# Damage Mapping with a Trained Model **Goal: produce a wall-to-wall damage map.** You draw labels on a small area, train a segmentation model on them, and HASTE runs that model across the entire image layer to generate a continuous damage prediction you can visualize and download as a geopackage. For a faster, building-level result with no training job, see {doc}`Rapid Building Assessment `. ```{admonition} The workflow at a glance :class: tip Standard-workflow layer → **Label** a training area → **Train** a model → **View & export** the results. ``` ## Before you start Create a project and add a **Standard**-workflow image layer with your pre-/post-event imagery. See {doc}`Projects ` and {doc}`Image layers `. ```{admonition} Try it with sample data :class: tip New to HASTE? Use the **Lahaina, Maui** sample (Vantor / Maxar post-event imagery) from {doc}`Image layers ` (see *Sample data to try HASTE*): create a **Standard**-workflow layer with `maxar_lahaina_8_12_2023-visual.tif` as the post-event imagery. Building footprints download automatically from Overture Maps. ``` ## Step 1 — Label a training area Labeling means manually annotating damaged, undamaged, and background areas on a small section of the imagery. These labels train the model, which then predicts damage across the rest of the image. This step is required before training. **Launch the tool** from the Projects page by clicking **Launch** next to the image layer. ### Draw labels  Use the **Pan**, **Polygon**, **Rectangle**, **Circle**, **Edit**, and **Delete** tools to annotate. Then pick a label class:  You must select **both a tool and a class** to draw a label. Click **Save** to store your labels, or the arrow next to **Save** to save and start training in one step. ### Adjust the imagery To make damage easier to see while you label, tune the view of the pre-/post-event imagery: **Opacity**, **Contrast**, **Hue Rotation**, and **Saturation** sliders (with **Reset**). Toggle between post- and pre-event imagery with the imagery switch or `Ctrl+Alt+C` — if you didn't upload pre-event imagery, the tool falls back to the Azure Basemap.  ### Tips for effective labeling The quality of your labels drives the quality of the model. A few pointers: **Minimum number of labels** — draw at least 5–10 per class. Training improves with more quality labels; 70–100 make a good training set, and more than ~150 isn't necessary. **Cluster labels closely** — label features adjacent to each other (e.g. a building *and* the background around it). Unlabeled areas aren't used in training, so labeling a building without its surroundings lets the model make a blurry prediction without penalty. | Correct | Incorrect | |---------|-----------| |  |  | **Draw labels precisely** — high precision matters more than covering large areas quickly. | Correct | Incorrect | |---------|-----------| |  |  | **Maximize label diversity** — 20 buildings with varied roofs, sizes, and textures teach the model more than 20 identical ones. | High diversity | Low diversity | |----------------|---------------| |  |  | **Label relevant portions only** — for a partially damaged building, label only the damaged pixels as damaged, not the whole building. | Correct | Incorrect | |---------|-----------| |  |  | (train-a-new-model)= ## Step 2 — Train the model Satellite imagery varies a lot between regions, so HASTE trains a fresh model on your labels for each run rather than reusing weights across areas. Once you've labeled, start training in either of two ways: 1. Click **Save and Train** in the labeling tool.  2. Or click **Train** for that image layer on the Projects page.  ### Training parameters Leave these at their defaults if you're unsure: - **Model Name** — a unique name for your model. - **Base Model** — a model from the {doc}`Model catalog ` to fine-tune. Only models whose event type and imagery source match your layer are shown. - **Learning Rate** — how much the model adjusts per update. - **Batch Size** — training examples per iteration; larger is faster but needs more memory. - **Max Epochs** — passes over the training data. ## Step 3 — View and export results When training and inference finish, open the model's **Results** menu for several ways to use the predictions.  ### View (built-in visualizer)  **View** opens the visualizer: the pre- and post-event imagery side by side with a swipe control, the predicted damage layer overlaid on both, and an optional **raw predictions** layer you can toggle on. Imagery sliders (opacity, contrast, hue, saturation) and keyboard shortcuts — `A` (all pre-event), `S` (split), `D` (all post-event) — help you inspect and share the result.  ### Download the outputs - **Download Geopackage (`.gpkg`)** — the predicted damage layer. - **Download Training Artifacts** — the training outputs (saved labels, checkpoints, logs). - **Download Inference Artifacts** — the inference outputs (predictions and building footprints). Each download shows its file size. ### Reports If you've validated buildings for this layer, the Results menu also offers a **Validation Report** (accuracy, per-class precision/recall/F1, and a confusion matrix against your validation labels) and an **Assessment Report** (a damage summary and a damaged-building population estimate with a 95% confidence interval). These are the same reports described under {doc}`Rapid Building Assessment `.