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The dataset generation failed because of a cast error
Error code: DatasetGenerationCastError
Exception: DatasetGenerationCastError
Message: An error occurred while generating the dataset
All the data files must have the same columns, but at some point there are 1 new columns ({'NEST3D EXPERT ANNOTATION VALIDATION: CLEMENT'}) and 1 missing columns ({'NEST3D EXPERT ANNOTATION VALIDATION: FRITZ'}).
This happened while the csv dataset builder was generating data using
hf://datasets/NEST3D/dataset/validation/expert_validation_R2.csv (at revision d2965233821d42bbb7f070165b3103741ce39556), ['hf://datasets/NEST3D/dataset@d2965233821d42bbb7f070165b3103741ce39556/validation/expert_validation_R1.csv', 'hf://datasets/NEST3D/dataset@d2965233821d42bbb7f070165b3103741ce39556/validation/expert_validation_R2.csv']
Please either edit the data files to have matching columns, or separate them into different configurations (see docs at https://hf.co/docs/hub/datasets-manual-configuration#multiple-configurations)
Traceback: Traceback (most recent call last):
File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1837, in _prepare_split_single
writer.write_table(table)
~~~~~~~~~~~~~~~~~~^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/arrow_writer.py", line 765, in write_table
self._write_table(pa_table, writer_batch_size=writer_batch_size)
~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/arrow_writer.py", line 773, in _write_table
pa_table = table_cast(pa_table, self._schema)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2369, in table_cast
return cast_table_to_schema(table, schema)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2297, in cast_table_to_schema
raise CastError(
...<3 lines>...
)
datasets.table.CastError: Couldn't cast
NEST3D EXPERT ANNOTATION VALIDATION: CLEMENT: string
Unnamed: 1: string
Unnamed: 2: string
Unnamed: 3: string
Unnamed: 4: string
Unnamed: 5: string
Unnamed: 6: string
Unnamed: 7: string
Unnamed: 8: string
Unnamed: 9: string
Unnamed: 10: string
Unnamed: 11: string
Unnamed: 12: string
-- schema metadata --
pandas: '{"index_columns": [{"kind": "range", "name": null, "start": 0, "' + 1913
to
{'NEST3D EXPERT ANNOTATION VALIDATION: FRITZ': Value('string'), 'Unnamed: 1': Value('string'), 'Unnamed: 2': Value('string'), 'Unnamed: 3': Value('string'), 'Unnamed: 4': Value('string'), 'Unnamed: 5': Value('string'), 'Unnamed: 6': Value('string'), 'Unnamed: 7': Value('string'), 'Unnamed: 8': Value('string'), 'Unnamed: 9': Value('string'), 'Unnamed: 10': Value('string'), 'Unnamed: 11': Value('string'), 'Unnamed: 12': Value('string')}
because column names don't match
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1369, in compute_config_parquet_and_info_response
parquet_operations, partial, estimated_dataset_info = stream_convert_to_parquet(
~~~~~~~~~~~~~~~~~~~~~~~~~^
builder, max_dataset_size_bytes=max_dataset_size_bytes
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
)
^
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 948, in stream_convert_to_parquet
builder._prepare_split(split_generator=splits_generators[split], file_format="parquet")
~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1683, in _prepare_split
for job_id, done, content in self._prepare_split_single(
~~~~~~~~~~~~~~~~~~~~~~~~~~^
gen_kwargs=gen_kwargs, job_id=job_id, **_prepare_split_args
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
):
^
File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1839, in _prepare_split_single
raise DatasetGenerationCastError.from_cast_error(
...<4 lines>...
)
datasets.exceptions.DatasetGenerationCastError: An error occurred while generating the dataset
All the data files must have the same columns, but at some point there are 1 new columns ({'NEST3D EXPERT ANNOTATION VALIDATION: CLEMENT'}) and 1 missing columns ({'NEST3D EXPERT ANNOTATION VALIDATION: FRITZ'}).
