futures):
+                idx, date_str, frame_data = future.result()
+                done += 1
+                if frame_data is not None and date_str:
+                    detections.extend(engine.detect_trucks(frame_data, bbox, date_str, road_mask))
+                progress(f"Frame {done}/{len(final_obs)}", 45 + int((done / len(final_obs)) * 50))
+
+    progress(f"Complete — {len(detections)} truck signatures", 100)
+    return store_analysis_result(
+        preset_id,
+        label=label,
+        bbox=bbox,
+        country=country,
+        category=category,
+        road_count=len(roads),
+        frame_count=len(final_obs),
+        detections=detections,
+        status="ok",
+    )
+
+
+def analyze_preset(preset_id: str, progress_cb: ProgressCb | None = None) -> dict[str, Any]:
+    from .presets import get_preset
+
+    preset = get_preset(preset_id)
+    if preset is None:
+        raise KeyError(f"Unknown preset: {preset_id}")
+    return analyze_corridor(
+        preset_id=preset["id"],
+        label=preset["label"],
+        bbox=preset["bbox"],
+        country=preset["country"],
+        category=preset["category"],
+        progress_cb=progress_cb,
+    )

backend/services/road_corridor_sat/presets.py

@@ -0,0 +1,59 @@
+"""Preset freight / chokepoint corridors for scheduled trend analysis."""
+from __future__ import annotations
+
+from typing import TypedDict
+
+
+class CorridorPreset(TypedDict):
+    id: str
+    label: str
+    bbox: list[float]  # [min_lat, min_lon, max_lat, max_lon]
+    country: str
+    category: str
+
+
+# Bboxes are small (~5–10 km) highway segments suitable for 10 m Sentinel-2 analysis.
+CORRIDOR_PRESETS: list[CorridorPreset] = [
+    {
+        "id": "laredo_i35",
+        "label": "Laredo I-35 (US–Mexico freight)",
+        "bbox": [27.48, -99.58, 27.54, -99.48],
+        "country": "USA / Mexico",
+        "category": "border_crossing",
+    },
+    {
+        "id": "bandar_abbas_feeder",
+        "label": "Bandar Abbas port feeder (Highway 71)",
+        "bbox": [27.12, 56.22, 27.22, 56.38],
+        "country": "Iran",
+        "category": "port_feeder",
+    },
+    {
+        "id": "rotterdam_a15",
+        "label": "Rotterdam A15 port feeder",
+        "bbox": [51.88, 4.42, 51.96, 4.58],
+        "country": "Netherlands",
+        "category": "port_feeder",
+    },
+    {
+        "id": "mombasa_nairobi_a109",
+        "label": "Mombasa–Nairobi A109 corridor",
+        "bbox": [-4.10, 39.55, -1.20, 37.00],
+        "country": "Kenya",
+        "category": "trade_corridor",
+    },
+    {
+        "id": "braunschweig_a7",
+        "label": "Braunschweig A7 (validation)",
+        "bbox": [52.25, 10.45, 52.32, 10.55],
+        "country": "Germany",
+        "category": "validation",
+    },
+]
+
+
+def get_preset(preset_id: str) -> CorridorPreset | None:
+    for preset in CORRIDOR_PRESETS:
+        if preset["id"] == preset_id:
+            return preset
+    return None

backend/services/road_corridor_sat/s2_truck_detect.py

@@ -0,0 +1,731 @@
+"""S2 truck motion detection core (DrishX / Fisser et al. 2022 — see third_party/drishx/NOTICE.md)."""
+from __future__ import annotations
+
+import logging
+import os
+import pickle
+import time
+from pathlib import Path
+
+import imageio.v3 as imageio
+import numpy as np
+import requests
+from requests.adapters import HTTPAdapter
+from shapely.geometry import LineString
+from urllib3.util.retry import Retry
+
+logger = logging.getLogger(__name__)
+
+SECONDS_OFFSET_B02_B04 = 1.01
+
+OVERPASS_MIRRORS = [
+    "https://lz4.overpass-api.de/api/interpreter",
+    "https://z.overpass-api.de/api/interpreter",
+    "https://overpass.osm.ch/api/interpreter",
+    "https://overpass-api.de/api/interpreter",
+]
+
+_session = requests.Session()
+_retry = Retry(
+    total=2,
+    backoff_factor=1.0,
+    status_forcelist=[429, 500, 502, 503, 504],
+    allowed_methods=["GET", "POST"],
+)
+_adapter = HTTPAdapter(max_retries=_retry)
+_session.mount("http://", _adapter)
+_session.mount("https://", _adapter)
+
+
+def _configure_osmnx(data_dir: str) -> None:
+    import osmnx as ox
+
+    ox.settings.requests_session = _session
+    ox.settings.requests_timeout = 30
+    ox.settings.overpass_rate_limit = False
+    ox.settings.max_query_area_size = 1_000_000_000_000
+    ox.settings.log_console = False
+    ox.settings.use_cache = True
+    ox.settings.cache_folder = os.path.join(data_dir, "osm_cache")
+
+
+def _default_rf_model_path() -> str:
+    return str(Path(__file__).resolve().parents[2] / "data" / "drishx" / "rf_model.pickle")
+
+
+# ─────────────────────────────────────────────────────────────────────────────
+# Helper math — mirrors S2TD.array_utils.math
+# ─────────────────────────────────────────────────────────────────────────────
+
+def normalized_ratio(a, b):
+    """(a - b) / (a + b), safe division."""
+    denom = a + b
+    with np.errstate(divide="ignore", invalid="ignore"):
+        result = np.where(denom != 0, (a - b) / denom, 0.0)
+    return result.astype(np.float32)
+
+
+def rescale_s2(bands):
+    """Rescale Sentinel-2 L2A reflectance values (typically 0–10000 int) to 0–1 float."""
