feat: every EO specialist's prediction renders as a map layer

Round-tripped the per-pixel argmax raster from the droplet so the HF
Space can vectorise it onto WGS84 polygons and the MapLibre layer
actually paints the live model output. Was: only terramind_synthesis's
local fallback path generated polygons; everything else returned
class fractions only and the map showed nothing.

Wire format: droplet's /v1/prithvi-pluvial and /v1/terramind responses
gain `pred_b64` (uint8 base64) + `pred_shape` + (terramind only)
`class_labels`. Small payload — 224x224 uint8 round-trips in ~50 KB.

HF side:
- new app/context/_polygonize.py: polygonize_class_raster() and
polygonize_binary_mask() helpers
- app/context/eo_chip_cache.py adds `bounds_4326` to chip output
- app/context/terramind_nyc lulc/buildings accept bounds_4326,
_try_remote polygonises the response and attaches polygons_geojson
- app/context/terramind_synthesis.py polygonises the remote response
- app/flood_layers/prithvi_live.py polygonises the remote water mask
- app/fsm.py threads chip.bounds_4326 to lulc + buildings calls

Frontend:
- RipMap.svelte adds prithvi-live + terramind-buildings layers; keeps
terramind-lulc for the LoRA + base synthesis (LoRA wins when both fire)
- +page.svelte populates each FC from the matching state field

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>

app/context/_polygonize.py

@@ -0,0 +1,165 @@
+"""Vectorize a uint8 prediction raster (binary mask or class index)
+into an EPSG:4326 GeoJSON FeatureCollection so the frontend can paint
+it on the MapLibre map.
+
+The droplet's `/v1/prithvi-pluvial` and `/v1/terramind` routes return
+their predictions as base64-encoded uint8 with a shape and (where
+relevant) a class-label list. This module reconstructs the affine
+transform from the chip's geographic bounds (which the HF Space
+already knows) and walks `rasterio.features.shapes` to build polygons
+in the chip's native CRS, then reprojects to WGS84 for the map.
+
+Best-effort: any failure returns an empty FeatureCollection rather
+than raising into the caller's path. The map layer is decorative —
+the briefing is the deliverable.
+"""
+from __future__ import annotations
+
+import base64
+import logging
+
+log = logging.getLogger("riprap.polygonize")
+
+EMPTY: dict = {"type": "FeatureCollection", "features": []}
+
+
+def _decode_pred(pred_b64: str, pred_shape: list[int]):
+    """Inverse of the droplet's `base64(pred.tobytes())`. Returns a
+    uint8 numpy array of shape `pred_shape`, or None on decode error."""
+    try:
+        import numpy as np
+        raw = base64.b64decode(pred_b64)
+        return np.frombuffer(raw, dtype="uint8").reshape(pred_shape)
+    except Exception:
+        log.exception("polygonize: pred decode failed")
+        return None
+
+
+def polygonize_class_raster(
+    pred_b64: str,
+    pred_shape: list[int],
+    class_labels: list[str] | None,
+    bounds_4326: tuple[float, float, float, float],
+    *,
+    drop_classes: tuple[int, ...] = (0,),
+    simplify_tolerance: float = 0.0,
+) -> dict:
+    """Vectorize a categorical prediction raster (one integer class per
+    pixel) into a FeatureCollection with one Feature per connected
+    polygon. `bounds_4326` is `(minlon, minlat, maxlon, maxlat)` of the
+    chip; the raster is assumed to span those bounds at uniform
+    pixel size. Each feature carries `class_idx` and `class_label`
+    so the frontend can color by class.
+
+    `drop_classes`: skip pixels matching these class indices (default
+    drops 0 = "Background" / "outside" / etc).
+    """
+    pred = _decode_pred(pred_b64, pred_shape)
+    if pred is None:
+        return EMPTY
+    try:
+        from rasterio.features import shapes
+        from rasterio.transform import from_bounds
+        from shapely.geometry import shape
+        h, w = pred.shape
+        minlon, minlat, maxlon, maxlat = bounds_4326
+        # The chip is in EPSG:4326 for our use — Sentinel-2 chips are
+        # natively in their UTM zone, but we can polygonize against the
+        # WGS84 extent because the inference chip is a small bbox where
+        # the pixel-grid → lat/lon mapping is locally affine (sub-pixel
+        # error at NYC scale).
+        transform = from_bounds(minlon, minlat, maxlon, maxlat, w, h)
+        feats = []
+        for geom, value in shapes(pred, mask=pred > 0, transform=transform):
+            v = int(value)
+            if v in drop_classes:
+                continue
+            label = (class_labels[v]
+                     if class_labels and 0 <= v < len(class_labels)
+                     else f"class_{v}")
+            poly = shape(geom)
+            if simplify_tolerance > 0:
+                poly = poly.simplify(simplify_tolerance, preserve_topology=True)
+            if poly.is_empty:
+                continue
+            feats.append({
+                "type": "Feature",
+                "geometry": poly.__geo_interface__,
+                "properties": {
+                    "class_idx": v,
+                    "class_label": label,
+                    "fill_color": _PALETTE.get(label.lower(), _DEFAULT_FILL),
+                },
+            })
+        return {"type": "FeatureCollection", "features": feats}
+    except Exception:
+        log.exception("polygonize: class raster vectorisation failed")
+        return EMPTY
+
+
+def polygonize_binary_mask(
+    pred_b64: str,
+    pred_shape: list[int],
+    bounds_4326: tuple[float, float, float, float],
+    *,
+    label: str = "water",
+    fill_color: str = "#4A90E2",
+    simplify_tolerance: float = 0.0,
+) -> dict:
+    """Vectorize a binary prediction raster (e.g. Prithvi water mask;
+    1 = water, 0 = not). Returns one Feature per connected positive
+    region. Use this for prithvi_eo_live and the buildings LoRA."""
+    pred = _decode_pred(pred_b64, pred_shape)
+    if pred is None:
+        return EMPTY
+    try:
+        from rasterio.features import shapes
+        from rasterio.transform import from_bounds
+        from shapely.geometry import shape
+        h, w = pred.shape
+        minlon, minlat, maxlon, maxlat = bounds_4326
+        transform = from_bounds(minlon, minlat, maxlon, maxlat, w, h)
+        feats = []
+        for geom, _value in shapes(pred, mask=pred > 0, transform=transform):
+            poly = shape(geom)
+            if simplify_tolerance > 0:
+                poly = poly.simplify(simplify_tolerance, preserve_topology=True)
+            if poly.is_empty:
+                continue
+            feats.append({
+                "type": "Feature",
+                "geometry": poly.__geo_interface__,
+                "properties": {
+                    "class_label": label,
+                    "fill_color": fill_color,
+                },
+            })
+        return {"type": "FeatureCollection", "features": feats}
+    except Exception:
+        log.exception("polygonize: binary mask vectorisation failed")
+        return EMPTY
+
+
+# Lightweight palette used by the LULC + buildings layers. Frontend
+# may override via `fill_color` per feature; this is a sensible
+# default keyed on lowercase class labels.
+_DEFAULT_FILL = "#A0A0A0"
+_PALETTE = {
+    # ESRI 2020 LULC schema (terramind v1 base generative)
+    "water":              "#1F77B4",
+    "trees":              "#2CA02C",
+    "grass":              "#7FBF53",
+    "flooded vegetation": "#74C476",
+    "crops":              "#E1C75A",
+    "scrub/shrub":        "#A6BC44",
+    "built":              "#D62728",
+    "bare ground":        "#B07A4C",
+    "snow/ice":            "#E0E7EC",
+    "clouds":              "#CCCCCC",
+    # NYC LoRA LULC schema
+    "cropland":           "#E1C75A",
+    "bare":               "#B07A4C",
+    # Buildings LoRA
+    "building":           "#D62728",
+    "background":         _DEFAULT_FILL,
+}

app/context/eo_chip_cache.py

@@ -281,6 +281,32 @@ def _fetch_and_build(lat: float, lon: float, timeout_s: float) -> dict[str, Any]
             log.exception("eo_chip: tensor build failed")
             return {"ok": False,
                     "err": f"tensor build failed: {type(e).__name__}: {e}"}
+        # Compute the chip's WGS84 bbox so downstream TerraMind specialists
+        # can polygonise their predictions onto the map. The chip is
+        # CHIP_PX × CHIP_PX at PIXEL_M (10 m) in the scene's UTM zone;
+        # reproject the four corners to EPSG:4326 and use the
+        # axis-aligned envelope.
+        try:
+            from pyproj import Transformer
+            half_m = (CHIP_PX * PIXEL_M) / 2.0
+            t_to_utm = Transformer.from_crs(
+                "EPSG:4326", f"EPSG:{modalities['epsg']}", always_xy=True)
+            t_to_4326 = Transformer.from_crs(
+                f"EPSG:{modalities['epsg']}", "EPSG:4326", always_xy=True)
+            cx, cy = t_to_utm.transform(lon, lat)
+            corners_utm = [
+                (cx - half_m, cy - half_m),
+                (cx - half_m, cy + half_m),
+                (cx + half_m, cy - half_m),
+                (cx + half_m, cy + half_m),
+            ]
+            corners_ll = [t_to_4326.transform(x, y) for x, y in corners_utm]
+            lons = [c[0] for c in corners_ll]
+            lats = [c[1] for c in corners_ll]
+            modalities["bounds_4326"] = (
+                min(lons), min(lats), max(lons), max(lats))
+        except Exception:
+            log.exception("eo_chip: bounds_4326 reprojection failed")
         return modalities
 
 

app/context/terramind_nyc.py

@@ -324,6 +324,34 @@ def _try_remote(adapter_name: str, modality_chips: dict) -> dict | None:
         result.setdefault("adapter", adapter_name)
         result.setdefault("repo", ADAPTERS_REPO)
         result["compute"] = f"remote · {result.get('device', 'gpu')}"
+        # Polygonize the prediction raster onto the chip's bounds so
+        # the map can paint the LULC / buildings overlay. Bounds come
+        # via the modality_chips dict — the eo_chip layer threads them
+        # through. Best-effort; never raises into the FSM.
+        bounds = modality_chips.get("bounds_4326") if modality_chips else None
+        pred_b64 = result.get("pred_b64")
+        pred_shape = result.get("pred_shape")
+        class_labels = result.get("class_labels")
+        if bounds and pred_b64 and pred_shape:
+            try:
+                from app.context._polygonize import (
+                    polygonize_binary_mask, polygonize_class_raster,
+                )
+                if adapter_name == "buildings":
+                    polys = polygonize_binary_mask(
+                        pred_b64, pred_shape, tuple(bounds),
+                        label="building", fill_color="#D62728",
+                        simplify_tolerance=2e-5,
+                    )
+                else:
+                    polys = polygonize_class_raster(
+                        pred_b64, pred_shape, class_labels, tuple(bounds),
+                        simplify_tolerance=2e-5,
+                    )
+                result["polygons_geojson"] = polys
+            except Exception:
+                log.exception("terramind/%s: polygonize failed", adapter_name)
+                result["polygons_geojson"] = None
         return result
     except _inf.RemoteUnreachable as e:
         log.info("terramind/%s: remote unreachable (%s); local fallback",
@@ -359,7 +387,12 @@ def _run(adapter_name: str, modality_chips: dict, summarizer):
     try:
         task = _ensure_adapter(adapter_name)
         spec = ADAPTER_SPECS[adapter_name]
-        logits = _tiled_predict(task, modality_chips, spec["num_classes"])
+        # Strip out bounds_4326 (auxiliary metadata, not a tensor) before
+        # handing the dict to terratorch's tiled_inference, which iterates
+        # all values as modalities.
+        tensors_only = {k: v for k, v in modality_chips.items()
+                        if k != "bounds_4326"}
+        logits = _tiled_predict(task, tensors_only, spec["num_classes"])
         # logits: (B, C, H, W). Argmax to per-pixel class id.
         pred = logits.argmax(dim=1).squeeze(0)
         result = summarizer(pred, spec["class_labels"])
@@ -390,7 +423,9 @@ def _run(adapter_name: str, modality_chips: dict, summarizer):
                 "elapsed_s": round(time.time() - t0, 2)}
 