This happened while the csv dataset builder was generating data using
hf://datasets/NEST3D/dataset/validation/expert_validation_R2.csv (at revision d2965233821d42bbb7f070165b3103741ce39556), ['hf://datasets/NEST3D/dataset@d2965233821d42bbb7f070165b3103741ce39556/validation/expert_validation_R1.csv', 'hf://datasets/NEST3D/dataset@d2965233821d42bbb7f070165b3103741ce39556/validation/expert_validation_R2.csv']
Please either edit the data files to have matching columns, or separate them into different configurations (see docs at https://hf.co/docs/hub/datasets-manual-configuration#multiple-configurations)Need help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
NEST3D EXPERT ANNOTATION VALIDATION: FRITZ string | Unnamed: 1 null | Unnamed: 2 null | Unnamed: 3 null | Unnamed: 4 null | Unnamed: 5 null | Unnamed: 6 null | Unnamed: 7 null | Unnamed: 8 null | Unnamed: 9 null | Unnamed: 10 null | Unnamed: 11 null | Unnamed: 12 null |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
null | null | null | null | null | null | null | null | null | null | null | null | null |
INSTRUCTIONS: For each sample row below, click 'View image' to open the QC plot (top/side/front views in label colors and RGB), then select answers from the dropdown menus in columns Q1-Q9. Q10 is free text. Q9 and Q10 are optional. | null | null | null | null | null | null | null | null | null | null | null | null |
LABEL COLORS: Green = Grass | Brown = Tree | Red = Nest | null | null | null | null | null | null | null | null | null | null | null | null |
null | null | null | null | null | null | null | null | null | null | null | null | null |
QUESTION GUIDE β read before starting | null | null | null | null | null | null | null | null | null | null | null | null |
Q1 β Nest Completeness: Does the labeled nest region capture the full extent of the visible nest structure? OPTIONS: Yes - fully captured | Partial - some material missing | No - significant missing | Cannot assess | null | null | null | null | null | null | null | null | null | null | null | null |
Q2 β Nest Precision: Does the nest label include points that belong to the tree canopy rather than the nest? (false positives) OPTIONS: None - label appears clean | Minor - small amounts of tree points | Significant - substantial mislabeling | Cannot assess | null | null | null | null | null | null | null | null | null | null | null | null |
Q3 β Tree/Nest Boundary: Is the boundary between nest and tree branch structure ecologically reasonable? OPTIONS: Yes - ecologically reasonable | Acceptable - minor issues | No - does not reflect actual interface | Cannot assess | null | null | null | null | null | null | null | null | null | null | null | null |
Q4 β Grass / Ground Plane: Does the grass label correctly represent ground-level vegetation? OPTIONS: Yes - well labeled | Acceptable - minor issues | No - significant issues | Cannot assess | null | null | null | null | null | null | null | null | null | null | null | null |
Q5 β Overall Quality: Rate the overall annotation quality. 1=Very poor, 5=Excellent. OPTIONS: 1 | 2 | 3 | 4 | 5 | null | null | null | null | null | null | null | null | null | null | null | null |
Q6 β Tree Species: Can you identify the tree species from canopy shape, branching, and morphology? OPTIONS: Vachellia erioloba (camel thorn) | Boscia albitrunca (shepherd's tree) | Other (specify in Q10) | Uncertain | null | null | null | null | null | null | null | null | null | null | null | null |
Q6b β Species Confidence: How confident are you in your species identification? OPTIONS: Confident | Somewhat confident | Uncertain | null | null | null | null | null | null | null | null | null | null | null | null |
Q7 β Nest Activity: Estimate nest activity. Active=dense intact thatch. Abandoned=degraded/sparse. OPTIONS: Active | Likely active | Likely abandoned | Abandoned | Cannot assess | null | null | null | null | null | null | null | null | null | null | null | null |
Q8 β Occlusion Severity: How much of the nest is obscured by the tree canopy? OPTIONS: Low - mostly visible | Medium - partially occluded | High - heavily occluded | null | null | null | null | null | null | null | null | null | null | null | null |
null | null | null | null | null | null | null | null | null | null | null | null | null |
Q9 β Annotation Difficulty (optional): How difficult would it be for a human expert to annotate this sample? OPTIONS: Easy | Moderate | Hard | Very hard | null | null | null | null | null | null | null | null | null | null | null | null |
Q10 β Free Observations (optional): Any notable ecological or structural observations? e.g. unusual nest shape, multiple nests, damage, branch architecture. Free text. | null | null | null | null | null | null | null | null | null | null | null | null |
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End of preview.
NEST3D: A High-Resolution Multimodal Dataset of Sociable Weaver Tree Nests
Dataset Description
NEST3D is a multimodal dataset of 104 sociable weaver nests, combining drone-based RGB and multispectral imagery with a semantically annotated 3D RGB point cloud. It captures trees hosting these nests through drone-based remote sensing, providing rich spatial and spectral information to benchmark and advance scene-level semantic segmentation methods for computer vision and ecological monitoring applications.
Key Characteristics
- Modality: Multimodal (RGB imagery, multispectral bands, 3D point clouds)
- Task: Scene-level semantic segmentation
- 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 segmentation, 3d reconstruction.