+    bands = bands.astype(np.float32)
+    if np.nanmax(bands) > 10:  # likely DN scale
+        bands /= 10000.0
+    return bands
+
+
+# ─────────────────────────────────────────────────────────────────────────────
+# Array subset — exact replica of S2TD.pick_arr_subset
+# ─────────────────────────────────────────────────────────────────────────────
+
+def pick_arr_subset(arr, y, x, size):
+    """Pick a size×size window centred on (y, x) from a 2D or 3D array."""
+    size_low = size // 2
+    size_up = size // 2
+    if size_low + size_up < size:
+        size_up += 1
+    ymin = max(0, y - size_low)
+    ymax = max(0, y + size_up)
+    xmin = max(0, x - size_low)
+    xmax = max(0, x + size_up)
+    if arr.ndim == 2:
+        return arr[ymin:ymax, xmin:xmax]
+    elif arr.ndim == 3:
+        return arr[:, ymin:ymax, xmin:xmax]
+    return arr
+
+
+# ─────────────────────────────────────────────────────────────────────────────
+# Feature stack — exact 7 features as in S2TD._build_feature_stack (Table 1)
+# ─────────────────────────────────────────────────────────────────────────────
+
+def build_feature_stack(data):
+    """
+    Build the 7-feature stack from Sentinel-2 bands.
+
+    Input `data` shape: (H, W, 5) with channels [B04(R), B03(G), B02(B), B08(NIR), CLM].
+
+    Feature order (Table 1, Fisser et al. 2022):
+        0: variance of (B04, B03, B02)
+        1: normalized_ratio(B04, B02)  — red / blue
+        2: normalized_ratio(B03, B02)  — green / blue
+        3: B04 - mean(B04)
+        4: B03 - mean(B03)
+        5: B02 - mean(B02)
+        6: B08 - mean(B08)
+    """
+    R = data[:, :, 0].astype(np.float32)    # B04
+    G = data[:, :, 1].astype(np.float32)    # B03
+    B = data[:, :, 2].astype(np.float32)    # B02
+    NIR = data[:, :, 3].astype(np.float32)  # B08
+    CLM = data[:, :, 4]
+
+    # Rescale if needed
+    bands = np.stack([R, G, B, NIR], axis=0)
+    bands = rescale_s2(bands)
+    R, G, B, NIR = bands[0], bands[1], bands[2], bands[3]
+
+    # Cloud mask → NaN
+    cloud = CLM > 0
+    R[cloud] = np.nan
+    G[cloud] = np.nan
+    B[cloud] = np.nan
+    NIR[cloud] = np.nan
+
+    H, W = R.shape
+    fs = np.zeros((7, H, W), dtype=np.float32)
+
+    # Check for any valid data to avoid "Mean of empty slice" warnings
+    if np.any(~cloud):
+        import warnings
+        with warnings.catch_warnings():
+            warnings.simplefilter("ignore", category=RuntimeWarning)
+            # Feature 0: variance of visible bands
+            fs[0] = np.nanvar(np.stack([R, G, B], axis=0), axis=0, ddof=0)
+
+            # Features 1–2: normalized ratios
+            fs[1] = normalized_ratio(R, B)
+            fs[2] = normalized_ratio(G, B)
+
+            # Features 3–6: mean-centered bands
+            fs[3] = R - np.nanmean(R)
+            fs[4] = G - np.nanmean(G)
+            fs[5] = B - np.nanmean(B)
+            fs[6] = NIR - np.nanmean(NIR)
+    else:
+        # All pixels are cloud-masked
+        fs.fill(np.nan)
+
+    # Ensure NaN consistency
+    nan_mask = np.isnan(fs[3])
+    fs[:, nan_mask] = np.nan
+
+    return {
+        "feature_stack": fs,
+        "bands": {"R": R, "G": G, "B": B, "NIR": NIR},
+        "cloud_mask": cloud,
+    }
+
+
+# ─────────────────────────────────────────────────────────────────────────────
+# RF Model loading
+# ─────────────────────────────────────────────────────────────────────────────
+
+# Path to the trained Random Forest model from S2TruckDetect
+RF_MODEL_PATH = _default_rf_model_path()
+_rf_model = None
+
+
+def load_rf_model(path=None):
+    """Load the trained RF model from pickle. Returns None if not found."""
+    global _rf_model
+    p = path or RF_MODEL_PATH
+    if _rf_model is not None:
+        return _rf_model
+    if os.path.isfile(p):
+        try:
+            _rf_model = pickle.load(open(p, "rb"))
+            logger.info(f"Loaded trained RF model from {p}")
+            return _rf_model
+        except Exception as e:
+            logger.error(f"Failed to load RF model from {p}: {e}")
+    else:
+        logger.warning(f"RF model not found at {p} — will use proxy classifier (lower accuracy)")
+    return None
+
+
+# ─────────────────────────────────────────────────────────────────────────────
+# Classification — real RF (preferred) or proxy fallback
+# ─────────────────────────────────────────────────────────────────────────────
+
+def rf_classify(feature_stack, road_mask, rf_model):
+    """
+    Classify pixels using the trained Random Forest model.
+    Exact replica of S2TD._predict + _postprocess_prediction.