 
-def lulc(s2l2a, s1rtc=None, dem=None) -> dict[str, Any]:
+def lulc(s2l2a, s1rtc=None, dem=None,
+          bounds_4326: tuple[float, float, float, float] | None = None,
+          ) -> dict[str, Any]:
     """5-class NYC macro land-cover.
 
     Inputs are torch tensors. The temporal models we trained expect
@@ -398,8 +433,14 @@ def lulc(s2l2a, s1rtc=None, dem=None) -> dict[str, Any]:
     Pass S1 and DEM if you have them — the published adapter was
     trained on the full triplet and accuracy degrades when modalities
     are dropped.
+
+    `bounds_4326` is `(minlon, minlat, maxlon, maxlat)` of the chip
+    in WGS84; when provided, the LULC raster is polygonised onto the
+    chip's geographic extent so the map can render an overlay.
     """
     chips = {"S2L2A": s2l2a}
+    if bounds_4326 is not None:
+        chips["bounds_4326"] = bounds_4326
     if s1rtc is not None:
         chips["S1RTC"] = s1rtc
     if dem is not None:
@@ -407,9 +448,13 @@ def lulc(s2l2a, s1rtc=None, dem=None) -> dict[str, Any]:
     return _run("lulc", chips, _summarize_lulc)
 
 
-def buildings(s2l2a, s1rtc=None, dem=None) -> dict[str, Any]:
+def buildings(s2l2a, s1rtc=None, dem=None,
+               bounds_4326: tuple[float, float, float, float] | None = None,
+               ) -> dict[str, Any]:
     """Binary NYC building-footprint mask. Same input contract as lulc()."""
     chips = {"S2L2A": s2l2a}
+    if bounds_4326 is not None:
+        chips["bounds_4326"] = bounds_4326
     if s1rtc is not None:
         chips["S1RTC"] = s1rtc
     if dem is not None:

app/context/terramind_synthesis.py

@@ -299,6 +299,28 @@ def fetch(lat: float, lon: float, timeout_s: float = 60.0) -> dict[str, Any]:
                                           timeout=timeout_s)
                 if remote.get("ok"):
                     elapsed = round(time.time() - t0, 2)
+                    # Polygonize the prediction raster for the map
+                    # layer. The droplet returns the per-pixel argmax;
+                    # we vectorize against the chip's bounds.
+                    polys = None
+                    pred_b64 = remote.get("pred_b64")
+                    pred_shape = remote.get("pred_shape")
+                    class_labels = (remote.get("class_labels")
+                                    or LULC_CLASSES)
+                    if pred_b64 and pred_shape:
+                        try:
+                            from app.context._polygonize import (
+                                polygonize_class_raster,
+                            )
+                            polys = polygonize_class_raster(
+                                pred_b64, pred_shape, class_labels,
+                                tuple(bounds_4326),
+                                simplify_tolerance=2e-5,
+                            )
+                        except Exception:
+                            log.exception("terramind/synthesis: "
+                                          "polygonize failed")
+                            polys = None
                     out = {
                         "ok": True,
                         "synthetic_modality": True,
@@ -313,7 +335,7 @@ def fetch(lat: float, lon: float, timeout_s: float = 60.0) -> dict[str, Any]:
                         "n_classes_observed": remote.get("n_classes_observed") or 0,
                         "chip_shape": remote.get("shape") or [],
                         "bounds_4326": list(bounds_4326),
-                        "polygons_geojson": None,
+                        "polygons_geojson": polys,
                         "label_schema": remote.get("label_schema") or "",
                         "compute": f"remote · {remote.get('device', 'gpu')}",
                         "elapsed_s": elapsed,

app/flood_layers/prithvi_live.py

@@ -402,6 +402,38 @@ def _fetch_inner(lat: float, lon: float, timeout_s: float) -> dict[str, Any]:
                     timeout=timeout_s,
                 )
                 if remote.get("ok"):
+                    # Vectorize the remote prediction raster so the map
+                    # actually renders the live water polygons. The
+                    # droplet returns `pred_b64` (uint8 binary mask);
+                    # we polygonize against the chip's WGS84 bounds
+                    # which we know locally from `ref_da`.
+                    polys = None
+                    pred_b64 = remote.get("pred_b64")
+                    pred_shape = remote.get("pred_shape")
+                    if pred_b64 and pred_shape:
+                        try:
+                            xs = ref_da.x.values
+                            ys = ref_da.y.values
+                            from pyproj import Transformer
+                            t_inv = Transformer.from_crs(
+                                f"EPSG:{epsg}", "EPSG:4326",
+                                always_xy=True)
+                            minx, maxx = float(xs.min()), float(xs.max())
+                            miny, maxy = float(ys.min()), float(ys.max())
+                            minlon, minlat = t_inv.transform(minx, miny)
+                            maxlon, maxlat = t_inv.transform(maxx, maxy)
+                            from app.context._polygonize import (
+                                polygonize_binary_mask,
+                            )
+                            polys = polygonize_binary_mask(
+                                pred_b64, pred_shape,
+                                (minlon, minlat, maxlon, maxlat),
+                                label="water", fill_color="#1F77B4",
+                                simplify_tolerance=2e-5,
+                            )
+                        except Exception:
+                            log.exception("prithvi_live: remote polygonize failed")
+                            polys = None
                     return {
                         "ok": True,
                         "item_id": item.id,
@@ -409,12 +441,7 @@ def _fetch_inner(lat: float, lon: float, timeout_s: float) -> dict[str, Any]:
                         "cloud_cover": cc,
                         "pct_water_full": remote.get("pct_water_full"),
                         "pct_water_within_500m": remote.get("pct_water_within_500m"),
-                        # Service doesn't currently return polygonised GeoJSON
-                        # (transport size); the local fallback below produces
-                        # them. For now the remote path leaves polygons null
-                        # and the map renders the layer empty until the
-                        # service grows a polygonisation step.
-                        "polygons_geojson": None,
+                        "polygons_geojson": polys,
                         "compute": f"remote · {remote.get('device', 'gpu')}",
                         "elapsed_s": round(time.time() - t0, 2),
                     }

app/fsm.py

@@ -812,6 +812,7 @@ def step_terramind_lulc(state: State) -> State:
             tensors.get("S2L2A"),
             s1rtc=tensors.get("S1RTC"),
             dem=tensors.get("DEM"),
+            bounds_4326=chip.get("bounds_4326"),
         )
         rec["ok"] = bool(out.get("ok"))
         if not rec["ok"]:
@@ -854,6 +855,7 @@ def step_terramind_buildings(state: State) -> State:
             tensors.get("S2L2A"),
             s1rtc=tensors.get("S1RTC"),
             dem=tensors.get("DEM"),
+            bounds_4326=chip.get("bounds_4326"),
         )
         rec["ok"] = bool(out.get("ok"))
         if not rec["ok"]:

services/riprap-models/main.py

@@ -201,6 +201,11 @@ def _prithvi_pluvial(payload: PrithviIn) -> dict[str, Any]:
     dist = np.sqrt((yy - cy) ** 2 + (xx - cx) ** 2)
     mask = dist <= min(50, min(h, w) // 4)
     pct_500m = float(100.0 * pred[mask].mean()) if mask.any() else pct_full
+    # Pass the raw prediction raster back so HF can vectorise it into
+    # GeoJSON for the map layer using the chip-georef it already has
+    # locally (ref_da from _build_chip). uint8 is small enough for a
+    # base64 round-trip (~50 KB at 224x224).
+    pred_b64 = base64.b64encode(pred.tobytes()).decode("ascii")
     return {
         "ok": True,
         "elapsed_s": round(time.time() - t0, 2),
@@ -211,6 +216,8 @@ def _prithvi_pluvial(payload: PrithviIn) -> dict[str, Any]:
         "scene_datetime": payload.scene_datetime,
         "cloud_cover": payload.cloud_cover,
         "shape": [int(h), int(w)],
+        "pred_b64": pred_b64,
+        "pred_shape": [int(h), int(w)],
     }
 
 
@@ -416,6 +423,8 @@ def _terramind_synthesis_inference(payload: TerramindIn) -> dict[str, Any]:
                            key=lambda kv: kv[1], reverse=True))
     dominant_class = next(iter(ordered)) if ordered else "unknown"
     dominant_pct = ordered.get(dominant_class, 0.0)
+    pred_u8 = class_idx.astype("uint8")
+    pred_b64 = base64.b64encode(pred_u8.tobytes()).decode("ascii")
     return {
         "ok": True,
         "adapter": "synthesis",
@@ -433,6 +442,9 @@ def _terramind_synthesis_inference(payload: TerramindIn) -> dict[str, Any]:
         "label_schema": "ESRI 2020-2022 Land Cover (tentative — TerraMind "
                          "tokenizer source confirms ESRI but not exact "
                          "label-to-index mapping)",
+        "pred_b64": pred_b64,
+        "pred_shape": [int(s) for s in pred_u8.shape],
+        "class_labels": _TERRAMIND_SPECS["synthesis"]["labels"],
     }
 
 
@@ -493,6 +505,8 @@ def _terramind_inference(payload: TerramindIn) -> dict[str, Any]:
         except Exception:
             log.debug("terramind/buildings: scipy.ndimage unavailable")
 