Dataset Organization
The dataset is organized into modality-specific directories to support flexible access and reuse:
Directory Structure
NEST3D/
βββ images/
β βββ sample001.tar.gz
β β βββ (extracts to:)
β β βββ RGB/
β β β βββ sample001_RGB_001.JPG
β β β βββ ...
β β βββ MS/
β β βββ Green/
β β β βββ sample001_G_001.TIF
β β β βββ ...
β β βββ Red/
β β β βββ sample001_R_001.TIF
β β β βββ ...
β β βββ Red_Edge/
β β β βββ sample001_RE_001.TIF
β β β βββ ...
β β βββ NIR/
β β βββ sample001_NIR_001.TIF
β β βββ ...
β βββ sample002.tar.gz
β βββ ...
β
βββ reconstructions/
β βββ sample001/
β β βββ sample001.ply
β β βββ sample001_rgb_cameras.json
β β βββ sample001_ms_cameras.json
β β βββ sample001_summary.json
β βββ ...
β
βββ metadata/
β βββ (ecological metadata β coming soon)
β
βββ validation/
β βββ expert_validation_R1.csv
β βββ expert_validation_R2.csv
β
βββ scripts/
β βββ preprocess_step1_correct_ply.py
β βββ preprocess_step2_qc_plots.py
β βββ preprocess_step3_ptv3.py
β βββ preprocess_step4_o3dml.py
β βββ extract_metadata.py
β
βββ train.txt
βββ val.txt
βββ test.txt
Data Modalities
1. RGB Imagery
- Raw drone images from aerial acquisition
- Format: JPEG, packaged per scene as a compressed archive
- Example path:
images/sample001.tar.gzβRGB/sample001_RGB_119.JPG
2. Multispectral Imagery
- Four spectral bands from the same acquisitions as RGB
- 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 (inside
images/sample001.tar.gz):MS/Green/sample001_G_119.TIFMS/Red/sample001_R_119.TIFMS/Red_Edge/sample001_RE_119.TIFMS/NIR/sample001_NIR_119.TIF
3. 3D Point Clouds
- 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)
- red, green, blue: RGB color values (0β255)
- 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, BrownβConrady distortion coefficients) and extrinsics (camera-to-chunk / chunk-to-camera 4Γ4 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 β 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 | sample001βsample104 |
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 nestβtree 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) β 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 | β |
Data Splits
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.
Processing Scripts
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 β 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.plyfiles).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 pertrain.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
from plyfile import PlyData
import numpy as np
# Load point cloud with semantic labels
ply = PlyData.read('reconstructions/sample001/sample001.ply')
vertex = ply['vertex']
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
import tarfile
from PIL import Image
import numpy as np
import io
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())))
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')
multispectral = np.stack([green, red, red_edge, nir], axis=-1)
Using with Hugging Face Datasets Library
from datasets import load_dataset
# Load the dataset
dataset = load_dataset('NEST3D/dataset')
Downloading the Dataset
Option 1: Using Hugging Face Hub
pip install huggingface_hub
huggingface-cli download NEST3D/dataset --repo-type dataset --local-dir ./NEST3D
Dataset Information
- Number of Scenes: 104
- Total Points: 951.66M (mean 9.15M Β± 8.63M per scene; range 0.69Mβ68.39M)
- Total RGB Images: 25,172 (mean 242 per scene; range 19β562)
- 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: Binary PLY
- Annotation Type: Per-point semantic labels, plus expert biological validation (see above)
Citation
If you use NEST3D, please cite the dataset:
@misc{nest3d_dataset,
author = {Molina Catricheo, Constanza A. and Guo, Ting-Jia and May, Giacomo and Reinhard, Friedrich F. and Risse, Benjamin},
title = {{NEST3D}: A High-Resolution Multimodal Dataset of Sociable Weaver Tree Nests},
year = {2026},
publisher = {Hugging Face},
doi = {10.57967/hf/9225},
url = {https://huggingface.co/datasets/NEST3D/dataset},
note = {Version 1.0}
}
DOI: https://doi.org/10.57967/hf/9225
Acknowledgments
This work was funded by:
- European Union's Horizon Europe research and innovation programme through the Marie SkΕodowska-Curie project "WildDrone β Autonomous Drones for Nature Conservation" (grant agreement no. 101071224)
- EPSRC-funded "Autonomous Drones for Nature Conservation Missions" grant (EP/X029077/1)
- Swiss State Secretariat for Education, Research and Innovation (SERI) under contract number 22.00280
We extend our gratitude to our collaborators and field partners in Namibia for their invaluable support during data collection.
Contact & Support
For questions, issues, or contributions, please visit the dataset discussion forum.
Last Updated: July 2026 Dataset Version: 1.1
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