+
+    :param feature_stack: (7, H, W) feature array
+    :param road_mask: (H, W) binary road mask
+    :param rf_model: trained sklearn RandomForestClassifier
+    :return: (probabilities (4, H, W), prediction (H, W) int8)
+    """
+    H, W = feature_stack.shape[1], feature_stack.shape[2]
+
+    # Reshape to (n_pixels, 7) for sklearn
+    vars_reshaped = []
+    for band_idx in range(feature_stack.shape[0]):
+        vars_reshaped.append(feature_stack[band_idx].flatten())
+    vars_reshaped = np.array(vars_reshaped).swapaxes(0, 1)  # (n_pixels, 7)
+
+    # Build NaN mask — exclude NaN and Inf pixels
+    nan_mask_flat = np.zeros_like(vars_reshaped)
+    for var_idx in range(vars_reshaped.shape[1]):
+        nan_mask_flat[:, var_idx] = ~np.isnan(vars_reshaped[:, var_idx])
+    not_nan = (np.nanmin(nan_mask_flat, axis=1).astype(bool)
+               & np.min(np.isfinite(vars_reshaped), axis=1).astype(bool))
+
+    # Run RF predict_proba on valid pixels only
+    if not np.any(not_nan):
+        # Graceful return if no valid pixels found (e.g., all cloud masked)
+        probabilities_shaped = np.zeros((4, H, W), dtype=np.float32)
+        classification = np.zeros((H, W), dtype=np.int8)
+        return probabilities_shaped, classification
+
+    predictions_flat = rf_model.predict_proba(vars_reshaped[not_nan])
+
+    # Map probabilities back to spatial grid
+    n_classes = predictions_flat.shape[1] 
+    probabilities_shaped = np.zeros((n_classes, H * W), dtype=np.float32)
+    for idx in range(n_classes):
+        probabilities_shaped[idx, not_nan] = predictions_flat[:, idx]
+
+    probabilities_shaped = probabilities_shaped.reshape((n_classes, H, W))
+
+    # Zero out NaN positions
+    nan_2d = np.isnan(feature_stack[0])
+    probabilities_shaped[:, nan_2d] = 0
+
+    # Post-process: suppress low-confidence background (exact S2TD logic)
+    probabilities_shaped[1][probabilities_shaped[1] < 0.75] = 0
+
+    classification = np.nanargmax(probabilities_shaped, axis=0).astype(np.int8) + 1
+    classification[np.max(probabilities_shaped, axis=0) == 0] = 0
+    classification[nan_2d] = 0
+
+    # Apply road mask
+    rm = road_mask.astype(bool)
+    classification[~rm] = 0
+
+    return probabilities_shaped, classification
+
+
+def proxy_classify(feature_stack, road_mask):
+    """
+    Heuristic proxy when RF model is unavailable. Lower accuracy.
+
+    Produces:
+        probabilities: (4, H, W) — class probs for [background, blue, green, red]
+        prediction:    (H, W)    — int8 labels {0=nan, 1=background, 2=blue, 3=green, 4=red}
+    """
+    fs = feature_stack  # (7, H, W)
+    H, W = fs.shape[1], fs.shape[2]
+    probs = np.zeros((4, H, W), dtype=np.float32)
+
+    centered_R = fs[3]
+    centered_G = fs[4]
+    centered_B = fs[5]
+    var_feat = fs[0]
+    nratio_rb = fs[1]
+    nratio_gb = fs[2]
+
+    rm = road_mask.astype(bool)
+    nan_mask = np.isnan(centered_R)
+
+    blue_score = np.clip(-nratio_rb * 2 + centered_B * 5 + var_feat * 10, 0, None)
+    blue_score[~rm | nan_mask] = 0
+
+    green_score = np.clip(nratio_gb * 2 + centered_G * 5 + var_feat * 10, 0, None)
+    green_score[~rm | nan_mask] = 0
+
+    red_score = np.clip(nratio_rb * 2 + centered_R * 5 + var_feat * 10, 0, None)
+    red_score[~rm | nan_mask] = 0
+
+    total = blue_score + green_score + red_score + 1e-8
+    probs[1] = blue_score / total
+    probs[2] = green_score / total
+    probs[3] = red_score / total
+    probs[0] = 1.0 - np.max(probs[1:], axis=0)
+
+    probs[0][probs[0] < 0.75] = 0
+
+    classification = np.nanargmax(probs, axis=0).astype(np.int8) + 1
+    classification[np.max(probs, axis=0) == 0] = 0
+    classification[nan_mask] = 0
+    classification[~rm] = 0
+
+    return probs, classification
+
+
+def classify(feature_stack, road_mask, rf_model=None):
+    """
+    Unified classifier entry point.
+    Uses trained RF if model is provided, otherwise falls back to proxy.
+    """
+    if rf_model is not None:
+        logger.debug("Using trained RF model for classification")
+        return rf_classify(feature_stack, road_mask, rf_model)
+    else:
+        logger.debug("Using proxy classifier (no RF model loaded)")
+        return proxy_classify(feature_stack, road_mask)
+
+
+# ─────────────────────────────────────────────────────────────────────────────
+# Object extraction — faithful port of S2TD ObjectExtractor
+# ─────────────────────────────────────────────────────────────────────────────
+
+class ObjectExtractor:
+    """
+    Extracts truck objects from the RF prediction raster using recursive
+    neighbourhood clustering, matching the S2TD reference implementation.
+    """
+
+    def __init__(self, probabilities, lat_arr, lon_arr):
+        """
+        :param probabilities: (4, H, W) class probabilities
+        :param lat_arr: 1-D array of latitude per row
+        :param lon_arr: 1-D array of longitude per column
+        """
+        self.probabilities = probabilities
+        self.lat = lat_arr
+        self.lon = lon_arr
+
+    def extract(self, predictions_arr):
+        """Main extraction loop over all blue (class 2) seed pixels."""