+    # Pass the per-pixel argmax raster back so HF can vectorise it.
+    pred_b64 = base64.b64encode(pred.tobytes()).decode("ascii")
     return {
         "ok": True,
         "adapter": payload.adapter,
@@ -507,6 +521,9 @@ def _terramind_inference(payload: TerramindIn) -> dict[str, Any]:
         "pct_buildings": round(100.0 * float((pred == 1).sum()) / n, 2)
                          if payload.adapter == "buildings" else None,
         "n_building_components": n_components,
+        "pred_b64": pred_b64,
+        "pred_shape": [int(s) for s in pred.shape],
+        "class_labels": spec["labels"],
     }
 
 

web/sveltekit/build/200.html

@@ -6,17 +6,17 @@
     <meta name="viewport" content="width=device-width, initial-scale=1" />
     <meta name="description" content="Riprap — citation-grounded NYC flood-exposure briefings." />
     <title>Riprap — flood-exposure briefing</title>
-    <link href="/_app/immutable/entry/start.Ct-7wur6.js" rel="modulepreload">
-		<link href="/_app/immutable/chunks/5yhzCbvM.js" rel="modulepreload">
+    <link href="/_app/immutable/entry/start.FA4XFe2L.js" rel="modulepreload">
+		<link href="/_app/immutable/chunks/BIe30OUb.js" rel="modulepreload">
 		<link href="/_app/immutable/chunks/BTUA7_xE.js" rel="modulepreload">
-		<link href="/_app/immutable/entry/app.CnUxdbZ2.js" rel="modulepreload">
+		<link href="/_app/immutable/entry/app.DBibj1kM.js" rel="modulepreload">
 		<link href="/_app/immutable/chunks/CXQd8Y6F.js" rel="modulepreload">
 		<link href="/_app/immutable/chunks/CWw6qgC_.js" rel="modulepreload">
 		<link href="/_app/immutable/chunks/Bd-v_9Ud.js" rel="modulepreload">
 		<link href="/_app/immutable/chunks/CW0zSL4D.js" rel="modulepreload">
-		<link href="/_app/immutable/nodes/0.CVp297sI.js" rel="modulepreload">
+		<link href="/_app/immutable/nodes/0.BaDkkERB.js" rel="modulepreload">
 		<link href="/_app/immutable/chunks/DxQlA7U2.js" rel="modulepreload">
-		<link href="/_app/immutable/chunks/Bsd24zeB.js" rel="modulepreload">
+		<link href="/_app/immutable/chunks/DlifwAOf.js" rel="modulepreload">
 		<link href="/_app/immutable/chunks/DCD6_LXk.js" rel="modulepreload">
 		<link href="/_app/immutable/chunks/B0XoTt7U.js" rel="modulepreload">
 		<link href="/_app/immutable/chunks/DixtWtwq.js" rel="modulepreload">
@@ -28,15 +28,15 @@
     <div style="display: contents">
 			<script>
 				{
-					__sveltekit_1fdrmyh = {
+					__sveltekit_2hdpwx = {
 						base: ""
 					};
 
 					const element = document.currentScript.parentElement;
 
 					Promise.all([
-						import("/_app/immutable/entry/start.Ct-7wur6.js"),
-						import("/_app/immutable/entry/app.CnUxdbZ2.js")
+						import("/_app/immutable/entry/start.FA4XFe2L.js"),
+						import("/_app/immutable/entry/app.DBibj1kM.js")
 					]).then(([kit, app]) => {
 						kit.start(app, element);
 					});

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class="app-footer-guard"><strong>Riprap does not predict damage.</strong> This tool is for professional analytical work, not personal property decisions.
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Get a written briefing where every numeric claim links to its primary public-record source.</span></h1> <form class="land-query svelte-drzq4r" role="search"><span class="land-query-prompt svelte-drzq4r" aria-hidden="true">›</span> <input type="text" placeholder="Address, neighborhood, or BBL. e.g. 80 Pioneer Street, Red Hook" class="land-query-input svelte-drzq4r" aria-label="Query an address, neighborhood, or BBL"/> <button type="submit" class="land-query-submit svelte-drzq4r">Brief this place →</button></form> <div class="land-cycling svelte-drzq4r" aria-live="polite"><span class="land-cycling-label svelte-drzq4r">Try:</span> <button type="button" class="land-cycling-rail svelte-drzq4r" title="Run this example"></button></div></main>');function je(e,s){P(s,!0);const r=["80 Pioneer Street, Red Hook","Coney Island Hospital","PS 188, Lower East Side","Hammels Houses, Rockaway","Bowling Green station","555 W 57th Street"];let t=A(""),a=A(0);E(()=>{if(typeof window>"u")return;const 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n=be(),u=c(n);ve(u,l=>N(t,l),()=>d(t)),k(2),o(n),m(e,n),M()}var Se=w(`<section class="land-section svelte-1anw2jf"><div class="land-section-head svelte-1anw2jf"><span class="section-label">What you'll get back</span> <span class="land-section-meta svelte-1anw2jf">A grounded paragraph with citations, not a chatbot answer.</span></div> <div class="land-preview-grid svelte-1anw2jf"><div class="land-preview-pane land-preview-pane-excerpt svelte-1anw2jf"><div class="land-preview-eyebrow svelte-1anw2jf">Briefing excerpt</div> <p class="land-preview-body svelte-1anw2jf">The lot sits inside the FEMA <span class="land-preview-cite svelte-1anw2jf">1% AE flood zone <sup class="svelte-1anw2jf">[c3]</sup></span>,
         with Sandy high-water marks recorded <span class="land-preview-cite svelte-1anw2jf">4.7 ft above grade <sup class="svelte-1anw2jf">[c1]</sup></span>.
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web/sveltekit/build/_app/immutable/nodes/{3.DJu_XRyp.js → 3.KbM-URMV.js}

