Upload ANNOTATION_PROTOCOL.md

ANNOTATION_PROTOCOL.md

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+# MatchGeo Annotation Protocol
+# Local Feature Matching Keypoint Annotation on Digital Elevation Models
+
+> **Version**: 1.1  
+> **Date**: 2026-07-24  
+> **Authors**: Sabrina Correa (UFV)  
+> **Dataset**: MatchGeo DEM v1.1 (DOI: 10.5281/zenodo.21229785)  
+> **Standard**: Aligned with OGC TrainingDML-AI (23-008r3 / 24-006r1)  
+> **License**: CC BY 4.0
+
+---
+
+## Table of Contents
+
+1. [Purpose and Scope](#1-purpose-and-scope)
+2. [Prerequisites](#2-prerequisites)
+3. [Annotation Class](#3-annotation-class)
+4. [Annotation Workflow](#4-annotation-workflow)
+5. [Annotation Rules](#5-annotation-rules)
+6. [Quality Control](#6-quality-control)
+7. [File Format and Metadata](#7-file-format-and-metadata)
+8. [Common Pitfalls and Edge Cases](#8-common-pitfalls-and-edge-cases)
+9. [Appendix A: Visual Example](#appendix-a-visual-example)
+10. [Appendix B: Glossary](#appendix-b-glossary)
+
+---
+
+## 1. Purpose and Scope
+
+### 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.
+
+### 1.2 What to Annotate
+
+Annotators must identify **morphologically salient points** on the DEM surface that would be reliably detected by feature descriptors such as SIFT, SURF, ORB, or learned descriptors (e.g., SuperPoint, R2D2). These points must be:
+
+- **Locally distinctive**: The point must stand out from its immediate neighborhood.
+- **Geometrically stable**: The point must remain detectable under moderate changes in resolution, noise, or viewing angle.
+- **Repeatable**: The point should be identifiable by another annotator (or the same annotator on a different day) with high spatial consistency.
+
+In **urban areas** (e.g., São Paulo, Bonn), the primary salient features are **building corners** — the intersection points of building roof edges where elevation changes abruptly.
+
+### 1.3 What NOT to Annotate
+
+Do **not** annotate:
+
+- Points in flat or uniformly sloped areas with no local distinctiveness.
+- Points at tile boundaries (within 5 pixels of the edge).
+- 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.
+
+### 1.4 Scope of Application
+
+This protocol applies to:
+
+- **GER_BN** (Bonn, Germany) — primary annotated city
+- **BRA_SP** (São Paulo, Brazil) — secondary annotated city
+- Any future cities added to the MatchGeo dataset with manual annotations
+
+---
+
+## 2. Prerequisites
+
+### 2.1 Required Software
+
+- **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
+
+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) |
+
+### 2.3 Annotator Qualifications
+
+Annotators should have:
+
+- Basic familiarity with DEM visualization techniques (hillshade, slope).
+- Experience with QGIS or equivalent GIS software.
+- Understanding of what makes a point "salient" for computer vision feature detectors.
+
+---
+
+## 3. Annotation Class
+
+All annotated keypoints belong to a **single class**:
+
+### `general_interest_point` — Salient Elevation Keypoint
+
+> **Definition**: A point on the DEM surface that is locally distinctive and suitable for local feature matching. In urban areas, this is typically a **building corner** (the intersection of roof edges). In natural terrain, this is any point with high local curvature or distinctiveness that would produce a strong response from a feature detector.
+
+**Rationale for single class**: The MatchGeo dataset is designed for **local feature matching**, not semantic segmentation or landform classification. The task requires correspondences between geometrically similar points, not semantic labels. A single class simplifies annotation while preserving the geometric information needed for feature matching research.
+
+---
+
+## 4. Annotation Workflow
+
+### 4.1 Pre-Annotation Setup
+
+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).
+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.
+
+### 4.2 Annotation Procedure
+
+For each tile, follow this sequence:
+
+#### Step 1: Global Scan (30 seconds)
+- 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)
+- 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)
+- 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.
+- 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:
+
+```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
+```
+
+The script will:
+1. Read the shapefile points.
+2. Convert geographic coordinates to pixel coordinates (0–332).
+3. Extract elevation values from the DEM tile.
+4. Generate the TDML-compliant JSON file.
+
+---
+
+## 5. Annotation Rules
+
+### 5.1 Minimum Feature Distinctiveness
+
+All annotated features must be resolvable at the tile resolution. The point must have:
+
+- **Local curvature**: The point must be a local extremum (maximum or minimum) in at least one direction, OR a point of high curvature (corner, junction).