+        preds = predictions_arr.copy()
+        probs = self.probabilities.copy()
+
+        preds[preds == 1] = 0  # zero out background
+        blue_ys, blue_xs = np.where(preds == 2)
+        detections = []
+        sub_size = 9
+
+        for i in range(len(blue_ys)):
+            y_blue, x_blue = int(blue_ys[i]), int(blue_xs[i])
+            if preds[y_blue, x_blue] == 0:
+                continue
+
+            subset_9 = pick_arr_subset(preds, y_blue, x_blue, sub_size).copy()
+            subset_3 = pick_arr_subset(preds, y_blue, x_blue, 3).copy()
+            subset_9_probs = pick_arr_subset(probs, y_blue, x_blue, sub_size).copy()
+
+            half_idx_y = y_blue if subset_9.shape[0] < sub_size else subset_9.shape[0] // 2
+            half_idx_x = x_blue if subset_9.shape[1] < sub_size else subset_9.shape[1] // 2
+            try:
+                current_value = subset_9[half_idx_y, half_idx_x]
+            except IndexError:
+                half_idx_y, half_idx_x = sub_size // 2, sub_size // 2
+                current_value = subset_9[half_idx_y, half_idx_x]
+
+            new_value = 100
+            if not all(v in subset_9 for v in [2, 3, 4]):
+                continue
+
+            cluster, seen_idx, seen_vals, _ = self._cluster_array(
+                arr=subset_9, probs=subset_9_probs,
+                point=[half_idx_y, half_idx_x],
+                new_value=new_value, current_value=current_value,
+                yet_seen_indices=[], yet_seen_values=[],
+                skipped_one=False,
+            )
+
+            if np.count_nonzero(cluster == new_value) < 3:
+                continue
+
+            det = self._postprocess_cluster(
+                cluster, preds, probs, subset_3,
+                y_blue, x_blue,
+                half_idx_y, half_idx_x,
+                new_value,
+            )
+            if det is not None:
+                preds = det["updated_preds"]
+                detections.append(det["detection"])
+
+        return detections
+
+    def _cluster_array(self, arr, probs, point, new_value, current_value,
+                       yet_seen_indices, yet_seen_values, skipped_one):
+        """Recursive neighbourhood clustering — matches S2TD._cluster_array."""
+        if len(yet_seen_indices) == 0:
+            yet_seen_indices.append(point)
+            yet_seen_values.append(current_value)
+
+        arr_mod = arr.copy()
+        arr_mod[point[0], point[1]] = 0
+
+        window_3x3 = pick_arr_subset(arr_mod, point[0], point[1], 3).copy()
+        if window_3x3.shape[0] >= 2 and window_3x3.shape[1] >= 2:
+            cy = min(1, window_3x3.shape[0] - 1)
+            cx = min(1, window_3x3.shape[1] - 1)
+            if window_3x3[cy, cx] == 2:
+                window_3x3[window_3x3 == 4] = 1  # eliminate reds near blue
+
+        y, x = point[0], point[1]
+        window_3x3_probs = pick_arr_subset(probs, y, x, 3)
+
+        windows = [window_3x3]
+        windows_probs = [window_3x3_probs]
+        if current_value == 4 or skipped_one:
+            windows = windows[0:1]
+
+        ys, xs = np.array([], dtype=int), np.array([], dtype=int)
+        window_idx = 0
+        offset_y, offset_x = 0, 0
+
+        while len(ys) == 0 and window_idx < len(windows):
+            window = windows[window_idx]
+            window_p = windows_probs[window_idx]
+            offset_y = window.shape[0] // 2
+            offset_x = window.shape[1] // 2
+
+            go_next = (current_value + 1) in window or current_value == 2
+            target_value = current_value + 1 if go_next else current_value
+            match = window == target_value
+            if np.count_nonzero(match) == 0:
+                target_value = current_value
+                match = window == target_value
+
+            ys_found, xs_found = np.where(match)
+
+            # Probability-based tie-breaking
+            if len(ys_found) > 1 and window_p.ndim == 3 and window_p.shape[0] > (target_value - 1):
+                wp_target = window_p[target_value - 1] * match
+                max_prob_mask = (wp_target == np.max(wp_target))
+                ys_found, xs_found = np.where(max_prob_mask)
+
+            ys, xs = ys_found, xs_found
+            window_idx += 1
+
+        ymin_w = max(0, point[0] - offset_y)
+        xmin_w = max(0, point[1] - offset_x)
+
+        for y_local, x_local in zip(ys, xs):
+            ny, nx = ymin_w + int(y_local), xmin_w + int(x_local)
+            if [ny, nx] in yet_seen_indices:
+                continue
+            if ny < 0 or ny >= arr.shape[0] or nx < 0 or nx >= arr.shape[1]:
+                continue
+            try:
+                cv = arr[ny, nx]
+            except IndexError:
+                continue
+
+            # Red already seen but this is green or blue → skip
+            if 4 in yet_seen_values and cv <= 3:
+                continue
+
+            arr_mod[ny, nx] = new_value
+            yet_seen_indices.append([ny, nx])
+            yet_seen_values.append(cv)
+
+            # Guard: avoid picking many more reds than blues and greens
+            n_blue = sum(1 for v in yet_seen_values if v == 2)
+            n_green = sum(1 for v in yet_seen_values if v == 3)
+            n_red = sum(1 for v in yet_seen_values if v == 4)
+            if n_red > n_blue and n_red > n_green:
+                break
+
+            arr_mod, yet_seen_indices, yet_seen_values, skipped_one = self._cluster_array(
+                arr_mod, probs, [ny, nx], new_value, cv,
+                yet_seen_indices, yet_seen_values, skipped_one,
+            )
+
+        arr_mod[point[0], point[1]] = new_value
+        return arr_mod, yet_seen_indices, yet_seen_values, skipped_one
+
+    def _postprocess_cluster(self, cluster, preds_copy, probs, subset_3,
+                             y_blue, x_blue, half_idx_y, half_idx_x,
+                             new_value):
+        """Validate cluster and produce a detection dict — mirrors S2TD._postprocess_cluster."""
