Update README

README.md

@@ -27,7 +27,7 @@ NEST3D is a multimodal dataset of 104 sociable weaver nests, combining drone-bas
 - **Scale**: Multiple tree-nest scenes with consistent spatial and spectral coverage
 - **Annotation**: Point-level semantic labels for 3D point clouds
 - **Data Source**: Drone-based RGB and multispectral imagery
-- **Application Domain**: Ecological monitoring, wildlife management, 3d semantic segmenation, 3d reconstruction.
+- **Application Domain**: Ecological monitoring, wildlife management, 3d semantic segmentation, 3d reconstruction.
 
 ## Dataset Organization
 
@@ -37,113 +37,163 @@ The dataset is organized into modality-specific directories to support flexible
 
 ```
 NEST3D/
-├── train/
-│   ├── sample_001/
-│   │   ├── RGB/                    # RGB drone images
-│   │   │   ├── sample001_RGB_001.JPG
-│   │   │   └── ...
-│   │   ├── MS/                     # Multispectral imagery
-│   │   │   ├── Green/
-│   │   │   │   ├── sample001_G_001.TIF
-│   │   │   │   └── ...
-│   │   │   ├── Red/
-│   │   │   │   ├── sample001_R_001.TIF
-│   │   │   │   └── ...
-│   │   │   ├── Red_Edge/
-│   │   │   │   ├── sample001_RE_001.TIF
-│   │   │   │   └── ...
-│   │   │   └── NIR/
-│   │   │       ├── sample001_NIR_001.TIF
-│   │   │       └── ...
-│   │   └── sample001.npy            # 3D point cloud with labels
-│   └── sample_002/
-│       └── ...
+├── images/
+│   ├── sample001.tar.gz
+│   │   └── (extracts to:)
+│   │       ├── RGB/
+│   │       │   ├── sample001_RGB_001.JPG
+│   │       │   └── ...
+│   │       └── MS/
+│   │           ├── Green/
+│   │           │   ├── sample001_G_001.TIF
+│   │           │   └── ...
+│   │           ├── Red/
+│   │           │   ├── sample001_R_001.TIF
+│   │           │   └── ...
+│   │           ├── Red_Edge/
+│   │           │   ├── sample001_RE_001.TIF
+│   │           │   └── ...
+│   │           └── NIR/
+│   │               ├── sample001_NIR_001.TIF
+│   │               └── ...
+│   ├── sample002.tar.gz
+│   └── ...
 │
-└── test/
-    ├── sample_084/
-    │   ├── RGB/
-    │   ├── MS/
-    │   └── sample084.npy
-    └── ...
+├── reconstructions/
+│   ├── sample001/
+│   │   ├── sample001.ply
+│   │   ├── sample001_rgb_cameras.json
+│   │   ├── sample001_ms_cameras.json
+│   │   └── sample001_summary.json
+│   └── ...
+│
+├── metadata/
+│   └── (ecological metadata — coming soon)
+│
+├── validation/
+│   ├── expert_validation_R1.csv
+│   └── expert_validation_R2.csv
+│
+├── scripts/
+│   ├── preprocess_step1_correct_ply.py
+│   ├── preprocess_step2_qc_plots.py
+│   ├── preprocess_step3_ptv3.py
+│   ├── preprocess_step4_o3dml.py
+│   └── extract_metadata.py
+│
+├── train.txt
+├── val.txt
+└── test.txt
 ```
-
 ### Data Modalities
 
 #### 1. **RGB Imagery**
 - Raw drone images from aerial acquisition
-- Format: JPEG
-- Organized by data split and scene identifier
-- Example path: `train/sample_001/RGB/sample001_RGB_119.JPG`
+- Format: JPEG, packaged per scene as a compressed archive
+- Example path: `images/sample001.tar.gz` → `RGB/sample001_RGB_119.JPG`
 
 #### 2. **Multispectral Imagery**
 - Four spectral bands from the same acquisitions as RGB
-- Organized into four band-specific folders:
+- Organized into four band-specific folders within each scene's archive:
   - **Green (G)**: Green channel imagery
   - **Red (R)**: Red channel imagery
   - **Red Edge (RE)**: Red Edge channel for vegetation analysis
   - **NIR**: Near-Infrared channel for vegetation health assessment
 - Format: GeoTIFF (.TIF)
-- Example paths:
-  - `train/sample_001/MS/Green/sample001_G_119.TIF`
-  - `train/sample_001/MS/Red/sample001_R_119.TIF`
-  - `train/sample_001/MS/Red_Edge/sample001_RE_119.TIF`
-  - `train/sample_001/MS/NIR/sample001_NIR_119.TIF`
+- Example paths (inside `images/sample001.tar.gz`):
+  - `MS/Green/sample001_G_119.TIF`
+  - `MS/Red/sample001_R_119.TIF`
+  - `MS/Red_Edge/sample001_RE_119.TIF`
+  - `MS/NIR/sample001_NIR_119.TIF`
 
