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README.md

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----
-license: cc-by-4.0
----
+---
+license: cc-by-4.0
+language:
+- en
+tags:
+- LiDAR
+- Image
+- Fusion
+- Electrical
+pretty_name: GridNet-HD
+size_categories:
+- 1B<n<10B
+---
+# 🗂 GridNet-HD dataset
+
+## 1. Introduction
+
+This dataset was developed for **3D semantic segmentation tasks** using both **images and 3D point clouds** specialized on electrical infrastructure.
+**Grid** (electrical) **Net**work at **H**igh **D**ensity and High Resolution represents the first Image+LiDAR dataset accurately co-referenced in the electrical infrastructure domain.
+This dataset is associated with a public leaderboard hosted on Hugging Face Spaces, available at: [leaderboard](https://huggingface.co/spaces/heig-vd-geo/GridNet-HD-Leaderboard).
+
+The dataset is associated with the following paper:
+
+> **Title**: GridNet-HD: A High-Resolution Multi-Modal Dataset for LiDAR-Image Fusion on Power Line Infrastructure
+
+> **Authors**: Masked for instance
+
+> **Conference**: Submitted to NeurIPS 2025 
+
+
+This repository hosts the official data splits and resources used in the experiments reported in the paper.
+
+---
+
+## 2. Dataset Structure
+
+This dataset consists of 36 geographic zones, each represented by a folder named after its area code (e.g. t1z4, t1z5a, etc.).
+
+Each zone contains aligned multimodal data (images, segmentation masks, LiDAR point cloud, and camera parameters), enabling high-precision image-to-3D projection for multimodal fusion 3D semantic segmentation task.
+
+A split.json file at the root of the dataset defines the official train/test partition of the zones.
+
+To ensure fair evaluation on the **official test set**, ground truth annotations are not provided for either the images or the LiDAR point clouds.
+Instead, participants must submit their predictions to the [leaderboard](https://huggingface.co/spaces/heig-vd-geo/GridNet-HD-Leaderboard), where the official metrics (e.g., mIoU) are automatically computed against the hidden labels.
+
+### 📁 Folder layout
+```
+dataset-root/
+├── t1z5b/
+│   ├── images/           # RGB images (.JPG)
+│   ├── masks/            # Semantic segmentation masks (.png, single-channel label)
+│   ├── lidar/            # LiDAR point cloud (.las format with field "ground_truth")
+│   └── pose/             # Camera poses and intrinsics (text files)
+├── t1z6a/
+│   ├── ...
+├── ...
+├── split.json            # JSON file specifying the train/test split
+└── README.md
+```
+### 🧾 Contents per zone
+
+Inside each zone folder, you will find:
+- 📷 images/
+	- High-resolution RGB images (.JPG)
+	- Captured from a UAV
+
+- 🏷️ masks/
+	- One .png mask per image, same filename as image
+	- Label-encoded masks (1 channel)
+
+- 🌍 lidar/
+	- Single .las file for the entire zone captured from a UAV
+	- Contains 3D point cloud data at high denisty with semantic ground_truth labels (stored in field named "ground_truth")
+
+- 📌 pose/
+	- camera_pose.txt: Camera positions and orientations per image (using Metashape Agisoft convention, more details in paper)
+	- camera_calibration.xml: Camera calibration parameters (using Metashape Agisoft calibration model)
+
+---
+
+## 3. Class Grouping
+
+Original classes have been grouped into **12 semantic groups** as follows:
+
+| Group ID | Original Classes  | Description                       |
+|:--------:|:-----------------:|:---------------------------------:|
+| 0        | 0,1,2,3,4         |   Pylon                           |
+| 1        | 5                 |   Conductor cable                 |
+| 2        | 6,7               |   Structural cable                |
+| 3        | 8,9,10,11         |   Insulator                       |
+| 4        | 14                |   Tall vegetation                 |
+| 5        | 15                |   Short vegetation                |
+| 6        | 16                |   Hebarceous vegetation           |
+| 7        | 17,18             |   Rock, gravel, soil              |
+| 8        | 19                |   Impervious soil (Road)          |
+| 9        | 20                |   Water                           |
+| 10       | 21                |   Building                        |
+| 255      | 12,13,255         |   Unassigned-Unlabeled            |
+
+If interested the original classes are described in the Appendices of the paper.
+
+> 📝 Note: group `(12,13,255)` is **ignored during official evaluations**.
+
+---
+
+## 4. Dataset Splits
+
+The dataset is split into two parts:
+- **Train** (~70% of LiDAR points)
+- **Test** (~30% of LiDAR points)
+
+The splits were carefully constructed to guarantee:
+- **Full coverage of all semantic groups** (except the ignored group)
+- **No project overlap** between train and test
+- **Balanced distribution** in terms of class representation
+
+Project assignments are listed in `split.json` with a proposal of split train/val.
+
+**Note** that the test set give only the LiDAR without labels (without ground_truth field) and without mask labeled for images, this label part is keep by us in private mode for leaderboard management. To submit results on test set and obtain mIoU score on leaderboard, please follow instructions here: [leaderboard](https://huggingface.co/spaces/heig-vd-geo/GridNet-HD-Leaderboard) on the remap classes presented below.