+        # Add neighbouring blues from the 3×3 window
+        ys_ba, xs_ba = np.where(subset_3 == 2)
+        ys_ba = ys_ba + half_idx_y - 1
+        xs_ba = xs_ba + half_idx_x - 1
+        for yb, xb in zip(ys_ba, xs_ba):
+            yb_c = int(np.clip(yb, 0, cluster.shape[0] - 1))
+            xb_c = int(np.clip(xb, 0, cluster.shape[1] - 1))
+            cluster[yb_c, xb_c] = new_value
+
+        cluster[cluster != new_value] = 0
+        cys, cxs = np.where(cluster == new_value)
+        if len(cys) == 0:
+            return None
+
+        # Map subset coords back to full array
+        ymin_sub = int(np.clip(y_blue - half_idx_y, 0, np.inf))
+        xmin_sub = int(np.clip(x_blue - half_idx_x, 0, np.inf))
+        cys_full = cys + ymin_sub
+        cxs_full = cxs + xmin_sub
+
+        ymin = int(np.min(cys_full))
+        xmin = int(np.min(cxs_full))
+        ymax = int(np.max(cys_full)) + 1  # +1: box extends to upper bound of pixel
+        xmax = int(np.max(cxs_full)) + 1
+
+        H, W = preds_copy.shape
+        ymin, ymax = max(0, ymin), min(H, ymax)
+        xmin, xmax = max(0, xmin), min(W, xmax)
+
+        box_preds = preds_copy[ymin:ymax, xmin:xmax].copy()
+        box_probs = probs[1:, ymin:ymax, xmin:xmax].copy()  # classes 2,3,4 → indices 0,1,2
+
+        # Spectral probability scores (exact S2TD logic)
+        max_probs = []
+        for cls_offset, cls_val in enumerate([2, 3, 4]):
+            mask = (box_preds == cls_val)
+            vals = box_probs[cls_offset] * mask
+            mp = float(np.nanmax(vals)) if np.any(mask) else 0.0
+            max_probs.append(mp)
+
+        mean_max_spectral_probability = float(np.nanmean(max_probs))
+        mean_spectral_probability = float(np.nanmean(np.nanmax(box_probs, axis=0)))
+
+        # Validation checks
+        all_given = all(v in box_preds for v in [2, 3, 4])
+        large_enough = box_preds.shape[0] > 2 or box_preds.shape[1] > 2
+        too_large = box_preds.shape[0] > 5 or box_preds.shape[1] > 5
+
+        if too_large or not all_given or not large_enough:
+            return None
+
+        # Score: TWO terms — matches reference
+        score = mean_max_spectral_probability + mean_spectral_probability
+        if score <= 1.2:
+            return None
+
+        # Direction (blue → red vector)
+        by, bx = np.where(box_preds == 2)
+        ry, rx = np.where(box_preds == 4)
+        blue_idx = np.array([by[0], bx[0]], dtype=np.int8)
+        red_idx = np.array([ry[0], rx[0]], dtype=np.int8)
+        vector = (blue_idx - red_idx) * np.array([1, -1], dtype=np.int8)
+        heading = float(np.degrees(np.arctan2(vector[1], vector[0])) % 360)
+
+        # Speed
+        diameter = max(box_preds.shape) * 10 - 10
+        speed_kmh = float(np.sqrt(diameter * 20) / SECONDS_OFFSET_B02_B04 * 3.6)
+
+        # Geo-coordinates (centre of detection box)
+        lat_centre = float((self.lat[ymin] + self.lat[min(ymax, len(self.lat) - 1)]) / 2)
+        lon_centre = float((self.lon[xmin] + self.lon[min(xmax, len(self.lon) - 1)]) / 2)
+
+        # Zero out detected pixels to prevent re-detection
+        preds_copy[ymin:ymax, xmin:xmax] *= np.zeros_like(box_preds)
+        # Also zero 3×3 around blue pixels
+        blue_in_box = np.where(box_preds == 2)
+        for yb, xb in zip(blue_in_box[0], blue_in_box[1]):
+            y0, y1 = max(0, ymin + yb - 1), min(H, ymin + yb + 2)
+            x0, x1 = max(0, xmin + xb - 1), min(W, xmin + xb + 2)
+            preds_copy[y0:y1, x0:x1] *= (preds_copy[y0:y1, x0:x1] != 2).astype(np.int8)
+
+        crop_id = f"truck_{int(time.time() * 1000)}_{ymin}_{xmin}.png"
+
+        return {
+            "updated_preds": preds_copy,
+            "detection": {
+                "lat": lat_centre,
+                "lon": lon_centre,
+                "confidence": float(min(score / 2.4, 1.0)),
+                "s_score": round(score, 3),
+                "speed_kmh": round(speed_kmh, 1),
+                "heading": round(heading, 1),
+                "heading_desc": self._direction_to_compass(heading),
+                "id": crop_id,
+                "image_url": f"/detections/{crop_id}",
+                "box_shape": list(box_preds.shape),
+                "max_probs": {"blue": max_probs[0], "green": max_probs[1], "red": max_probs[2]},
+            },
+        }
+
+    @staticmethod
+    def _direction_to_compass(deg):
+        bins = np.arange(0, 359, 45, dtype=np.float32)
+        labels = ["N", "NE", "E", "SE", "S", "SW", "W", "NW"]
+        return labels[int(np.argmin(np.abs(bins - deg)))]
+
+
+# ─────────────────────────────────────────────────────────────────────────────
+# ARGUS Engine
+# ─────────────────────────────────────────────────────────────────────────────
+
+class S2TruckEngine:
+    def __init__(self, *, cache_dir: str, detection_dir: str, rf_model_path: str | None = None):
+        self.cache_dir = cache_dir
+        self.detection_dir = detection_dir
+        os.makedirs(self.detection_dir, exist_ok=True)
+        _configure_osmnx(cache_dir)
+        self.rf_model = load_rf_model(rf_model_path)
+
+    def fetch_roads(self, bbox_coords, progress_cb=None):
+        """Fetch major roads with automatic mirror rotation and fallbacks."""