 #### 3. **3D Point Clouds**
-- One NumPy file per scene containing the complete 3D reconstruction
-- Format: `.npy` (NumPy binary format)
-- Per-point attributes: `[x, y, z, r, g, b, label]`
+- One binary PLY file per scene containing the complete 3D reconstruction
+- Format: `.ply` (binary, little-endian)
+- Per-point attributes: `[x, y, z, red, green, blue, scalar_Classification]`
   - **x, y, z**: 3D spatial coordinates (meters)
-  - **r, g, b**: RGB color values (0-255)
-  - **label**: Semantic class label (integer)
-- Example path: `train/sample_001/sample001.npy`
+  - **red, green, blue**: RGB color values (0–255)
+  - **scalar_Classification**: Semantic class label (float-encoded integer): `0` = grass, `1` = tree, `2` = nest, `255` = unclassified / ignore
+- Example path: `reconstructions/sample001/sample001.ply`
+- **Note on the ignore label**: a subset of points in some scenes could not be confidently assigned a class during manual annotation and are marked `255`. This affects 24 of the 104 scenes, ranging from 0.14% to 13.09% of points in the affected scenes. We recommend excluding these points from training and evaluation via an ignore-index mask.
+
+#### 4. **Camera Parameters**
+Each scene's `reconstructions/sampleXXX/` folder includes three JSON files:
+- **`sampleXXX_rgb_cameras.json`**: per-image intrinsics (focal length, principal point, Brown–Conrady distortion coefficients) and extrinsics (camera-to-chunk / chunk-to-camera 4×4 transforms) for every RGB image used in the photogrammetric reconstruction.
+- **`sampleXXX_ms_cameras.json`**: the same per-image intrinsics and extrinsics for every multispectral image (all four bands). Camera extrinsics are identical between the RGB camera and the four multispectral bands at each capture, reflecting the rigid multi-camera rig calibration in which all five sensors are treated as co-located on a shared gimbal. Intrinsics are estimated independently per sensor.
+- **`sampleXXX_summary.json`**: per-scene summary metadata, including total point count, number of aligned RGB/multispectral cameras per band, and the chunk-to-world georeferencing transform.
+
+#### 5. **Ecological Metadata**
+*(Section coming soon — derived per-scene ecological statistics, e.g. tree height, canopy area, nest count and volume.)*
+
+## Expert Biological Validation
+
+The files `expert_validation_R1.csv` and `expert_validation_R2.csv` contain independent biological assessments of all 104 annotated point clouds, conducted by two field biologists with expertise in sociable weaver ecology. Each sample was evaluated on the following criteria:
+
+| Column | Description | Options |
+|--------|-------------|---------|
+| `sample_id` | Sample identifier | sample001–sample104 |
+| `Q1_nest_completeness` | Does the nest label capture the full visible nest structure? | Yes - fully captured / Partial / No / Cannot assess |
+| `Q2_nest_precision` | Does the nest label include false positives from the tree canopy? | None / Minor / Significant / Cannot assess |
+| `Q3_boundary_quality` | Is the nest–tree boundary ecologically reasonable? | Yes / Acceptable / No / Cannot assess |
+| `Q4_grass_ground_plane` | Does the grass label correctly represent ground-level vegetation? | Yes / Acceptable / No / Cannot assess |
+| `Q5_overall_quality` | Overall annotation quality | 1 (very poor) – 5 (excellent) |
+| `Q6_tree_species` | Identified host tree species | Vachellia erioloba / Boscia albitrunca / Other / Uncertain |
+| `Q6b_species_confidence` | Confidence in species identification | Confident / Somewhat confident / Uncertain |
+| `Q7_nest_activity` | Estimated nest activity status | Active / Likely active / Likely abandoned / Abandoned / Cannot assess |
+| `Q8_occlusion_severity` | Degree of nest occlusion by canopy | Low / Medium / High |
+| `Q9_annotation_difficulty` | Estimated annotation difficulty | Easy / Moderate / Hard / Very hard |
+| `Q10_free_observations` | Free-text ecological observations | — |
 
 ## Data Splits
 
-The dataset is divided into fixed training and test sets:
+Sample IDs for a stratified 72/16/16 train/validation/test split are provided as `train.txt`, `val.txt`, and `test.txt` (one sample ID per line) in the repository root. The split was stratified by nest-point percentage to ensure balanced representation of the minority (nest) class across subsets. These files are the authoritative split definition used in our reported benchmarks; users are of course free to define alternative splits from the provided scenes.
 