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-import{d as z,e as V,a as U,f as q,s as j}from"../chunks/CWw6qgC_.js";import{p as Z,am as X,a5 as d,a4 as l,a9 as Q,ai as $,t as J,a as ee,s as r,c as e,o as i,r as t,al as te}from"../chunks/BTUA7_xE.js";import{B as ie}from"../chunks/BatqQaKj.js";import{f as re,R as ae,M as ne,C as se,F as oe}from"../chunks/C3emz_VZ.js";import{p as de,b as M}from"../chunks/DxQlA7U2.js";const le=!0,fe=Object.freeze(Object.defineProperty({__proto__:null,prerender:le},Symbol.toStringTag,{value:"Module"})),R="80 Pioneer Street · Red Hook · Brooklyn",y={c1:{id:"c1",n:1,tier:"empirical",source:"USGS",title:"Hurricane Sandy storm tide elevations, NY-NJ Harbor",docId:"USGS-OFR-2013-1234",url:"https://pubs.usgs.gov/of/2013/1234/",vintage:"2013-05",retrieved:"2026-04-28"},c2:{id:"c2",n:2,tier:"empirical",source:"NYC OEM",title:"Hurricane Sandy Inundation Zone (2012)",docId:"NYCOEM-SIZ-2013",url:"https://data.cityofnewyork.us/dataset/sandy-inundation-zone",vintage:"2013-01",retrieved:"2026-04-28"},c3:{id:"c3",n:3,tier:"empirical",source:"FloodNet NYC",title:"Sensor BK-RH-002 — Coffey Park, monthly exceedance",docId:"FN-BK-RH-002",url:"https://floodnet.nyc/sensor/BK-RH-002",vintage:"2026-04",retrieved:"2026-05-02"},c4:{id:"c4",n:4,tier:"modeled",source:"FEMA",title:"Preliminary Flood Insurance Rate Map, panel 36047C0207G",docId:"FEMA-FIRM-36047C0207G",url:"https://msc.fema.gov/portal/search",vintage:"2024-09",retrieved:"2026-04-28"},c5:{id:"c5",n:5,tier:"modeled",source:"NYC DEP",title:"Stormwater Flood Map — Moderate Stormwater Scenario",docId:"NYCDEP-SWFM-2024",url:"https://nyc.gov/stormwater-map",vintage:"2024-06",retrieved:"2026-04-28"},c6:{id:"c6",n:6,tier:"modeled",source:"NPCC4",title:"Sea-level rise projections, 2050 90th percentile",docId:"NPCC4-Ch3-Tbl3.2",url:"https://nyas.org/npcc4",vintage:"2024-03",retrieved:"2026-04-28"},c7:{id:"c7",n:7,tier:"proxy",source:"NYC 311",title:"Flooding service requests, BK CB6 2019–2025",docId:"NYC311-FLD-CB6",url:"https://data.cityofnewyork.us/311",vintage:"2025-12",retrieved:"2026-05-01"},c8:{id:"c8",n:8,tier:"proxy",source:"FEMA NFIP",title:"National Flood Insurance Program claims, tract 36047008500",docId:"NFIP-T36047008500",url:"https://www.fema.gov/openfema",vintage:"2024-12",retrieved:"2026-04-28"},c9:{id:"c9",n:9,tier:"synthetic",source:"TerraMind v1.2",title:"Synthetic SAR backscatter for 2025-09-14 (Sentinel-1 cloud-occluded)",docId:"RIPRAP-SYN-20250914",url:"#methodology-synthetic",vintage:"2025-09",retrieved:"2026-05-02"},c10:{id:"c10",n:10,tier:"modeled",source:"NYC DCP",title:"Waterfront Revitalization Program — Coastal Risk Area",docId:"NYCDCP-WRP-2022",url:"https://nyc.gov/dcp/wrp",vintage:"2022-11",retrieved:"2026-04-28"}},x=[{kind:"status",html:'<p class="briefing-deck"><strong>80 Pioneer Street, Red Hook, Brooklyn 11231.</strong> Block 597, Lot 30. Industrial Business Zone (IBZ-RH). Queried 2026-05-02 14:22 ET. <span class="briefing-meta">Briefing v0.4.2 · 9 specialists fired · 1 silent (TidalGauge: out of range)</span></p>'},{kind:"head",n:"01",label:"Status",title:"Coastal-edge, post-Sandy, multi-hazard"},{kind:"prose",parts:[{tier:"empirical",text:"The address sits 380 ft inland of the Erie Basin bulkhead, at a ground elevation of 6.2 ft NAVD88",cite:"c1"},{text:" — within the "},{tier:"empirical",text:"2012 Sandy Inundation Zone, which recorded a peak storm tide of 11.4 ft NAVD88 at the Battery",cite:"c2"},{text:" 2.4 mi to the northwest. "},{tier:"modeled",text:"FEMA's preliminary FIRM places the parcel in Zone AE (BFE 11 ft NAVD88)",cite:"c4"},{text:", a 4.8 ft freeboard above current grade. The site is upgradient of two FloodNet sensors and three blocks from a chronic 311 cluster."}]},{kind:"head",n:"02",label:"Empirical evidence",tier:"empirical"},{kind:"prose",parts:[{tier:"empirical",text:"FloodNet sensor BK-RH-002 (Coffey Park, 1,200 ft south) recorded 7 above-curb events between 2024-06 and 2026-04",cite:"c3"},{text:", with a peak depth of 14.3 cm during the 2025-09-29 nor'easter. "},{tier:"empirical",text:"USGS post-Sandy high-water marks within 500 ft cluster between 6.8 and 8.1 ft NAVD88",cite:"c1"},{text:", consistent with 0.6–1.9 ft of standing water at the queried address during the storm."}]},{kind:"head",n:"03",label:"Modeled scenarios",tier:"modeled"},{kind:"prose",parts:[{tier:"modeled",text:"DEP's Moderate Stormwater Scenario (2.13 in/hr design storm) shows ponding ≥4 in across the western half of the lot",cite:"c5"},{text:", routed by the 1.2% slope toward Imlay St. "},{tier:"modeled",text:"Under NPCC4's 2050 90th-percentile sea-level rise (30 in)",cite:"c6"},{text:", the parcel falls within the projected daily-tidal floodplain by mid-century. "},{tier:"synthetic",text:"Synthetic SAR backscatter for 2025-09-14 (Sentinel-1 cloud-occluded) was generated by TerraMind v1.2 and is presented as a prior, not an observation",cite:"c9"},{text:"; treat with appropriate caution."}]},{kind:"head",n:"04",label:"Policy context"},{kind:"prose",parts:[{tier:"proxy",text:"311 flood complaints within the surrounding census tract total 89 calls over 2019���2025, with seasonal clustering in Aug–Oct",cite:"c7"},{text:". "},{tier:"proxy",text:"NFIP claims aggregated to tract 36047008500 total $4.1M across 47 paid losses since 2000",cite:"c8"},{text:". "},{tier:"modeled",text:"The site lies within the NYC Waterfront Revitalization Program Coastal Risk Area; CEQR §817 review applies to any discretionary action",cite:"c10"},{text:"."}]}],ce={wallSeconds:14,cacheHit:.92,cards:[{id:"fc-fema",stone:"cornerstone",tier:"modeled",variant:"headline",source:"FEMA",agency:"Federal Emergency Management Agency",vintage:"2024-09",title:"Preliminary FIRM, panel 36047C0207G",headline:"Zone AE",subhead:"BFE 11 ft NAVD88 · freeboard +4.8 ft",body:"Address sits within the regulatory 1% annual-chance floodplain. Base Flood Elevation 11.0 ft NAVD88; first floor must be at or above this datum for NFIP rating.",docId:"FEMA-FIRM-36047C0207G",citeId:"c4",mapLayer:"fema-ae"},{id:"fc-hwm",stone:"cornerstone",tier:"empirical",variant:"tabular",source:"USGS",agency:"U.S. Geological Survey",vintage:"2013-05",title:"Post-Sandy high-water marks within 500 ft",columns:["id","elev.","dist."],rows:[["HWM-NY-3081","7.4 ft NAVD88","0.18 mi"],["HWM-NY-3082","8.1 ft NAVD88","0.22 mi"],["HWM-NY-3105","6.8 ft NAVD88","0.31 mi"]],sub:"3 marks · max 8.1 ft · surveyed Nov 2012",docId:"USGS-OFR-2013-1234",citeId:"c1",mapLayer:"hwm"},{id:"fc-stormwater",stone:"cornerstone",tier:"modeled",variant:"raster",source:"NYC DEP",agency:"NYC Dept. of Environmental Protection",vintage:"2024-06",title:"Stormwater Flood Map · moderate scenario",rasterKind:"stormwater",sub:"2.13 in/hr · ponding ≥4 in W half of lot · routed toward Imlay St",docId:"NYCDEP-SWFM-2024",citeId:"c5",mapLayer:"stormwater"},{id:"fc-register-rh",stone:"keystone",tier:"empirical",variant:"register",source:"NYC OpenData",agency:"NYC OpenData · multi-agency join",vintage:"2026-05",title:"Nearby exposed assets",registers:[{reg:"MTA",tier:"empirical",label:"Smith–9 St subway entrance",detail:"0.34 mi · F · G",sourceId:"MTA-ENT-N048",vintage:"2025-11",note:null},{reg:"NYCHA",tier:"empirical",label:"Red Hook East Houses",detail:"0.41 mi · 2,878 res.",sourceId:"NYCHA-RHE",vintage:"2025-Q3",note:null},{reg:"NYCHA",tier:"empirical",label:"Red Hook West Houses",detail:"0.52 mi · 3,142 res.",sourceId:"NYCHA-RHW",vintage:"2025-Q3",note:null},{reg:"DOE",tier:"empirical",label:"PS 27 Agnes Y. Humphrey",detail:"0.29 mi · 271 K-5",sourceId:"DOE-K027",vintage:"2024-25",note:null},{reg:"DOH",tier:"empirical",label:null,detail:null,sourceId:null,vintage:null,note:"no acute-care hospital within 1.0 mi (silent)"},{reg:"PLUTO",tier:"empirical",label:"Lot 36047 / 521 / 7",detail:"BIN 3018472 · MX-1",sourceId:"PLUTO-2024v2",vintage:"2024-12",note:null}],sub:"5 of 6 registers fired · 1 silent · joined within 1.0 mi",docId:"RIPRAP-EXP-RH80",citeId:"c-reg-rh",mapLayer:"registers"},{id:"fc-floodnet",stone:"touchstone",tier:"empirical",variant:"spark",source:"FloodNet",agency:"FloodNet NYC sensor network",vintage:"2026-04",title:"Sensor BK-RH-002, monthly above-curb events",headline:"7 events",subhead:"Jun 2024 → Apr 2026 · peak 14.3 cm",spark:[0,0,1,0,2,1,0,0,3,0,1,0,0,0,2,1,0,0,1,0,2,4,1,1],sparkSub:"Sensor located 0.21 mi N at Coffey & Van Brunt. Above-curb depth in cm; events ≥2 cm.",docId:"FN-BK-RH-002",citeId:"c3",mapLayer:"floodnet"},{id:"fc-311",stone:"touchstone",tier:"proxy",variant:"histogram",source:"NYC 311",agency:"NYC 311 service requests",vintage:"2025-12",title:"Recent 311 flood complaints, BK CB6",headline:"89 calls",subhead:"2019–2025 · seasonal cluster Aug–Oct",histogram:[3,2,1,0,1,4,7,12,18,11,5,3,4,2,1,0,2,3,8,9,4,2,1,0],sparkSub:"Filtered to complaint types: Sewer (Backup), Street Flooding, Catch Basin Clogged. Within 200 m of address.",docId:"NYC311-FLD-CB6",citeId:"c7",mapLayer:"complaints"},{id:"fc-prithvi-pluvial",stone:"touchstone",tier:"modeled",variant:"raster-pred",source:"Prithvi-NYC-Pluvial",agency:"NASA-IBM Prithvi v2 · NYC fine-tune",vintage:"2026-05-02 · Sentinel-2",title:"Pluvial flood prediction · Prithvi-NYC-Pluvial",rasterKind:"prithvi",headline:"0.3% flooded",subhead:"no flooding apparent · scene 2026-05-02",sub:"Model interpretation of imagery, not real-time observation. Confidence-mean 0.84 across non-flooded pixels.",docId:"PRITHVI-NYC-PLUV-V2-20260502",citeId:"c-prithvi",illustrative:!0,mapLayer:"prithvi-pluvial"},{id:"fc-terramind-lulc",stone:"touchstone",tier:"synthetic",variant:"lulc",source:"TerraMind v1.2",agency:"IBM TerraMind v1.2 · Sentinel-2 inputs",vintage:"Sentinel-2 · 2024-09-18",title:"Land use / land cover · TerraMind v1.2",rasterKind:"lulc",classMix:[{k:"urban",pct:62,color:"#C66"},{k:"water",pct:18,color:"#5B7FB4"},{k:"vegetation",pct:12,color:"#5B8A4A"},{k:"barren",pct:6,color:"#A89A78"},{k:"wetland",pct:2,color:"#D9C75A"}],sub:"Synthetic prior. LULC palette is a layer convention, not a tier signal.",docId:"TERRAMIND-LULC-20240918",citeId:"c-tm-lulc",illustrative:!0,mapLayer:"terramind-lulc"},{id:"fc-nws",stone:"touchstone",tier:"empirical",variant:"scalars",source:"NWS KNYC",agency:"NOAA · National Weather Service",vintage:"2026-05-05",title:"Current weather, station KNYC",scalars:[{value:"0.02 in",label:"precip · last 24h"},{value:"67°F",label:"temp · current"},{value:"PC",label:"conditions"}],sub:"Observation timestamp 2026-05-05 14:18 ET. Central Park station; not point-of-query.",docId:"NWS-KNYC",citeId:"c-nws",mapLayer:"nws"},{id:"fc-ttm-surge",stone:"lodestone",tier:"modeled",variant:"timeseries",source:"Granite TTM r2 (zero-shot)",agency:"IBM Granite-TimeSeries · regional",vintage:"2026-05-05 12:00 ET",title:"Storm surge nowcast at The Battery — 9.6 h horizon (regional)",timeseries:{hours:96,peak:{x:38,y:47},peakLabel:"+47 cm @ +38h"},headline:"+47 cm",subhead:"peak surge residual · 9.6h horizon · 6-min cadence",sub:"Regional disclosure. Nowcast