+        import geopandas as gpd
+        import osmnx as ox
+
+        def log(msg, level="info", pct=None):
+            if level == "info":
+                logger.info(msg)
+            elif level == "warn":
+                logger.warning(msg)
+            if progress_cb:
+                progress_cb(msg, pct)
+
+        min_lat, min_lon, max_lat, max_lon = bbox_coords
+        center_lat = (min_lat + max_lat) / 2
+        center_lon = (min_lon + max_lon) / 2
+
+        lat_span = (max_lat - min_lat) * 111000
+        lon_span = (max_lon - min_lon) * 111000 * np.cos(np.radians(center_lat))
+        dist_m = int(max(lat_span, lon_span) * 0.6) + 1000
+
+        log(f"Starting road discovery (ROI: {center_lat:.4f}, {center_lon:.4f})", pct=5)
+
+        for i, mirror in enumerate(OVERPASS_MIRRORS):
+            log(f"Trying mirror {i+1}/{len(OVERPASS_MIRRORS)}: {mirror}", pct=10 + i * 5)
+            ox.settings.overpass_url = mirror
+            try:
+                graph = ox.graph_from_point(
+                    (center_lat, center_lon), dist=dist_m,
+                    network_type="drive", simplify=True,
+                    retain_all=False, truncate_by_edge=True,
+                )
+                roads = ox.graph_to_gdfs(graph, nodes=False)
+                major_types = [
+                    "motorway", "trunk", "primary", "secondary",
+                    "motorway_link", "trunk_link", "primary_link",
+                ]
+                roads = roads[roads["highway"].isin(major_types)].copy()
+                if not roads.empty:
+                    logger.info(f"Fetched {len(roads)} major roads from {mirror}")
+                    return roads
+            except Exception as e:
+                logger.warning(f"Mirror {mirror} failed: {e}")
+                time.sleep(1)
+
+        # Raw Overpass fallback
+        logger.warning("All mirrors failed. Trying raw Overpass query.")
+        try:
+            query = f"""
+            [out:json][timeout:60];
+            (way["highway"~"motorway|trunk|primary"]({min_lat},{min_lon},{max_lat},{max_lon}););
+            out body; >; out skel qt;
+            """
+            resp = requests.post(OVERPASS_MIRRORS[0], data={"data": query}, timeout=60)
+            if resp.status_code == 200:
+                data = resp.json()
+                nodes = {n["id"]: (n["lon"], n["lat"]) for n in data["elements"] if n["type"] == "node"}
+                ways = []
+                for w in data["elements"]:
+                    if w["type"] == "way" and "nodes" in w:
+                        coords = [nodes[nid] for nid in w["nodes"] if nid in nodes]
+                        if len(coords) > 1:
+                            ways.append({"geometry": LineString(coords), "highway": w["tags"].get("highway")})
+                if ways:
+                    roads = gpd.GeoDataFrame(ways, crs="EPSG:4326")
+                    logger.info(f"Raw fallback: {len(roads)} roads")
+                    return roads
+        except Exception as e:
+            logger.error(f"Raw fallback failed: {e}")
+
+        return gpd.GeoDataFrame()
+
+    def detect_trucks(self, data, bbox_coords, timestamp, road_mask):
+        """
+        Detect trucks using corrected Fisser et al. methodology.
+
+        :param data: (H, W, 5) array — [B04, B03, B02, B08, CLM]
+        :param bbox_coords: [min_lat, min_lon, max_lat, max_lon]
+        :param timestamp: str ISO timestamp
+        :param road_mask: (H, W) binary mask of road pixels
+        :return: list of detection dicts
+        """
+        min_lat, min_lon, max_lat, max_lon = bbox_coords
+        H, W = data.shape[:2]
+
+        # 1. Build feature stack (corrected order)
+        feat = build_feature_stack(data)
+        feature_stack = feat["feature_stack"]
+
+        # 2. Classify (real RF if loaded, proxy fallback otherwise)
+        probs, prediction = classify(feature_stack, road_mask, self.rf_model)
+
+        # 3. Lat/lon arrays for geo-referencing
+        lat_arr = np.linspace(max_lat, min_lat, H)  # top to bottom
+        lon_arr = np.linspace(min_lon, max_lon, W)  # left to right
+
+        # 4. Object extraction (corrected)
+        extractor = ObjectExtractor(probs, lat_arr, lon_arr)
+        detections = extractor.extract(prediction)
+
+        # 5. Add timestamp and save crops
+        for det in detections:
+            det["timestamp"] = timestamp
+            try:
+                self._save_crop(data, det, H, W, min_lat, min_lon, max_lat, max_lon)
+            except Exception as e:
+                logger.warning(f"Could not save crop for {det['id']}: {e}")
+
+        return detections
+
+    def _save_crop(self, data, det, H, W, min_lat, min_lon, max_lat, max_lon):
+        """Save a 20×20 RGB crop centred on the detection."""
+        cy = int((max_lat - det["lat"]) / (max_lat - min_lat + 1e-9) * H)
+        cx = int((det["lon"] - min_lon) / (max_lon - min_lon + 1e-9) * W)
+        cy, cx = int(np.clip(cy, 0, H - 1)), int(np.clip(cx, 0, W - 1))
+
+        y0, y1 = max(0, cy - 10), min(H, cy + 10)
+        x0, x1 = max(0, cx - 10), min(W, cx + 10)
+
+        rgb = data[y0:y1, x0:x1, :3].astype(np.float32)
+        rgb = rescale_s2(rgb)
+        rgb = (np.clip(rgb, 0, 0.3) / 0.3 * 255).astype(np.uint8)
+
+        path = os.path.join(self.detection_dir, det["id"])
+        imageio.imwrite(path, rgb)

backend/services/road_corridor_sat/storage.py

@@ -0,0 +1,145 @@
+"""Disk persistence for road corridor trend runs."""
