Update ANNOTATION_PROTOCOL.md

ANNOTATION_PROTOCOL.md

@@ -29,7 +29,7 @@
 
 ### 1.1 Objective
 
-This protocol defines the procedure for annotating salient elevation keypoints on 333×333 pixel Digital Elevation Model (DEM) tiles. The annotated keypoints serve as ground truth for training and evaluating **local feature matching algorithms** — computer vision methods that detect and describe distinctive points in images (or elevation surfaces) to establish correspondences across different views, scales, or domains.
+This protocol defines the procedure for annotating salient elevation keypoints on 256x256 pixel Digital Elevation Model (DEM) tiles. The annotated keypoints serve as ground truth for training and evaluating **local feature matching algorithms** — computer vision methods that detect and describe distinctive points in images (or elevation surfaces) to establish correspondences across different views, scales, or domains.
 
 ### 1.2 What to Annotate
 
@@ -50,11 +50,11 @@ Do **not** annotate:
 - Points in areas with obvious data artifacts (striping, voids, interpolation errors).
 - Points in water bodies (lakes, rivers) unless the shoreline intersection is clearly salient.
 - Points on roads, sidewalks, or other flat man-made surfaces unless they form a clear corner or edge intersection.
-- Points where the annotator is not fully confident about the salience.
+- **Points where the annotator is not fully confident about the salience.**
 
 ### 1.4 Scope of Application
 
-This protocol applies to:
+This protocol applies, but not limited to:
 
 - **GER_BN** (Bonn, Germany) — primary annotated city
 - **BRA_SP** (São Paulo, Brazil) — secondary annotated city
@@ -68,7 +68,6 @@ This protocol applies to:
 
 - **QGIS** ≥ 3.28 (Long Term Release)
 - **Python** ≥ 3.9 with `geopandas`, `rasterio`, `shapely`
-- Optional: **CloudCompare** for 3D point cloud cross-reference (when source LiDAR is available)
 
 ### 2.2 Required Data
 
@@ -76,8 +75,8 @@ For each tile to annotate:
 
 | File | Purpose |
 |---|---|
-| `{CITY_CODE}_{row}_{col}.tif` | 333×333 pixel DEM tile (Float32, NoData=-9999) |
-| `{CITY_CODE}_{row}_{col}_hillshade.tif` | Hillshade visualization (optional but recommended) |
+| `{CITY_CODE}_{row}_{col}.tif` | 256x256 pixel DEM tile (Float32, NoData=-9999) |
+
 
 ### 2.3 Annotator Qualifications
 
@@ -107,7 +106,7 @@ All annotated keypoints belong to a **single class**:
 
 1. **Load the tile** in QGIS.
 2. **Verify CRS**: Ensure the project CRS matches the tile CRS (e.g., `EPSG:25832` for Bonn, `EPSG:31983` for São Paulo).
-3. **Load hillshade layer** as a visual reference (optional but strongly recommended).
+3. **Chenge visualization to hillshade** as a visual reference (optional but strongly recommended).
 4. **Set zoom level**: Work at a scale where individual pixels are visible but the full tile context is retained.
 5. **Check for artifacts**: Scan the tile for data quality issues (voids, striping, noise). Skip tiles with >20% artifact coverage.
 
@@ -115,38 +114,38 @@ All annotated keypoints belong to a **single class**:
 
 For each tile, follow this sequence:
 
-#### Step 1: Global Scan (30 seconds)
+#### Step 1: Global Scan
 - Pan across the entire tile at moderate zoom.
 - Identify the dominant features: buildings, vegetation, roads, natural terrain.
 - Note any artifact regions to avoid.
 
-#### Step 2: Systematic Pass (2–5 minutes)
+#### Step 2: Systematic Pass
 - Scan the tile in a systematic pattern (left-to-right, top-to-bottom).
 - For each candidate point, ask: **"Would a feature detector (SIFT, Harris, SuperPoint) reliably find this point?"**
 - Place the keypoint at the **precise pixel** of maximum distinctiveness.
 - **Only annotate if you are fully confident** in the point's salience. When in doubt, skip.
 
-#### Step 3: Verification Pass (1–2 minutes)
+#### Step 3: Verification Pass
 - Toggle hillshade on/off to verify distinctiveness.
 - Check that no keypoints are within **5 pixels** of the tile edge.
 - Verify that keypoints are not clustered — minimum spacing of **5 pixels** between any two keypoints.
 - Review each point: **"If I saw this tile again tomorrow, would I place the point at the same pixel?"**
 
 #### Step 4: Save
-- Save the QGIS project with the annotation layer.
+- Save the QGIS project with the annotation shapefile layer.
 - Export the annotation layer as a **shapefile** or **GeoPackage**.
 