applies city-wide via NOAA station 8518750. Distinct from the fine-tuned Battery surge nowcast.",spatialNote:"regional · The Battery, not point-of-query",docId:"ttm_battery_surge_zeroshot",citeId:"c-ttm",mapLayer:null},{id:"fc-ttm-surge-ft",stone:"lodestone",tier:"modeled",variant:"timeseries-ft",source:"msradam/Granite-TTM-r2-Battery-Surge",agency:"Granite TTM r2 · NYC-specialized fine-tune",vintage:"2026-05-05 12:00 ET",title:"Storm surge nowcast at The Battery — 96 h horizon (NYC-specialized fine-tune)",timeseries:{hours:96,peak:{x:38,y:53},peakLabel:"+53 cm @ +38h"},headline:"+53 cm",subhead:"peak surge · 96h horizon · hourly cadence",sub:"Fine-tuned on NYC tide-gauge history. Trained on AMD MI300X.",spatialNote:"regional · The Battery, not point-of-query",docId:"ttm_battery_surge_finetune",citeId:"c-ttm-ft",mapLayer:null,hfModelCard:"huggingface.co/msradam/Granite-TTM-r2-Battery-Surge",rmse:"0.157 m",skillVsPersistence:"−35% vs persistence",hardwareBadge:"MI300X"},{id:"fc-npcc4",stone:"lodestone",tier:"modeled",variant:"forecast",source:"NPCC4",agency:"NYC Panel on Climate Change, 4th Assessment",vintage:"2024-03",title:"Sea-level rise projections, Lower NY Harbor",forecast:[{year:2030,low:4,mid:6,high:9},{year:2050,low:13,mid:22,high:30},{year:2080,low:28,mid:49,high:75},{year:2100,low:38,mid:71,high:114}],sub:"inches MSL · 17th–83rd %ile range, median line. Battery tide-gauge baseline.",docId:"NPCC4-Ch3-Tbl3.2",citeId:"c6",mapLayer:null},{id:"fc-mellea-meta",stone:"capstone",tier:"modeled",variant:"meta",source:"Mellea",agency:"Capstone synthesis · grounding check",vintage:"2026-05-05 14:22 ET",title:"Briefing reconciliation",metaRows:[{k:"mellea reroll",v:"1 reroll"},{k:"grounding checks",v:"4/4 passed"},{k:"citations resolved",v:"4"},{k:"wall-clock",v:"24.0 s"}],sub:"Capstone produces prose, not cards. This meta-card summarizes the reconciler chain that wrote the four-section briefing above.",docId:"RIPRAP-CAP-RH80",citeId:null,mapLayer:null}],stones:[{key:"cornerstone",members:[{id:"CORN-001",name:"sandy_inundation",status:"fired",tier:"empirical",ms:412},{id:"CORN-002",name:"dep_stormwater",status:"fired",tier:"modeled",ms:540},{id:"CORN-003",name:"ida_hwm_2021",status:"fired",tier:"empirical",ms:612},{id:"CORN-004",name:"prithvi_eo_v2",status:"fired",tier:"modeled",ms:980},{id:"CORN-005",name:"microtopo_lidar",status:"fired",tier:"proxy",ms:1240}]},{key:"keystone",members:[{id:"KEY-001",name:"mta_entrance_exposure",status:"silent_by_design",tier:"empirical",ms:30,note:"no entrances within radius"},{id:"KEY-002",name:"nycha_development_exposure",status:"silent_by_design",tier:"empirical",ms:28,note:"no NYCHA developments within 1.0 mi"},{id:"KEY-003",name:"doe_school_exposure",status:"silent_by_design",tier:"empirical",ms:24,note:"no DOE schools within 1.0 mi"},{id:"KEY-004",name:"doh_hospital_exposure",status:"silent_by_design",tier:"empirical",ms:22,note:"no acute-care hospitals within 1.0 mi"}]},{key:"touchstone",members:[{id:"TCH-001",name:"floodnet",status:"fired",tier:"empirical",ms:285},{id:"TCH-002",name:"nyc311",status:"fired",tier:"proxy",ms:410},{id:"TCH-003",name:"nws_obs",status:"fired",tier:"empirical",ms:240},{id:"TCH-004",name:"noaa_tides",status:"fired",tier:"empirical",ms:196},{id:"TCH-005",name:"prithvi_eo_live",status:"fired",tier:"modeled",ms:4920},{id:"TCH-006",name:"terramind_lulc",status:"fired",tier:"synthetic",ms:2100}]},{key:"lodestone",members:[{id:"LOD-001",name:"nws_alerts",status:"fired",tier:"modeled",ms:110},{id:"LOD-002",name:"ttm_forecast",status:"fired",tier:"modeled",ms:1500},{id:"LOD-003",name:"ttm_battery_surge",status:"fired",tier:"modeled",ms:1480},{id:"LOD-004",name:"floodnet_forecast",status:"silent_by_design",tier:"modeled",ms:14,note:"sensor has only 2 historical events; 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+import{d as z,e as V,a as U,f as q,s as j}from"../chunks/CWw6qgC_.js";import{p as Z,am as X,a5 as d,a4 as l,a9 as Q,ai as $,t as J,a as ee,s as r,c as e,o as i,r as t,al as te}from"../chunks/BTUA7_xE.js";import{B as ie}from"../chunks/BatqQaKj.js";import{f as re,R as ae,M as ne,C as se,F as oe}from"../chunks/zVqBGhq2.js";import{p as de,b as M}from"../chunks/DxQlA7U2.js";const le=!0,fe=Object.freeze(Object.defineProperty({__proto__:null,prerender:le},Symbol.toStringTag,{value:"Module"})),R="80 Pioneer Street · Red Hook · Brooklyn",y={c1:{id:"c1",n:1,tier:"empirical",source:"USGS",title:"Hurricane Sandy storm tide elevations, NY-NJ Harbor",docId:"USGS-OFR-2013-1234",url:"https://pubs.usgs.gov/of/2013/1234/",vintage:"2013-05",retrieved:"2026-04-28"},c2:{id:"c2",n:2,tier:"empirical",source:"NYC OEM",title:"Hurricane Sandy Inundation Zone (2012)",docId:"NYCOEM-SIZ-2013",url:"https://data.cityofnewyork.us/dataset/sandy-inundation-zone",vintage:"2013-01",retrieved:"2026-04-28"},c3:{id:"c3",n:3,tier:"empirical",source:"FloodNet NYC",title:"Sensor BK-RH-002 — Coffey Park, monthly exceedance",docId:"FN-BK-RH-002",url:"https://floodnet.nyc/sensor/BK-RH-002",vintage:"2026-04",retrieved:"2026-05-02"},c4:{id:"c4",n:4,tier:"modeled",source:"FEMA",title:"Preliminary Flood Insurance Rate Map, panel 36047C0207G",docId:"FEMA-FIRM-36047C0207G",url:"https://msc.fema.gov/portal/search",vintage:"2024-09",retrieved:"2026-04-28"},c5:{id:"c5",n:5,tier:"modeled",source:"NYC DEP",title:"Stormwater Flood Map — Moderate Stormwater Scenario",docId:"NYCDEP-SWFM-2024",url:"https://nyc.gov/stormwater-map",vintage:"2024-06",retrieved:"2026-04-28"},c6:{id:"c6",n:6,tier:"modeled",source:"NPCC4",title:"Sea-level rise projections, 2050 90th percentile",docId:"NPCC4-Ch3-Tbl3.2",url:"https://nyas.org/npcc4",vintage:"2024-03",retrieved:"2026-04-28"},c7:{id:"c7",n:7,tier:"proxy",source:"NYC 311",title:"Flooding service requests, BK CB6 2019–2025",docId:"NYC311-FLD-CB6",url:"https://data.cityofnewyork.us/311",vintage:"2025-12",retrieved:"2026-05-01"},c8:{id:"c8",n:8,tier:"proxy",source:"FEMA NFIP",title:"National Flood Insurance Program claims, tract 36047008500",docId:"NFIP-T36047008500",url:"https://www.fema.gov/openfema",vintage:"2024-12",retrieved:"2026-04-28"},c9:{id:"c9",n:9,tier:"synthetic",source:"TerraMind v1.2",title:"Synthetic SAR backscatter for 2025-09-14 (Sentinel-1 cloud-occluded)",docId:"RIPRAP-SYN-20250914",url:"#methodology-synthetic",vintage:"2025-09",retrieved:"2026-05-02"},c10:{id:"c10",n:10,tier:"modeled",source:"NYC DCP",title:"Waterfront Revitalization Program — Coastal Risk Area",docId:"NYCDCP-WRP-2022",url:"https://nyc.gov/dcp/wrp",vintage:"2022-11",retrieved:"2026-04-28"}},x=[{kind:"status",html:'<p class="briefing-deck"><strong>80 Pioneer Street, Red Hook, Brooklyn 11231.</strong> Block 597, Lot 30. Industrial Business Zone (IBZ-RH). Queried 2026-05-02 14:22 ET. <span class="briefing-meta">Briefing v0.4.2 · 9 specialists fired · 1 silent (TidalGauge: out of range)</span></p>'},{kind:"head",n:"01",label:"Status",title:"Coastal-edge, post-Sandy, multi-hazard"},{kind:"prose",parts:[{tier:"empirical",text:"The address sits 380 ft inland of the Erie Basin bulkhead, at a ground elevation of 6.2 ft NAVD88",cite:"c1"},{text:" — within the "},{tier:"empirical",text:"2012 Sandy Inundation Zone, which recorded a peak storm tide of 11.4 ft NAVD88 at the Battery",cite:"c2"},{text:" 2.4 mi to the northwest. "},{tier:"modeled",text:"FEMA's preliminary FIRM places the parcel in Zone AE (BFE 11 ft NAVD88)",cite:"c4"},{text:", a 4.8 ft freeboard above current grade. The site is upgradient of two FloodNet sensors and three blocks from a chronic 311 cluster."}]},{kind:"head",n:"02",label:"Empirical evidence",tier:"empirical"},{kind:"prose",parts:[{tier:"empirical",text:"FloodNet sensor BK-RH-002 (Coffey Park, 1,200 ft south) recorded 7 above-curb events between 2024-06 and 2026-04",cite:"c3"},{text:", with a peak depth of 14.3 cm during the 2025-09-29 nor'easter. "},{tier:"empirical",text:"USGS post-Sandy high-water marks within 500 ft cluster between 6.8 and 8.1 ft NAVD88",cite:"c1"},{text:", consistent with 0.6–1.9 ft of standing water at the queried address during the storm."}]},{kind:"head",n:"03",label:"Modeled scenarios",tier:"modeled"},{kind:"prose",parts:[{tier:"modeled",text:"DEP's Moderate Stormwater Scenario (2.13 in/hr design storm) shows ponding ≥4 in across the western half of the lot",cite:"c5"},{text:", routed by the 1.2% slope toward Imlay St. "},{tier:"modeled",text:"Under NPCC4's 2050 90th-percentile sea-level rise (30 in)",cite:"c6"},{text:", the parcel falls within the projected daily-tidal floodplain by mid-century. "},{tier:"synthetic",text:"Synthetic SAR backscatter for 2025-09-14 (Sentinel-1 cloud-occluded) was generated by TerraMind v1.2 and is presented as a prior, not an observation",cite:"c9"},{text:"; treat with appropriate caution."}]},{kind:"head",n:"04",label:"Policy context"},{kind:"prose",parts:[{tier:"proxy",text:"311 flood complaints within the surrounding census tract total 89 calls over 2019–2025, with seasonal clustering in Aug–Oct",cite:"c7"},{text:". "},{tier:"proxy",text:"NFIP claims aggregated to tract 36047008500 total $4.1M across 47 paid losses since 2000",cite:"c8"},{text:". "},{tier:"modeled",text:"The site lies within the NYC Waterfront Revitalization Program Coastal Risk Area; CEQR §817 review applies to any discretionary action",cite:"c10"},{text:"."}]}],ce={wallSeconds:14,cacheHit:.92,cards:[{id:"fc-fema",stone:"cornerstone",tier:"modeled",variant:"headline",source:"FEMA",agency:"Federal Emergency Management Agency",vintage:"2024-09",title:"Preliminary FIRM, panel 36047C0207G",headline:"Zone AE",subhead:"BFE 11 ft NAVD88 · freeboard +4.8 ft",body:"Address sits within the regulatory 1% annual-chance floodplain. Base Flood Elevation 11.0 ft NAVD88; first floor must be at or above this datum for NFIP rating.",docId:"FEMA-FIRM-36047C0207G",citeId:"c4",mapLayer:"fema-ae"},{id:"fc-hwm",stone:"cornerstone",tier:"empirical",variant:"tabular",source:"USGS",agency:"U.S. Geological Survey",vintage:"2013-05",title:"Post-Sandy high-water marks within 500 ft",columns:["id","elev.","dist."],rows:[["HWM-NY-3081","7.4 ft NAVD88","0.18 mi"],["HWM-NY-3082","8.1 ft NAVD88","0.22 mi"],["HWM-NY-3105","6.8 ft NAVD88","0.31 mi"]],sub:"3 marks · max 8.1 ft · surveyed Nov 2012",docId:"USGS-OFR-2013-1234",citeId:"c1",mapLayer:"hwm"},{id:"fc-stormwater",stone:"cornerstone",tier:"modeled",variant:"raster",source:"NYC DEP",agency:"NYC Dept. of Environmental Protection",vintage:"2024-06",title:"Stormwater Flood Map · moderate scenario",rasterKind:"stormwater",sub:"2.13 in/hr · ponding ≥4 in W half of lot · routed toward Imlay St",docId:"NYCDEP-SWFM-2024",citeId:"c5",mapLayer:"stormwater"},{id:"fc-register-rh",stone:"keystone",tier:"empirical",variant:"register",source:"NYC OpenData",agency:"NYC OpenData · multi-agency join",vintage:"2026-05",title:"Nearby exposed assets",registers:[{reg:"MTA",tier:"empirical",label:"Smith–9 St subway entrance",detail:"0.34 mi · F · G",sourceId:"MTA-ENT-N048",vintage:"2025-11",note:null},{reg:"NYCHA",tier:"empirical",label:"Red