+- **Minimum neighborhood**: The distinctiveness must be visible in a 5×5 pixel neighborhood.
+- **No ambiguity**: The point must have a single, clear location. If the "best" pixel is ambiguous across a 3×3 region, do not annotate.
+
+### 5.2 Edge Proximity
+
+- **Default**: No keypoints within 5 pixels of the tile boundary.
+- **Hard limit**: Never annotate within 2 pixels of the boundary.
+- **Rationale**: Points near boundaries may be truncated or padded differently during model training, leading to inconsistent feature descriptors.
+
+### 5.3 Spacing
+
+- Minimum distance between any two keypoints: **5 pixels**.
+- **Rationale**: Feature descriptors typically operate on patches of 8×8 to 32×32 pixels. Points closer than 5 pixels would have overlapping descriptor windows, reducing independence.
+
+### 5.4 Urban Areas: Building Corners
+
+In urban DEMs (Bonn, São Paulo), the vast majority of salient points are **building corners**:
+
+- **What is a building corner?** The intersection of two roof edges, where the elevation surface changes direction abruptly.
+- **How to identify**: On hillshade, building corners appear as sharp, dark-light transitions at the intersection of two edges.
+- **Placement**: Place the point at the **intersection pixel** — the exact corner where the two edges meet.
+- **Avoid**: Points along straight roof edges (not corners), points on flat roof surfaces, points on building shadows (unless the shadow edge coincides with a real corner).
+
+### 5.5 Natural Terrain
+
+In natural terrain (if annotated in future cities), salient points include:
+
+- **Peaks and summits**: Local maxima with clear prominence.
+- **Saddles and cols**: Local minima along ridge lines.
+- **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?"*
+
+### 5.6 Multi-Scale Considerations
+
+Annotators should consider how the feature would appear at different resolutions:
+
+- A feature visible at 1.0 m resolution should still be detectable (though blurred) at 2.0 m.
+- Do not annotate features that are **resolution-dependent artifacts** (e.g., single-pixel noise spikes).
+- When in doubt, check the source data quality or hillshade at multiple sun angles.
+
+---
+
+## 6. Quality Control
+
+### 6.1 Self-Review Checklist
+
+Before submitting annotations, the annotator must complete this checklist:
+
+- [ ] All keypoints are within the valid tile area (≥5 px from edge).
+- [ ] No two keypoints are within 5 pixels of each other.
+- [ ] All keypoints are on clearly salient features (building corners or natural morphological features).
+- [ ] No keypoints are in obvious artifact areas (voids, striping).
+- [ ] All keypoints would be repeatable by the same annotator on a different day.
+- [ ] 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
+
+All annotated keypoints are assumed to have **high confidence** (≥0.8). The annotator is instructed to:
+
+- **Only annotate points they are fully confident about.**
+- **Skip ambiguous points.** It is better to have fewer, high-quality annotations than many uncertain ones.
+- **Do not guess.** If a point might be salient but the annotator is not sure, skip it.
+
+For retroactive application to existing annotations (GER_BN, BRA_SP), all keypoints are assigned a default confidence of **0.8**.
+
+---
+
+## 7. File Format and Metadata
+
+### 7.1 QGIS Annotation Layer
+
+During annotation, points are stored in a QGIS vector layer (shapefile or GeoPackage):
+
+| Field | Type | Description |
+|---|---|---|
+| `id` | Integer | Unique keypoint ID within the tile |
+| `class` | String | Fixed `"general_interest_point"` |
+| `confidence` | Float | Annotator confidence (0.0–1.0); default 0.8 |
+
+### 7.2 Annotation File Naming
+
+```
+{CITY_CODE}_{row}_{col}_annotation.json
+
+Example: GER_BN_001_012_annotation.json
+```
+
+### 7.3 JSON Structure
+
+Each annotation file conforms to the MatchGeo Keypoint Annotation Schema:
+
+```json
+{
+  "type": "AI_ObjectLabel",
+  "id": "GER_BN_001_012_annotation",
+  "tileId": "GER_BN_001_012",
+  "cityCode": "GER_BN",
+  "crs": "EPSG:25832",
+  "resolution": 1.0,
+  "tileExtent": {
+    "westBound": 369500.0,