-- **Training Set**: Used for model training and development
-- **Test Set**: Reserved for model evaluation and benchmarking
+## Processing Scripts
 
-Each split contains a consistent collection of scenes to ensure reliable evaluation.
+The `scripts/` folder contains the preprocessing pipeline used to prepare the raw data for model training:
+
+- **`preprocess_step1_correct_ply.py`**: Cleans the raw point clouds — removes spatial outlier grass points, levels the ground plane where needed, grounds the Z-axis, and applies a point budget/downsampling step for very large scenes. Produces a corrected point cloud (not released; regenerate locally by running this script on the raw `.ply` files).
+- **`preprocess_step2_qc_plots.py`**: Generates quality-control visualizations (top/side/front views, labeled and RGB-colored, with per-class point counts) for visual inspection of raw or corrected point clouds.
+- **`preprocess_step3_ptv3.py`**: Converts corrected point clouds into the Pointcept training format (`coord.npy`, `color.npy`, `segment.npy`), split into train/val/test folders per `train.txt`/`val.txt`/`test.txt`.
+- **`preprocess_step4_o3dml.py`**: Converts the Pointcept-format data into the Open3D-ML format used for RandLA-Net and KPConv training/evaluation.
+- **`extract_metadata.py`**: *(documentation coming soon)*
 
 ## Usage
 
 ### Loading 3D Point Clouds
 
 ```python
+from plyfile import PlyData
 import numpy as np
 
 # Load point cloud with semantic labels
-point_cloud = np.load('train/sample_001/sample001.npy')
-
-# Extract coordinates
-xyz = point_cloud[:, :3]
+ply = PlyData.read('reconstructions/sample001/sample001.ply')
+vertex = ply['vertex']
 
-# Extract colors
-rgb = point_cloud[:, 3:6]
-
-# Extract semantic labels
-labels = point_cloud[:, 6]
+xyz = np.stack([vertex['x'], vertex['y'], vertex['z']], axis=-1)
+rgb = np.stack([vertex['red'], vertex['green'], vertex['blue']], axis=-1)
+labels = np.asarray(vertex['scalar_Classification'])  # 0=grass, 1=tree, 2=nest, 255=ignore
 ```
 
 ### Loading Multispectral Imagery
 
 ```python
+import tarfile
 from PIL import Image
 import numpy as np
+import io
 
-# Load a single band
-green_band = np.array(Image.open('train/sample_001/MS/Green/sample001_G_001.TIF'))
+with tarfile.open('images/sample001.tar.gz') as tar:
+    def load_band(path):
+        f = tar.extractfile(path)
+        return np.array(Image.open(io.BytesIO(f.read())))
 
-# Load all four bands for a given image
-green = np.array(Image.open('train/sample_001/MS/Green/sample001_G_001.TIF'))
-red = np.array(Image.open('train/sample_001/MS/Red/sample001_R_001.TIF'))
-red_edge = np.array(Image.open('train/sample_001/MS/Red_Edge/sample001_RE_001.TIF'))
-nir = np.array(Image.open('train/sample_001/MS/NIR/sample001_NIR_001.TIF'))
+    green    = load_band('MS/Green/sample001_G_001.TIF')
+    red      = load_band('MS/Red/sample001_R_001.TIF')
+    red_edge = load_band('MS/Red_Edge/sample001_RE_001.TIF')
+    nir      = load_band('MS/NIR/sample001_NIR_001.TIF')
 
-# Stack into multiband image
-multispectral = np.stack([green, red, red_edge, nir], axis=-1)
+    multispectral = np.stack([green, red, red_edge, nir], axis=-1)
 ```
 
 ### Using with Hugging Face Datasets Library
@@ -166,13 +216,14 @@ huggingface-cli download NEST3D/dataset --repo-type dataset --local-dir ./NEST3D
 
 ## Dataset Information
 
-- **Total Size**: 
-  - **Number of Scenes**: 104 samples. Split into train/test as 83/21
+- **Number of Scenes**: 104
+- **Total Points**: 951.66M (mean 9.15M ± 8.63M per scene; range 0.69M–68.39M)
+- **Total RGB Images**: 25,172 (mean 242 per scene; range 19–562)
+- **Class Distribution**: Grass 45.39%, Tree 48.30%, Nest 5.28%
 - **Modalities**: RGB, Multispectral (4 bands), 3D Point Clouds
 - **Image Format**: JPEG (RGB), GeoTIFF (Multispectral)
-- **Point Cloud Format**: NumPy arrays
-- **Annotation Type**: Per-point semantic labels
-
+- **Point Cloud Format**: Binary PLY
+- **Annotation Type**: Per-point semantic labels, plus expert biological validation (see above)
 
 ## Citation
 
@@ -207,5 +258,5 @@ We extend our gratitude to our collaborators and field partners in Namibia for t
 For questions, issues, or contributions, please visit the [dataset discussion forum](https://huggingface.co/datasets/NEST3D/dataset/discussions).
 
 
-**Last Updated**: May 2026  
-**Dataset Version**: 1.0
+**Last Updated**: July 2026
+**Dataset Version**: 1.1