Hook East Houses",detail:"0.41 mi · 2,878 res.",sourceId:"NYCHA-RHE",vintage:"2025-Q3",note:null},{reg:"NYCHA",tier:"empirical",label:"Red Hook West Houses",detail:"0.52 mi · 3,142 res.",sourceId:"NYCHA-RHW",vintage:"2025-Q3",note:null},{reg:"DOE",tier:"empirical",label:"PS 27 Agnes Y. Humphrey",detail:"0.29 mi · 271 K-5",sourceId:"DOE-K027",vintage:"2024-25",note:null},{reg:"DOH",tier:"empirical",label:null,detail:null,sourceId:null,vintage:null,note:"no acute-care hospital within 1.0 mi (silent)"},{reg:"PLUTO",tier:"empirical",label:"Lot 36047 / 521 / 7",detail:"BIN 3018472 · MX-1",sourceId:"PLUTO-2024v2",vintage:"2024-12",note:null}],sub:"5 of 6 registers fired · 1 silent · joined within 1.0 mi",docId:"RIPRAP-EXP-RH80",citeId:"c-reg-rh",mapLayer:"registers"},{id:"fc-floodnet",stone:"touchstone",tier:"empirical",variant:"spark",source:"FloodNet",agency:"FloodNet NYC sensor network",vintage:"2026-04",title:"Sensor BK-RH-002, monthly above-curb events",headline:"7 events",subhead:"Jun 2024 → Apr 2026 · peak 14.3 cm",spark:[0,0,1,0,2,1,0,0,3,0,1,0,0,0,2,1,0,0,1,0,2,4,1,1],sparkSub:"Sensor located 0.21 mi N at Coffey & Van Brunt. Above-curb depth in cm; events ≥2 cm.",docId:"FN-BK-RH-002",citeId:"c3",mapLayer:"floodnet"},{id:"fc-311",stone:"touchstone",tier:"proxy",variant:"histogram",source:"NYC 311",agency:"NYC 311 service requests",vintage:"2025-12",title:"Recent 311 flood complaints, BK CB6",headline:"89 calls",subhead:"2019–2025 · seasonal cluster Aug–Oct",histogram:[3,2,1,0,1,4,7,12,18,11,5,3,4,2,1,0,2,3,8,9,4,2,1,0],sparkSub:"Filtered to complaint types: Sewer (Backup), Street Flooding, Catch Basin Clogged. Within 200 m of address.",docId:"NYC311-FLD-CB6",citeId:"c7",mapLayer:"complaints"},{id:"fc-prithvi-pluvial",stone:"touchstone",tier:"modeled",variant:"raster-pred",source:"Prithvi-NYC-Pluvial",agency:"NASA-IBM Prithvi v2 · NYC fine-tune",vintage:"2026-05-02 · Sentinel-2",title:"Pluvial flood prediction · Prithvi-NYC-Pluvial",rasterKind:"prithvi",headline:"0.3% flooded",subhead:"no flooding apparent · scene 2026-05-02",sub:"Model interpretation of imagery, not real-time observation. Confidence-mean 0.84 across non-flooded pixels.",docId:"PRITHVI-NYC-PLUV-V2-20260502",citeId:"c-prithvi",illustrative:!0,mapLayer:"prithvi-pluvial"},{id:"fc-terramind-lulc",stone:"touchstone",tier:"synthetic",variant:"lulc",source:"TerraMind v1.2",agency:"IBM TerraMind v1.2 · Sentinel-2 inputs",vintage:"Sentinel-2 · 2024-09-18",title:"Land use / land cover · TerraMind v1.2",rasterKind:"lulc",classMix:[{k:"urban",pct:62,color:"#C66"},{k:"water",pct:18,color:"#5B7FB4"},{k:"vegetation",pct:12,color:"#5B8A4A"},{k:"barren",pct:6,color:"#A89A78"},{k:"wetland",pct:2,color:"#D9C75A"}],sub:"Synthetic prior. LULC palette is a layer convention, not a tier signal.",docId:"TERRAMIND-LULC-20240918",citeId:"c-tm-lulc",illustrative:!0,mapLayer:"terramind-lulc"},{id:"fc-nws",stone:"touchstone",tier:"empirical",variant:"scalars",source:"NWS KNYC",agency:"NOAA · National Weather Service",vintage:"2026-05-05",title:"Current weather, station KNYC",scalars:[{value:"0.02 in",label:"precip · last 24h"},{value:"67°F",label:"temp · current"},{value:"PC",label:"conditions"}],sub:"Observation timestamp 2026-05-05 14:18 ET. Central Park station; not point-of-query.",docId:"NWS-KNYC",citeId:"c-nws",mapLayer:"nws"},{id:"fc-ttm-surge",stone:"lodestone",tier:"modeled",variant:"timeseries",source:"Granite TTM r2 (zero-shot)",agency:"IBM Granite-TimeSeries · regional",vintage:"2026-05-05 12:00 ET",title:"Storm surge nowcast at The Battery — 9.6 h horizon (regional)",timeseries:{hours:96,peak:{x:38,y:47},peakLabel:"+47 cm @ +38h"},headline:"+47 cm",subhead:"peak surge residual · 9.6h horizon · 6-min cadence",sub:"Regional disclosure. Nowcast applies city-wide via NOAA station 8518750. Distinct from the fine-tuned Battery surge nowcast.",spatialNote:"regional · The Battery, not point-of-query",docId:"ttm_battery_surge_zeroshot",citeId:"c-ttm",mapLayer:null},{id:"fc-ttm-surge-ft",stone:"lodestone",tier:"modeled",variant:"timeseries-ft",source:"msradam/Granite-TTM-r2-Battery-Surge",agency:"Granite TTM r2 · NYC-specialized fine-tune",vintage:"2026-05-05 12:00 ET",title:"Storm surge nowcast at The Battery — 96 h horizon (NYC-specialized fine-tune)",timeseries:{hours:96,peak:{x:38,y:53},peakLabel:"+53 cm @ +38h"},headline:"+53 cm",subhead:"peak surge · 96h horizon · hourly cadence",sub:"Fine-tuned on NYC tide-gauge history. Trained on AMD MI300X.",spatialNote:"regional · The Battery, not point-of-query",docId:"ttm_battery_surge_finetune",citeId:"c-ttm-ft",mapLayer:null,hfModelCard:"huggingface.co/msradam/Granite-TTM-r2-Battery-Surge",rmse:"0.157 m",skillVsPersistence:"−35% vs persistence",hardwareBadge:"MI300X"},{id:"fc-npcc4",stone:"lodestone",tier:"modeled",variant:"forecast",source:"NPCC4",agency:"NYC Panel on Climate Change, 4th Assessment",vintage:"2024-03",title:"Sea-level rise projections, Lower NY Harbor",forecast:[{year:2030,low:4,mid:6,high:9},{year:2050,low:13,mid:22,high:30},{year:2080,low:28,mid:49,high:75},{year:2100,low:38,mid:71,high:114}],sub:"inches MSL · 17th–83rd %ile range, median line. Battery tide-gauge baseline.",docId:"NPCC4-Ch3-Tbl3.2",citeId:"c6",mapLayer:null},{id:"fc-mellea-meta",stone:"capstone",tier:"modeled",variant:"meta",source:"Mellea",agency:"Capstone synthesis · grounding check",vintage:"2026-05-05 14:22 ET",title:"Briefing reconciliation",metaRows:[{k:"mellea reroll",v:"1 reroll"},{k:"grounding checks",v:"4/4 passed"},{k:"citations resolved",v:"4"},{k:"wall-clock",v:"24.0 s"}],sub:"Capstone produces prose, not cards. This meta-card summarizes the reconciler chain that wrote the four-section briefing above.",docId:"RIPRAP-CAP-RH80",citeId:null,mapLayer:null}],stones:[{key:"cornerstone",members:[{id:"CORN-001",name:"sandy_inundation",status:"fired",tier:"empirical",ms:412},{id:"CORN-002",name:"dep_stormwater",status:"fired",tier:"modeled",ms:540},{id:"CORN-003",name:"ida_hwm_2021",status:"fired",tier:"empirical",ms:612},{id:"CORN-004",name:"prithvi_eo_v2",status:"fired",tier:"modeled",ms:980},{id:"CORN-005",name:"microtopo_lidar",status:"fired",tier:"proxy",ms:1240}]},{key:"keystone",members:[{id:"KEY-001",name:"mta_entrance_exposure",status:"silent_by_design",tier:"empirical",ms:30,note:"no entrances within radius"},{id:"KEY-002",name:"nycha_development_exposure",status:"silent_by_design",tier:"empirical",ms:28,note:"no NYCHA developments within 1.0 mi"},{id:"KEY-003",name:"doe_school_exposure",status:"silent_by_design",tier:"empirical",ms:24,note:"no DOE schools within 1.0 mi"},{id:"KEY-004",name:"doh_hospital_exposure",status:"silent_by_design",tier:"empirical",ms:22,note:"no acute-care hospitals within 1.0 mi"}]},{key:"touchstone",members:[{id:"TCH-001",name:"floodnet",status:"fired",tier:"empirical",ms:285},{id:"TCH-002",name:"nyc311",status:"fired",tier:"proxy",ms:410},{id:"TCH-003",name:"nws_obs",status:"fired",tier:"empirical",ms:240},{id:"TCH-004",name:"noaa_tides",status:"fired",tier:"empirical",ms:196},{id:"TCH-005",name:"prithvi_eo_live",status:"fired",tier:"modeled",ms:4920},{id:"TCH-006",name:"terramind_lulc",status:"fired",tier:"synthetic",ms:2100}]},{key:"lodestone",members:[{id:"LOD-001",name:"nws_alerts",status:"fired",tier:"modeled",ms:110},{id:"LOD-002",name:"ttm_forecast",status:"fired",tier:"modeled",ms:1500},{id:"LOD-003",name:"ttm_battery_surge",status:"fired",tier:"modeled",ms:1480},{id:"LOD-004",name:"floodnet_forecast",status:"silent_by_design",tier:"modeled",ms:14,note:"sensor has only 2 historical events; forecast omitted (silent-floor: 5)"},{id:"LOD-005",name:"ttm_311_forecast",status:"errored",tier:"modeled",ms:0,note:"311 history fetch failed: HTTP 503 at NYC OpenData (3 retries)"}]},{key:"capstone",members:[{id:"CAP-001",name:"rag_granite_embedding",status:"fired",tier:"proxy",ms:410},{id:"CAP-002",name:"gliner_extract",status:"fired",tier:"proxy",ms:280},{id:"CAP-003",name:"reconcile_granite41",status:"fired",tier:"modeled",ms:6240}]}].map(a=>({key:a.key,members:re(a.key,a.members)}))};var me=q('<section class="hero-band"><div class="hero-band-inner"><div class="app-shell-top is-desktop"><main id="region-briefing" class="app-region app-region-brief" aria-labelledby="brief-h1"><header class="region-head"><span class="section-label">Briefing</span> <button type="button" class="region-action" aria-label="Replay streaming">↻ replay stream</button></header> <h1 id="brief-h1" class="brief-h1">Flood-exposure briefing <span class="brief-h1-addr"> </span></h1> <!></main> <div class="app-region-side" style="grid-area: side;"><aside id="region-map" class="app-region app-region-map" aria-label="Map region"><header class="region-head"><span class="section-label">Map</span> <span class="region-head-meta">Carto Positron · z15 · 40.6776°N 74.0096°W</span></header> <div style="position: relative; flex: 1; min-height: 0;"><!> <!></div></aside> <aside id="region-cites" class="app-region app-region-cites" aria-label="Citations"><!></aside></div></div> <div class="app-shell-bottom"><section class="app-region app-region-findings" aria-label="Findings"><!></section></div></div></section>');function ye(a,T){Z(T,!0);let c=l(null),F="comfortable",B="smart",b=l(!1);X(()=>{typeof window<"u"&&d(b,new URL(window.location.href).searchParams.get("grammar")==="1")});function Y(o){d(c,o,!0)}function E(o){const s=document.getElementById("region-cites");s&&s.scrollIntoView({behavior:"smooth",block:"start"})}let n=l(Q({empirical:!0,modeled:!0,synthetic:!0,proxy:!0})),m=l(0);$(()=>(de({queryId:"sample",queryText:R,intent:"single_address",specialists:9,blocks:x,citations:y,generatedAt:new Date().toISOString(),attempts:1}),M.markReady(),()=>M.reset()));const L={label:"80 Pioneer St",lat:40.6776,lon:-74.0096},C={type:"FeatureCollection",features:[{type:"Feature",properties:{source:"sample-fixture",tier:"synthetic"},geometry:{type:"Polygon",coordinates:[[[-74.014,40.679],[-74.007,40.68],[-74.005,40.677],[-74.009,40.6755],[-74.014,40.679]]]}}]};var u=me(),N=e(u),p=e(N),h=e(p),g=e(h),D=r(e(g),2);t(g);var v=r(g,2),w=r(e(v)),H=e(w,!0);t(w),t(v);var O=r(v,2);ie(O,{get blocks(){return x},get citations(){return y},streaming:!0,get replayKey(){return i(m)}}),t(h);var I=r(h,2),f=e(I),S=r(e(f),2),k=e(S);ae(k,{get address(){return L},get activeLayers(){return i(n)},get syntheticPrior(){return C},get linkedKey(){return i(c)}});var K=r(k,2);{let o=te(()=>({empirical:0,modeled:0,synthetic:C.features.length,proxy:0}));ne(K,{get active(){return i(n)},get featureCounts(){return i(o)},onToggle:s=>d(n,{...i(n),[s]:!i(n)[s]},!0)})}t(S),t(f);var _=r(f,2),G=e(_);se(G,{get citations(){return y}}),t(_),t(I),t(p);var P=r(p,2),A=e(P),W=e(A);oe(W,{get data(){return ce},density:F,provenanceMode:B,get showGrammar(){return i(b)},get linkedKey(){return i(c)},onLink:Y,onCite:E}),t(A),t(P),t(N),t(u),J(()=>j(H,R)),V("click",D,()=>d(m,i(m)+1)),U(a,u),ee()}z(["click"]);export{ye as component,fe as universal};