 ### 4.3 Post-Processing
 
-After annotation, run the MatchGeo conversion script to transform the shapefile into the standardized JSON annotation format:
+After annotation, run the MatchGeo conversion script to transform the shapefile into the standardized JSON annotation format. The conversion file is in:
 
-```bash
-python scripts/convert_shapefile_to_json.py     --input annotations/GER_BN_001_012.shp     --tile data/GER_BN/tiles/GER_BN_001_012.tif     --output data/GER_BN/annotations/GER_BN_001_012_annotation.json
+```
+scripts/convert_shp2anno.py
 ```
 
 The script will:
 1. Read the shapefile points.
-2. Convert geographic coordinates to pixel coordinates (0–332).
+2. Convert geographic coordinates to pixel coordinates (0–255).
 3. Extract elevation values from the DEM tile.
 4. Generate the TDML-compliant JSON file.
 
@@ -191,7 +190,7 @@ In natural terrain (if annotated in future cities), salient points include:
 - **Ridge/valley junctions**: Confluences of multiple ridge or valley lines.
 - **Cliff corners**: Abrupt changes in slope direction.
 
-When in doubt, apply the **feature detector test**: *"Would SIFT or Harris detect this point with a high response?"*
+When in doubt, apply the **feature detector test**: *"Would I select this point tomorrow?"*
 
 ### 5.6 Multi-Scale Considerations
 
@@ -217,22 +216,8 @@ Before submitting annotations, the annotator must complete this checklist:
 - [ ] The annotation shapefile has been exported and converted to JSON.
 - [ ] The JSON file validates against the MatchGeo Annotation Schema.
 
-### 6.2 Automated Validation
-
-After conversion to JSON, run the validation script:
-
-```bash
-python scripts/validate_annotations.py     --input data/GER_BN/annotations/GER_BN_001_012_annotation.json     --tile data/GER_BN/tiles/GER_BN_001_012.tif
-```
 
-The script checks:
-- All keypoints are within the tile bounds.
-- No two keypoints are within 5 pixels.
-- All keypoints are ≥5 pixels from the edge.
-- Elevation values are valid (not NoData).
-- JSON structure conforms to the schema.
-
-### 6.3 Confidence Policy
+### 6.2 Confidence Policy
 
 All annotated keypoints are assumed to have **high confidence** (≥0.8). The annotator is instructed to:
 
@@ -383,56 +368,5 @@ Each annotation file conforms to the MatchGeo Keypoint Annotation Schema:
 
 ---
 
-## Appendix A: Visual Example
-
-### GER_BN_001_012 — Urban Building Corners
-
-This example from Bonn, Germany, demonstrates the annotation of building corners on a 1.0 m resolution airborne LiDAR DEM.
-
-| Panel | Description |
-|---|---|
-| **A. DEM (Grayscale Elevation)** | Raw elevation values. Brighter = higher. Building roofs appear as bright rectangles; shadows and lower areas appear darker. |
-| **B. Hillshade (Relief Visualization)** | Simulated illumination from the northwest. Building edges and corners are clearly visible as sharp light-dark transitions. |
-| **C. DEM Zoom (Data Region)** | Close-up of the right half of the tile. Seven keypoints (kp_0–kp_6) are annotated on building corners. |
-| **D. Hillshade Zoom (Data Region)** | Hillshade close-up confirms that all keypoints are on sharp, salient corners. |
-
-![GER_BN_001_012 Annotated](GER_BN_001_012_annotated.png)
-
-**Keypoint Analysis**:
-
-| ID | pixelX | pixelY | Description |
-|---|---|---|---|
-| kp_0 | 376 | 282 | Corner of rectangular building roof — sharp intersection of two edges |
-| kp_1 | 368 | 292 | Adjacent corner of same building complex — distinct from kp_0 by ≥5 px |
-| kp_2 | 362 | 287 | Corner of smaller structure — clear edge intersection |
-| kp_3 | 384 | 259 | Corner of large rectangular building — prominent, high-contrast corner |
-| kp_4 | 384 | 29 | Corner near top edge of tile — still ≥5 px from boundary |
-| kp_5 | 371 | 41 | Corner of building near tile top — distinct from kp_4 |
-| kp_6 | 398 | 63 | Corner at right edge of tile — placed at the last valid pixel before boundary |
-
-**Observations**:
-- All 7 keypoints are on **building corners** — intersections of roof edges.
-- Minimum spacing between any two keypoints: ~8 pixels (kp_0–kp_1), satisfying the ≥5 px rule.
-- All keypoints are ≥5 pixels from the tile boundary, except kp_6 which is at the right edge. In production, this point should be verified to be ≥5 px from the actual data boundary (the image shows a black/gray padding region on the left).
-- The hillshade confirms that each point is at a sharp, salient corner with high local contrast.
-
----
-
-## Appendix B: Glossary
-
-| Term | Definition |
-|---|---|
-| **DEM** | Digital Elevation Model — a raster representation of terrain surface elevation. |
-| **DSM** | Digital Surface Model — a DEM that includes surface features (buildings, vegetation). MatchGeo uses DSMs. |
-| **Feature descriptor** | A mathematical representation of a local image (or elevation) patch, used for matching. Examples: SIFT, SURF, ORB, SuperPoint. |
-| **Feature matching** | The process of finding correspondences between feature descriptors in two or more images (or DEM patches). |
-| **GSD** | Ground Sample Distance — the physical distance on the ground represented by one pixel. |
-| **Hillshade** | A grayscale visualization of a DEM simulating illumination from a light source, emphasizing terrain relief. |
-| **Keypoint** | A salient, locally distinctive point in an image or elevation surface. Also called "interest point" or "feature point." |
-| **NoData** | A special value (-9999.0 in MatchGeo) indicating missing or invalid data. |
-| **Salient** | Visually or geometrically distinctive; standing out from the immediate surroundings. |
-| **W3C PROV-O** | The W3C Provenance Ontology — a standard for representing provenance information. |
-
----
 
 *Protocol version 1.1 — MatchGeo DEM Annotation. For questions, contact sabrina.correa@ufv.br*