+from __future__ import annotations
+
+import json
+import logging
+from datetime import datetime, timezone
+from pathlib import Path
+from typing import Any
+
+from .config import DATA_ROOT, STATE_PATH
+from .presets import CORRIDOR_PRESETS, get_preset
+
+logger = logging.getLogger(__name__)
+
+
+def _utc_now_iso() -> str:
+    return datetime.now(timezone.utc).replace(microsecond=0).isoformat()
+
+
+def preset_result_path(preset_id: str) -> Path:
+    return DATA_ROOT / f"{preset_id}.json"
+
+
+def load_preset_result(preset_id: str) -> dict[str, Any] | None:
+    path = preset_result_path(preset_id)
+    if not path.is_file():
+        return None
+    try:
+        return json.loads(path.read_text(encoding="utf-8"))
+    except (OSError, json.JSONDecodeError) as exc:
+        logger.warning("Could not read road corridor result %s: %s", path, exc)
+        return None
+
+
+def save_preset_result(preset_id: str, payload: dict[str, Any]) -> None:
+    DATA_ROOT.mkdir(parents=True, exist_ok=True)
+    path = preset_result_path(preset_id)
+    path.write_text(json.dumps(payload, indent=2), encoding="utf-8")
+
+
+def load_refresh_state() -> dict[str, str]:
+    if not STATE_PATH.is_file():
+        return {}
+    try:
+        raw = json.loads(STATE_PATH.read_text(encoding="utf-8"))
+        return {str(k): str(v) for k, v in raw.items()}
+    except (OSError, json.JSONDecodeError):
+        return {}
+
+
+def save_refresh_state(state: dict[str, str]) -> None:
+    DATA_ROOT.mkdir(parents=True, exist_ok=True)
+    STATE_PATH.write_text(json.dumps(state, indent=2), encoding="utf-8")
+
+
+def mark_preset_refreshed(preset_id: str) -> None:
+    state = load_refresh_state()
+    state[preset_id] = _utc_now_iso()
+    save_refresh_state(state)
+
+
+def list_corridor_summaries() -> list[dict[str, Any]]:
+    summaries: list[dict[str, Any]] = []
+    for preset in CORRIDOR_PRESETS:
+        stored = load_preset_result(preset["id"])
+        if stored:
+            summaries.append(stored)
+            continue
+        summaries.append(
+            {
+                "preset_id": preset["id"],
+                "label": preset["label"],
+                "bbox": preset["bbox"],
+                "country": preset["country"],
+                "category": preset["category"],
+                "status": "never_run",
+                "daily_counts": [],
+                "total_detections": 0,
+            }
+        )
+    return summaries
+
+
+def build_trends_payload() -> dict[str, Any]:
+    return {
+        "updated_at": _utc_now_iso(),
+        "corridors": list_corridor_summaries(),
+    }
+
+
+def store_analysis_result(
+    preset_id: str,
+    *,
+    label: str,
+    bbox: list[float],
+    country: str,
+    category: str,
+    road_count: int,
+    frame_count: int,
+    detections: list[dict[str, Any]],
+    status: str = "ok",
+    error: str | None = None,
+) -> dict[str, Any]:
+    daily: dict[str, int] = {}
+    for det in detections:
+        ts = str(det.get("timestamp", ""))[:10]
+        if ts:
+            daily[ts] = daily.get(ts, 0) + 1
+    daily_counts = [{"date": d, "count": daily[d]} for d in sorted(daily.keys())]
+    payload = {
+        "preset_id": preset_id,
+        "label": label,
+        "bbox": bbox,
+        "country": country,
+        "category": category,
+        "updated_at": _utc_now_iso(),
+        "road_count": road_count,
+        "frame_count": frame_count,
+        "total_detections": len(detections),
+        "daily_counts": daily_counts,
+        "status": status,
+        "error": error,
+    }
+    save_preset_result(preset_id, payload)
+    mark_preset_refreshed(preset_id)
+    return payload
+
+
+def preset_metadata(preset_id: str) -> dict[str, Any] | None:
+    preset = get_preset(preset_id)
+    if preset is None:
+        return None
+    stored = load_preset_result(preset_id)
+    if stored:
+        return stored
+    return {
+        "preset_id": preset["id"],
+        "label": preset["label"],
+        "bbox": preset["bbox"],
+        "country": preset["country"],
+        "category": preset["category"],
+        "status": "never_run",
+        "daily_counts": [],
+        "total_detections": 0,
+    }

backend/services/road_corridor_sat/viewport.py

@@ -0,0 +1,19 @@
+"""Map-viewport helpers for on-demand corridor analysis."""
+from __future__ import annotations
+
+import hashlib
+
+
+def bbox_around_point(lat: float, lon: float, *, half_span_deg: float = 0.04) -> list[float]:
+    """Square AOI around a map center (~4–5 km half-span, under the 0.5° engine cap)."""
+    span = min(max(half_span_deg, 0.02), 0.24)
+    return [lat - span, lon - span, lat + span, lon + span]
+
+
+def adhoc_preset_id(lat: float, lon: float) -> str:
+    digest = hashlib.sha256(f"{lat:.4f},{lon:.4f}".encode()).hexdigest()[:12]
+    return f"adhoc_{digest}"
+
+
+def default_label_for_point(lat: float, lon: float) -> str:
+    return f"Map center ({lat:.4f}, {lon:.4f})"

backend/tests/test_road_corridor_sat.py

@@ -0,0 +1,147 @@
+"""Tests for opt-in Sentinel-2 road corridor trend layer."""