web/sveltekit/build/_app/version.json

@@ -1 +1 @@
-{"version":"1778247349864"}
+{"version":"1778255822052"}

web/sveltekit/build/index.html

@@ -6,21 +6,21 @@
     <meta name="viewport" content="width=device-width, initial-scale=1" />
     <meta name="description" content="Riprap — citation-grounded NYC flood-exposure briefings." />
     <title>Riprap — flood-exposure briefing</title>
-    <link href="./_app/immutable/entry/start.Ct-7wur6.js" rel="modulepreload">
-		<link href="./_app/immutable/chunks/5yhzCbvM.js" rel="modulepreload">
+    <link href="./_app/immutable/entry/start.FA4XFe2L.js" rel="modulepreload">
+		<link href="./_app/immutable/chunks/BIe30OUb.js" rel="modulepreload">
 		<link href="./_app/immutable/chunks/BTUA7_xE.js" rel="modulepreload">
-		<link href="./_app/immutable/entry/app.CnUxdbZ2.js" rel="modulepreload">
+		<link href="./_app/immutable/entry/app.DBibj1kM.js" rel="modulepreload">
 		<link href="./_app/immutable/chunks/CXQd8Y6F.js" rel="modulepreload">
 		<link href="./_app/immutable/chunks/CWw6qgC_.js" rel="modulepreload">
 		<link href="./_app/immutable/chunks/Bd-v_9Ud.js" rel="modulepreload">
 		<link href="./_app/immutable/chunks/CW0zSL4D.js" rel="modulepreload">
-		<link href="./_app/immutable/nodes/0.CVp297sI.js" rel="modulepreload">
+		<link href="./_app/immutable/nodes/0.BaDkkERB.js" rel="modulepreload">
 		<link href="./_app/immutable/chunks/DxQlA7U2.js" rel="modulepreload">
-		<link href="./_app/immutable/chunks/Bsd24zeB.js" rel="modulepreload">
+		<link href="./_app/immutable/chunks/DlifwAOf.js" rel="modulepreload">
 		<link href="./_app/immutable/chunks/DCD6_LXk.js" rel="modulepreload">
 		<link href="./_app/immutable/chunks/B0XoTt7U.js" rel="modulepreload">
 		<link href="./_app/immutable/chunks/DixtWtwq.js" rel="modulepreload">
-		<link href="./_app/immutable/nodes/2.DFf9SCs7.js" rel="modulepreload">
+		<link href="./_app/immutable/nodes/2.Bas78CjE.js" rel="modulepreload">
 		<link href="./_app/immutable/chunks/cDW0xQNP.js" rel="modulepreload">
 		<link href="./_app/immutable/chunks/25_y8TFd.js" rel="modulepreload">
 		<link href="./_app/immutable/chunks/D907np-5.js" rel="modulepreload">
@@ -37,15 +37,15 @@
 			
 			<script>
 				{
-					__sveltekit_1fdrmyh = {
+					__sveltekit_2hdpwx = {
 						base: new URL(".", location).pathname.slice(0, -1)
 					};
 
 					const element = document.currentScript.parentElement;
 
 					Promise.all([
-						import("./_app/immutable/entry/start.Ct-7wur6.js"),
-						import("./_app/immutable/entry/app.CnUxdbZ2.js")
+						import("./_app/immutable/entry/start.FA4XFe2L.js"),
+						import("./_app/immutable/entry/app.DBibj1kM.js")
 					]).then(([kit, app]) => {
 						kit.start(app, element, {
 							node_ids: [0, 2],

web/sveltekit/build/q/sample.html

@@ -6,24 +6,24 @@
     <meta name="viewport" content="width=device-width, initial-scale=1" />
     <meta name="description" content="Riprap — citation-grounded NYC flood-exposure briefings." />
     <title>Riprap — flood-exposure briefing</title>
-    <link href="../_app/immutable/entry/start.Ct-7wur6.js" rel="modulepreload">
-		<link href="../_app/immutable/chunks/5yhzCbvM.js" rel="modulepreload">
+    <link href="../_app/immutable/entry/start.FA4XFe2L.js" rel="modulepreload">
+		<link href="../_app/immutable/chunks/BIe30OUb.js" rel="modulepreload">
 		<link href="../_app/immutable/chunks/BTUA7_xE.js" rel="modulepreload">
-		<link href="../_app/immutable/entry/app.CnUxdbZ2.js" rel="modulepreload">
+		<link href="../_app/immutable/entry/app.DBibj1kM.js" rel="modulepreload">
 		<link href="../_app/immutable/chunks/CXQd8Y6F.js" rel="modulepreload">
 		<link href="../_app/immutable/chunks/CWw6qgC_.js" rel="modulepreload">
 		<link href="../_app/immutable/chunks/Bd-v_9Ud.js" rel="modulepreload">
 		<link href="../_app/immutable/chunks/CW0zSL4D.js" rel="modulepreload">
-		<link href="../_app/immutable/nodes/0.CVp297sI.js" rel="modulepreload">
+		<link href="../_app/immutable/nodes/0.BaDkkERB.js" rel="modulepreload">
 		<link href="../_app/immutable/chunks/DxQlA7U2.js" rel="modulepreload">
-		<link href="../_app/immutable/chunks/Bsd24zeB.js" rel="modulepreload">
+		<link href="../_app/immutable/chunks/DlifwAOf.js" rel="modulepreload">
 		<link href="../_app/immutable/chunks/DCD6_LXk.js" rel="modulepreload">
 		<link href="../_app/immutable/chunks/B0XoTt7U.js" rel="modulepreload">
 		<link href="../_app/immutable/chunks/DixtWtwq.js" rel="modulepreload">
-		<link href="../_app/immutable/nodes/5.CuscLKzC.js" rel="modulepreload">
+		<link href="../_app/immutable/nodes/5.NbriEyGS.js" rel="modulepreload">
 		<link href="../_app/immutable/chunks/BatqQaKj.js" rel="modulepreload">
 		<link href="../_app/immutable/chunks/25_y8TFd.js" rel="modulepreload">
-		<link href="../_app/immutable/chunks/C3emz_VZ.js" rel="modulepreload">
+		<link href="../_app/immutable/chunks/zVqBGhq2.js" rel="modulepreload">
 		<link href="../_app/immutable/chunks/D907np-5.js" rel="modulepreload">
 		
