# Rapid Building Assessment **Goal: go from imagery to a per-building damage map in minutes — no model training job required.** HASTE computes a compact "fingerprint" (an *embedding*) for every building footprint, you label a handful of buildings, and a fast in-browser model predicts damage for all of them. You then validate a sample and get accuracy metrics plus a damaged-building estimate. This is the recommended path when you have building footprints and want a quick answer. For a continuous, pixel-level damage raster instead, see {doc}`Damage Mapping with a Trained Model `. ```{admonition} The workflow at a glance :class: tip Building layer → **Embed** → **Interactive Label** (label a few, model predicts the rest) → **Predict all buildings** → **Validate** a sample → **Reports**. ``` ## Before you start Create a project and add an image layer with the **Building** workflow selected, then load your pre-/post-event imagery — building footprints are prepared automatically during preprocessing. See {doc}`Projects ` and {doc}`Image layers `. A building-workflow layer shows an **Embed** button once its status is **Processed**. Footprints are sourced automatically from Overture Maps, or you can supply your own — see {doc}`Custom building footprints `. ```{admonition} Try it with sample data :class: tip New to HASTE? Use the **Black River** sample from {doc}`Image layers ` (see *Sample data to try HASTE*): create a **Building**-workflow layer with `black-river_visual_mosaic_cog.tif` as the post-event imagery and `black-river_footprints.gpkg` as the custom footprints. ``` ## Step 1 — Embed the buildings On the image layer, click the **Embed** button:  In the **New Embedding** dialog, configure: - **Embedding backbone** — **MOSAIKS** (random convolutional features) or **DINOv2** (ViT-S/14 or ViT-B/14). MOSAIKS is the lightweight default. - **Output dimensions** — features per building (MOSAIKS only; default 1024). - **Resize factor** — how much to upscale the crop around each footprint (default 4 for MOSAIKS). - **Batch size** — how many buildings to process at once.  Click **Embed** to queue the job. It analyzes the imagery around every footprint, producing a feature vector per building plus vector tiles (PMTiles) for fast map display. When it finishes, an **embedding row** appears with an **Interactive Label** button.  ```{tip} An *embedding* is a compact set of numbers capturing what each building looks like in the imagery. Similar-looking buildings get similar embeddings — which is what lets a small model generalize your labels to every building. ``` ## Step 2 — Label interactively Click **Interactive Label** to open the map-based labeler: satellite imagery (with a pre-/post-event toggle), building footprints (visible at zoom 15 and closer), and a side panel with the class selector, counts, a view toggle, and quality metrics.  - **Left-click** a building to label it with the selected class; **right-click** to remove a label. - **Ctrl+drag** (Cmd+drag on macOS) to box-select and label many buildings at once. - **Classes:** **Intact** (green), **Damaged** (red), **Cloudy** (purple) for obscured buildings; unlabeled buildings are gray. - **Shortcuts:** `1`/`2`/`3` pick a class, `T` cycles, `P` toggles Labeled/Predicted view, `Space` shows/hides footprints. Once you've labeled at least **3 buildings across 2+ classes**, an in-browser model (logistic regression, WebGPU-accelerated when available) trains automatically and predicts damage for every building in view. Toggle **View: Labeled ↔ Predicted** to compare. The panel shows holdout **precision / recall / F1 for the Damaged class** so you can watch quality improve as you label diverse examples.  ## Step 3 — Predict all buildings - **Save labels** persists your manual labels so you can resume later (no full prediction). - **Predict all buildings** trains on all your labels, scores **every** building, and saves the result as a predictions layer (a GeoPackage) — this unlocks the reports below. - **Clear labels** removes your labels (including the saved copy); it cannot be undone. ```{important} Labels and predictions are tied to a specific embedding. If footprints don't respond to labeling or look stale, the layer likely needs to be **re-embedded** — re-run **Embed** and open the new embedding row. ``` ## Step 4 — Validate a sample Open **Building Validation** for the layer to spot-check the predictions. It loads a random sample of footprints (~200 by default) with the pre-/post-event imagery.  - Select a building and label it **Damaged**, **Not Damaged**, or **Unknown**. - **Shortcuts:** `1`/`2`/`3` to label; arrow keys to move Prev/Next (auto-advances to the next unlabeled). Filter by label status; a progress bar tracks your coverage. - **Save Labels** to persist, or **Download GeoJSON** to export the labeled sample. These human labels are the **ground truth** the reports compare predictions against (**Unknown** labels are excluded from metrics). ## Step 5 — Review the reports From the embedding row's **Reports** menu:  - **Validation Report** — matches predictions against your validation labels: overall accuracy, per-class precision/recall/F1 (Damaged, Not Damaged), macro-F1, and a confusion matrix.  - **Assessment Report** — summarizes predictions across the layer (total, scored vs. cloud-excluded, count and % predicted damaged) and, using your validation sample, estimates the **total damaged buildings with a 95% confidence interval**, plus a precision–recall curve.  ## Tips - Label a **diverse** set of buildings (varied roofs, colors, damage severity) rather than many similar ones. - Use **Cloudy** for cloud-obscured buildings so they're excluded from scoring. - Watch the **Damaged-class F1**; if recall is low, add more damaged examples before running **Predict all buildings**.