+from __future__ import annotations
+
+import json
+from unittest.mock import patch
+
+import pytest
+from fastapi.testclient import TestClient
+
+from services.fetchers._store import active_layers, latest_data
+from services.fetchers.road_corridor_sat import fetch_road_corridor_trends
+from services.road_corridor_sat.presets import get_preset
+
+
+class TestRoadCorridorGates:
+    def test_fetch_skips_when_layer_disabled(self, monkeypatch):
+        monkeypatch.setenv("ROAD_CORRIDOR_SAT_ENABLED", "true")
+        active_layers["road_corridor_trends"] = False
+        with patch("services.road_corridor_sat.pipeline.analyze_preset") as analyze:
+            fetch_road_corridor_trends(force=True)
+            analyze.assert_not_called()
+
+    def test_fetch_skips_when_feature_disabled(self, monkeypatch):
+        active_layers["road_corridor_trends"] = True
+        monkeypatch.delenv("ROAD_CORRIDOR_SAT_ENABLED", raising=False)
+        with patch("services.road_corridor_sat.pipeline.analyze_preset") as analyze:
+            fetch_road_corridor_trends(force=True)
+            analyze.assert_not_called()
+
+    def test_fetch_runs_when_enabled(self, monkeypatch, tmp_path):
+        monkeypatch.setenv("ROAD_CORRIDOR_SAT_ENABLED", "true")
+        monkeypatch.setenv("SENTINEL_CLIENT_ID", "test-id")
+        monkeypatch.setenv("SENTINEL_CLIENT_SECRET", "test-secret")
+        monkeypatch.setenv("ROAD_CORRIDOR_DATA_DIR", str(tmp_path))
+        active_layers["road_corridor_trends"] = True
+
+        fake_result = {
+            "preset_id": "laredo_i35",
+            "label": "Laredo I-35",
+            "status": "ok",
+            "daily_counts": [{"date": "2026-05-01", "count": 3}],
+            "total_detections": 3,
+        }
+        with patch("services.road_corridor_sat.config.optional_deps_available", return_value=True):
+            with patch(
+                "services.road_corridor_sat.pipeline.analyze_preset",
+                return_value=fake_result,
+            ) as analyze:
+                fetch_road_corridor_trends(force=True)
+                analyze.assert_called_once_with("laredo_i35")
+
+        assert latest_data["road_corridor_trends"]["corridors"]
+
+
+class TestAnalyzeHere:
+    def test_analyze_requires_credentials(self, monkeypatch):
+        from main import app
+
+        monkeypatch.setattr("routers.road_corridors.optional_deps_available", lambda: True)
+        monkeypatch.setattr("routers.road_corridors.sentinel_credentials_configured", lambda: False)
+        client = TestClient(app)
+        resp = client.post(
+            "/api/road-corridors/analyze",
+            json={"lat": 27.51, "lon": -99.53},
+        )
+        assert resp.status_code == 503
+
+    def test_analyze_starts_job(self, monkeypatch):
+        from main import app
+
+        monkeypatch.setattr("routers.road_corridors.optional_deps_available", lambda: True)
+        monkeypatch.setattr("routers.road_corridors.sentinel_credentials_configured", lambda: True)
+
+        def fake_enqueue(lat, lon, label=None):
+            from services.road_corridor_sat.jobs import AnalyzeJob
+
+            return AnalyzeJob(job_id="job123", lat=lat, lon=lon, status="queued")
+
+        monkeypatch.setattr("routers.road_corridors.enqueue_analyze", fake_enqueue)
+        client = TestClient(app)
+        resp = client.post(
+            "/api/road-corridors/analyze",
+            json={"lat": 27.51, "lon": -99.53},
+        )
+        assert resp.status_code == 200
+        body = resp.json()
+        assert body["job_id"] == "job123"
+        assert body["status"] == "queued"
+
+
+class TestRoadCorridorApi:
+    def test_list_presets(self):
+        from main import app
+
+        client = TestClient(app)
+        resp = client.get("/api/road-corridors")
+        assert resp.status_code == 200
+        body = resp.json()
+        assert body["ok"] is True
+        ids = {p["id"] for p in body["presets"]}
+        assert "laredo_i35" in ids
+
+    def test_get_preset_detail(self):
+        from main import app
+
+        client = TestClient(app)
+        resp = client.get("/api/road-corridors/laredo_i35")
+        assert resp.status_code == 200
+        body = resp.json()
+        assert body["preset"]["id"] == "laredo_i35"
+        assert body["result"]["preset_id"] == "laredo_i35"
+
+    def test_unknown_preset_404(self):
+        from main import app
+
+        client = TestClient(app)
+        resp = client.get("/api/road-corridors/not-a-real-preset")
+        assert resp.status_code == 404
+
+
+class TestStorage:
+    def test_store_analysis_result_roundtrip(self, tmp_path, monkeypatch):
+        monkeypatch.setattr("services.road_corridor_sat.storage.DATA_ROOT", tmp_path)
+        monkeypatch.setattr(
+            "services.road_corridor_sat.storage.STATE_PATH",
+            tmp_path / "_refresh_state.json",
+        )
+        from services.road_corridor_sat.storage import load_preset_result, store_analysis_result
+
+        preset = get_preset("laredo_i35")
+        assert preset is not None
+        store_analysis_result(
+            preset["id"],
+            label=preset["label"],
+            bbox=preset["bbox"],
+            country=preset["country"],
+            category=preset["category"],
+            road_count=4,
+            frame_count=2,
+            detections=[{"timestamp": "2026-05-01T12:00:00Z", "confidence": 0.9}],
+        )
+        loaded = load_preset_result("laredo_i35")
+        assert loaded is not None
+        assert loaded["total_detections"] == 1
+        assert loaded["daily_counts"] == [{"date": "2026-05-01", "count": 1}]
+        on_disk = json.loads((tmp_path / "laredo_i35.json").read_text(encoding="utf-8"))
+        assert on_disk["status"] == "ok"

backend/third_party/drishx/NOTICE.md

@@ -0,0 +1,11 @@
+# DrishX / S2 truck motion detection — third-party notice
+
+Detection code in `backend/services/road_corridor_sat/` is adapted from:
+
+- **DrishX** — MIT License — [sparkyniner/DRISH-X-Satellite-powered-freight-intelligence-](https://github.com/sparkyniner/DRISH-X-Satellite-powered-freight-intelligence-)
+- **S2TruckDetect / Fisser et al. (2022)** — [Detecting Moving Trucks on Roads Using Sentinel-2 Data](https://ui.adsabs.harvard.edu/abs/2022RemS...14.1595F/abstract)
+
+The trained Random Forest weights ship as `backend/data/drishx/rf_model.pickle` from the DrishX distribution.
+
+Satellite imagery: [Copernicus Data Space Ecosystem](https://dataspace.copernicus.eu/) (free, ESA).  
+Road network: [OpenStreetMap](https://www.openstreetmap.org/) via Overpass.