 		<link href="../_app/immutable/assets/RipMark.ClxF_PAC.css" rel="stylesheet">
@@ -38,15 +38,15 @@
 			
 			<script>
 				{
-					__sveltekit_1fdrmyh = {
+					__sveltekit_2hdpwx = {
 						base: new URL("..", location).pathname.slice(0, -1)
 					};
 
 					const element = document.currentScript.parentElement;
 
 					Promise.all([
-						import("../_app/immutable/entry/start.Ct-7wur6.js"),
-						import("../_app/immutable/entry/app.CnUxdbZ2.js")
+						import("../_app/immutable/entry/start.FA4XFe2L.js"),
+						import("../_app/immutable/entry/app.DBibj1kM.js")
 					]).then(([kit, app]) => {
 						kit.start(app, element, {
 							node_ids: [0, 5],

web/sveltekit/src/lib/components/map/RipMap.svelte

@@ -27,10 +27,21 @@
      *  not gated by `activeLayers`. */
     registerPoints?: GeoJSON.FeatureCollection;
     registerPolygons?: GeoJSON.FeatureCollection;
-    /** TerraMind-synthesis LULC polygons from the SSE final payload
-     *  (terramind.polygons_geojson). Categorical fill by `fill_color`
-     *  property; synthetic tier; controlled by the SYN master toggle. */
+    /** TerraMind-NYC LULC polygons (5-class fine-tune, Sentinel-2-driven)
+     *  or, if the LoRA didn't fire, the IBM v1 base synthesis output.
+     *  Carried via `state.terramind_lulc.polygons_geojson` /
+     *  `state.terramind.polygons_geojson`. Categorical fill by per-feature
+     *  `fill_color`; synthetic tier; controlled by the SYN master toggle. */
     terramindLulc?: GeoJSON.FeatureCollection;
+    /** TerraMind-NYC Buildings polygons (binary fine-tune, Sentinel-2-
+     *  driven). Carried via `state.terramind_buildings.polygons_geojson`.
+     *  Red fill; modeled tier; controlled by SYN master toggle. */
+    terramindBuildings?: GeoJSON.FeatureCollection;
+    /** Prithvi-NYC-Pluvial water polygons — live Sentinel-2 segmentation
+     *  via the NYC fine-tune of Prithvi-EO 2.0. Carried via
+     *  `state.prithvi_live.polygons_geojson`. Blue fill; modeled tier;
+     *  controlled by MOD master toggle. */
+    prithviLive?: GeoJSON.FeatureCollection;
     /** USGS Ida 2021 high-water mark points. Empirical tier; amber fill.
      *  Controlled by EMP master toggle. */
     idaHwm?: GeoJSON.FeatureCollection;
@@ -51,6 +62,8 @@
     registerPoints,
     registerPolygons,
     terramindLulc,
+    terramindBuildings,
+    prithviLive,
     idaHwm,
     activeLayers = { empirical: true, modeled: true, synthetic: true, proxy: true },
     linkedKey = null,
@@ -81,6 +94,8 @@
   $effect(() => { setSourceData('register-points', registerPoints); });
   $effect(() => { setSourceData('register-polygons', registerPolygons); });
   $effect(() => { setSourceData('terramind-lulc', terramindLulc); });
+  $effect(() => { setSourceData('terramind-buildings', terramindBuildings); });
+  $effect(() => { setSourceData('prithvi-live', prithviLive); });
   $effect(() => { setSourceData('ida-hwm', idaHwm); });
 
   $effect(() => {
@@ -93,6 +108,10 @@
     setLayerVisibility('tier-synthetic-line', activeLayers.synthetic);
     setLayerVisibility('terramind-lulc-fill', activeLayers.synthetic);
     setLayerVisibility('terramind-lulc-line', activeLayers.synthetic);
+    setLayerVisibility('terramind-buildings-fill', activeLayers.synthetic);
+    setLayerVisibility('terramind-buildings-line', activeLayers.synthetic);
+    setLayerVisibility('prithvi-live-fill', activeLayers.modeled);
+    setLayerVisibility('prithvi-live-line', activeLayers.modeled);
     setLayerVisibility('tier-proxy-dots', activeLayers.proxy);
   });
 
@@ -135,6 +154,8 @@
       map.addSource('register-points', { type: 'geojson', data: registerPoints ?? fcEmpty() });
       map.addSource('register-polygons', { type: 'geojson', data: registerPolygons ?? fcEmpty() });
       map.addSource('terramind-lulc', { type: 'geojson', data: terramindLulc ?? fcEmpty() });
+      map.addSource('terramind-buildings', { type: 'geojson', data: terramindBuildings ?? fcEmpty() });
+      map.addSource('prithvi-live', { type: 'geojson', data: prithviLive ?? fcEmpty() });
       map.addSource('ida-hwm', { type: 'geojson', data: idaHwm ?? fcEmpty() });
       map.addSource('queried-address', {
         type: 'geojson',
@@ -206,6 +227,30 @@
         paint: { 'line-color': ['get', 'fill_color'], 'line-width': 0.75, 'line-opacity': 0.45, 'line-dasharray': [3, 2] }
       });
 
+      // TerraMind-NYC Buildings LoRA polygons. Red fill (matches the
+      // _PALETTE entry for "building" in app/context/_polygonize.py),
+      // sharper outline so individual structures read distinctly.
+      map.addLayer({
+        id: 'terramind-buildings-fill', type: 'fill', source: 'terramind-buildings',
+        paint: { 'fill-color': '#D62728', 'fill-opacity': 0.32 }
+      });
+      map.addLayer({
+        id: 'terramind-buildings-line', type: 'line', source: 'terramind-buildings',
+        paint: { 'line-color': '#A31D1F', 'line-width': 0.6, 'line-opacity': 0.7 }
+      });
+
+      // Prithvi-NYC-Pluvial water polygons (live Sentinel-2 segmentation).
+      // Blue fill, slightly higher opacity than the modeled DEP layer so
+      // the live signal reads as the dominant flood overlay.
+      map.addLayer({
+        id: 'prithvi-live-fill', type: 'fill', source: 'prithvi-live',
+        paint: { 'fill-color': '#1F77B4', 'fill-opacity': 0.42 }
+      });
+      map.addLayer({
+        id: 'prithvi-live-line', type: 'line', source: 'prithvi-live',
+        paint: { 'line-color': '#0F4F7C', 'line-width': 1.0, 'line-opacity': 0.85 }
+      });
+
       // Register-asset polygons (NYCHA developments only). Fill graded
       // by pct_inside_sandy_2012 — denser if more of the development is
       // in the 2012 zone. Outline always-on so the boundary is legible.

web/sveltekit/src/routes/q/[queryId]/+page.svelte

@@ -288,6 +288,12 @@
   let synFc = $state<FeatureCollection | undefined>(undefined);
   let proxyFc = $state<FeatureCollection | undefined>(undefined);
   let terramindLulcFc = $state<FeatureCollection | undefined>(undefined);
+  // Live EO polygon layers — populated from the specialist outputs in
+  // FSM `final` state when the remote inference path returns a
+  // polygonised raster. None of these go through a /api/layers fetch;
+  // they ride the SSE stream alongside the specialist results.
+  let prithviLiveFc = $state<FeatureCollection | undefined>(undefined);
+  let terramindBuildingsFc = $state<FeatureCollection | undefined>(undefined);
   let idaHwmFc = $state<FeatureCollection | undefined>(undefined);
 
   // Compare-intent: independent geocoded addresses per place.
@@ -598,14 +604,37 @@
         const fr = f as unknown as Record<string, unknown>;
         registerPointsFc = buildRegisterPointsFc(fr);
         registerPolygonsFc = buildRegisterPolygonsFc(fr);
-        // TerraMind-synthesis LULC polygons — already computed by the
-        // specialist and carried in terramind.polygons_geojson. Wire into
-        // the map's synthetic layer without an additional API round-trip.
-        const tm = fr.terramind as Record<string, unknown> | null | undefined;
-        if (tm?.ok && tm?.polygons_geojson) {
-          const pg = tm.polygons_geojson as FeatureCollection;
+        // EO map layers — every specialist that returns a polygon
+        // collection plumbs onto the map. terramind synthesis +
+        // terramind LULC LoRA both contribute to the synthetic-LULC
+        // overlay; LULC LoRA wins when both fire (it's the
+        // fine-tuned, Sentinel-2-driven signal). Buildings LoRA gets
+        // its own layer. Prithvi NYC Pluvial water mask is the
+        // marquee live-EO overlay.
+        const tmSyn = fr.terramind as Record<string, unknown> | null | undefined;
+        const tmLulc = fr.terramind_lulc as Record<string, unknown> | null | undefined;
+        const tmBld = fr.terramind_buildings as Record<string, unknown> | null | undefined;
+        const pl = fr.prithvi_live as Record<string, unknown> | null | undefined;
+        const lulcCandidate =
+          (tmLulc?.ok && tmLulc?.polygons_geojson)
+            ? (tmLulc.polygons_geojson as FeatureCollection)
+            : (tmSyn?.ok && tmSyn?.polygons_geojson)
+            ? (tmSyn.polygons_geojson as FeatureCollection)
+            : undefined;
+        if (lulcCandidate?.type === 'FeatureCollection'
+            && (lulcCandidate.features?.length ?? 0) > 0) {
+          terramindLulcFc = lulcCandidate;
+        }
+        if (tmBld?.ok && tmBld?.polygons_geojson) {
+          const pg = tmBld.polygons_geojson as FeatureCollection;
+          if (pg?.type === 'FeatureCollection' && (pg.features?.length ?? 0) > 0) {
+            terramindBuildingsFc = pg;
+          }
+        }
+        if (pl?.ok && pl?.polygons_geojson) {
+          const pg = pl.polygons_geojson as FeatureCollection;
           if (pg?.type === 'FeatureCollection' && (pg.features?.length ?? 0) > 0) {
-            terramindLulcFc = pg;
+            prithviLiveFc = pg;
           }
         }
         // v0.4.2 §12 grounding failure: budget exhausted with failed checks.
@@ -792,6 +821,8 @@
                 registerPoints={registerPointsFc}
                 registerPolygons={registerPolygonsFc}
                 terramindLulc={terramindLulcFc}
+                terramindBuildings={terramindBuildingsFc}
+                prithviLive={prithviLiveFc}
                 {linkedKey}
               />
               <MapLegend