+    "eastBound": 369833.0,
+    "southBound": 5621500.0,
+    "northBound": 5621833.0
+  },
+  "keypoints": [
+    {
+      "id": "kp_001",
+      "pixelX": 312,
+      "pixelY": 282,
+      "geoX": 369812.0,
+      "geoY": 5621782.0,
+      "class": "general_interest_point",
+      "elevation": 158.4,
+      "confidence": 0.8
+    }
+  ],
+  "labeling": {
+    "labelingMethod": "manual",
+    "annotatorId": "annotator_ufv_01",
+    "annotationTime": "2025-11-14T14:32:00Z",
+    "toolVersion": "qgis-shapefile-conversion",
+    "labelingProtocol": "https://github.com/paeslemesa/matchgeodem/blob/main/docs/annotation_protocol.md"
+  },
+  "quality": {
+    "sourceDataQuality": "Airborne LiDAR, ground classified, vertical accuracy <0.15m RMSE"
+  }
+}
+```
+
+### 7.4 Required vs. Optional Fields
+
+| Field | Required | Description |
+|---|---|---|
+| `type` | ✅ | Fixed `"AI_ObjectLabel"` |
+| `id` | ✅ | Unique annotation file ID |
+| `tileId` | ✅ | Corresponding DEM tile ID |
+| `cityCode` | ✅ | City code (e.g., `"GER_BN"`, `"BRA_SP"`) |
+| `crs` | ✅ | EPSG code |
+| `resolution` | ✅ | GSD in meters |
+| `tileExtent` | ❌ | Bounding box in projected coords |
+| `keypoints` | ✅ | Array of keypoint objects |
+| `keypoints[].id` | ✅ | Unique keypoint ID |
+| `keypoints[].pixelX` | ✅ | X in pixel space (0–332) |
+| `keypoints[].pixelY` | ✅ | Y in pixel space (0–332) |
+| `keypoints[].geoX` | ❌ | X in projected CRS |
+| `keypoints[].geoY` | ❌ | Y in projected CRS |
+| `keypoints[].class` | ✅ | Fixed `"general_interest_point"` |
+| `keypoints[].elevation` | ✅ | Elevation at keypoint (m) |
+| `keypoints[].confidence` | ❌ | Annotator confidence (0–1); default 0.8 |
+| `labeling` | ✅ | Provenance object |
+| `labeling.labelingMethod` | ✅ | Fixed `"manual"` |
+| `labeling.annotatorId` | ✅ | Anonymous annotator ID |
+| `labeling.annotationTime` | ✅ | ISO 8601 timestamp |
+| `labeling.toolVersion` | ❌ | `"qgis-shapefile-conversion"` |
+| `labeling.labelingProtocol` | ❌ | URL to this protocol |
+| `quality` | ❌ | Quality metrics object |
+
+---
+
+## 8. Common Pitfalls and Edge Cases
+
+### 8.1 Building Corners vs. Roof Edges
+
+**Problem**: A long, straight roof edge has many points that look somewhat distinctive.
+
+**Solution**: Only annotate **corners** — points where two edges intersect. A point along a straight edge is not locally distinctive in all directions and would not produce a strong corner detector response.
+
+### 8.2 Flat Roofs
+
+**Problem**: Large flat-roofed buildings have no corners visible in the DEM.
+
+**Solution**: Do not annotate flat roof surfaces. If the building has a parapet or edge wall that creates a corner, annotate that corner.
+
+### 8.3 Vegetation
+
+**Problem**: Trees and vegetation create noisy, irregular elevation surfaces.
+
+**Solution**: In urban areas, focus on building corners. In natural terrain, avoid annotating on dense vegetation unless a clear morphological feature (e.g., a forest clearing corner) is visible.
+
+### 8.4 Data Artifacts
+
+**Problem**: Striping, voids, or interpolation artifacts create spurious elevation features.
+
+**Solution**:
+- Learn to recognize common artifacts:
+  - **Striping**: Parallel lines of alternating high/low elevation (common in satellite DEMs).
+  - **Voids**: Areas with NoData (-9999) or interpolated fill values.
+  - **Noise**: Single-pixel spikes or pits with no surrounding morphological context.
+- Cross-reference with hillshade.
+- Skip artifact-affected tiles if >20% of the tile is affected.
+
+### 8.5 Partial Tiles
+
+**Problem**: Tiles at the city boundary may be partially empty (NoData).
+
+**Solution**: Annotate only the valid data region. Ensure all keypoints are on valid elevation pixels (not NoData).
+
+### 8.6 Multi-Story Buildings
+
+**Problem**: Buildings of different heights create complex roof patterns.
+
+**Solution**: Annotate the corners of the **roof footprint** — the outermost edges visible in the DEM. Internal roof structures (skylights, HVAC units) are typically too small to be reliable at 1.0 m resolution.
+
+---
+
+## 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*