+
+---
+
+## 5. Dataset Statistics
+
+### 📈 Class Distribution
+
+The number of points per semantic group across `train` and `test` splits is summarized here:
+
+| Group ID | Train Points | Test Points | Total points | % test/total | Distribution classes in train set | Distribution classes in test set |
+|:--------:|:------------:|:-----------:|:------------:|:------------:|:---------------------------------:|:--------------------------------:|
+| 0        | 11'490'104   | 3'859'573   | 15'349'677   | 25.1         | 0.7                               | 0.5                              |
+| 1        | 7'273'270    | 3'223'720   | 10'496'990   | 30.7         | 0.4                               | 0.4                              |
+| 2        | 1'811'422    | 903'089     | 2'714'511    | 33.3         | 0.1                               | 0.1                              |
+| 3        | 821'712      | 230'219     |1'051'931     | 21.9         | 0.05                              | 0.03                             |
+| 4        | 278'527'781  | 135'808'699 |414'336'480   | 32.8         | 16.5                              | 17.9                             |
+| 5        | 78'101'152   | 37'886'731  |115'987'883   | 32.7         | 4.6                               | 5.0                              |
+| 6        | 1'155'217'319| 461'212'378 | 1'616'429'697| 28.5         | 68.4                              | 60.7                             |
+| 7        | 135'026'058  | 99'817'139  | 234'843'197  | 42.5         | 8.0                               | 13.1                             |
+| 8        | 13'205'411   | 12'945'414  | 26'150'825   | 49.5         | 0.8                               | 1.7                              |
+| 9        | 1'807'216    | 1'227'892   | 3'035'108    | 40.5         | 0.1                               | 0.2                              |
+| 10       | 6'259'260    | 2'107'391   | 8'366'651    | 25.2         | 0.4                               | 0.3                              |
+| **TOTAL**| 1'689'540'705| 759'222'245 | 2'448'762'950| 31.0         | 100                               | 100                              |
+
+
+The proposed split for train/val repartition:
+| Group ID | Train Points | Val Points  | Total points | % val/total  | Distribution classes in train set | Distribution classes in val set  |
+|:--------:|:------------:|:-----------:|:------------:|:------------:|:---------------------------------:|:--------------------------------:|
+| 0        | 8'643'791    | 2'846'313   | 11'490'104   | 24.8         | 0.7                               | 0.7                              |
+| 1        | 5'782'668    | 1'490'602   | 7'273'270    | 20.5         | 0.4                               | 0.4                              |
+| 2        | 1'370'331    | 441'091     | 1'811'422    | 24.4         | 0.1                               | 0.1                              |
+| 3        | 625'937      | 195'775     | 821'712      | 23.8         | 0.05                              | 0.05                             |
+| 4        | 160'763'512  | 117'764'269 | 278'527'781  | 42.3         | 12.4                              | 29.7                             |
+| 5        | 43'442'079   | 34'659'073  | 78'101'152   | 44.4         | 3.4                               | 8.7                              |
+| 6        | 968'689'542  | 186'527'777 | 1'155'217'319| 16.1         | 74.9                              | 47.0                             |
+| 7        | 87'621'550   | 47'404'508  | 135'026'058  | 35.1         | 6.8                               | 11.9                             |
+| 8        | 10'420'302   | 2'785'109   | 13'205'411   | 21.1         | 0.8                               | 0.7                              |
+| 9        | 310'240      | 1'496'976   | 1'807'216    | 82.8         | 0.02                              | 0.4                              |
+| 10       | 4'793'225    | 1'466'035   | 6'259'260    | 23.4         | 0.4                               | 0.4                              |
+| **TOTAL**|1'292'463'177 | 397'077'528 | 1'689'540'705| 23.5         | 100                               | 100                              |
+
+### 📈 Class Distribution Visualisation
+
+
+---
+
+## 6. How to Use
+
+### Download via Hugging Face Hub
+
+```python
+from datasets import load_dataset
+
+dataset = load_dataset("heig-vd-geo/GridNet-HD")
+```
+
+Input/Target Format
+
+    Input RGB image: .JPG
+
+    Input LiDAR: .las with ground_truth field, values 0-21 + 255 corresponding to original classes (adaptation to 0-10 +255 needs to be coded in dataloader).
+
+    Input mask: 1-channel .png, values 0–21 + 255 corresponding to original classes (adaptation to 0-10 +255 needs to be coded in dataloader).
+    
+    Target LiDAR for compatibility with the leaderboard : only 9 test .las file same as input with a new "classif" field with group_id same as described above (0-10, 255 not used in the mIoU).
+
+Quick Example:
+
+
+---
+
+## 7. Running baselines
+
+Please follow instructions on dedicated git repository for models running on this dataset: [label](git_url)
+
+Results are visible here with the 3 different baselines:
+
+
+---
+
+## 8. License and Citation
+
+This dataset is released under the CC-BY-4.0 license.
+
+If you use this dataset, please cite the following paper:
+
+    [GridNet-HD: A High-Resolution Multi-Modal Dataset for LiDAR-Image Fusion on Power Line Infrastructure]
+    [A. Carreaud, S. Li. M. De-Lacour, D. Frinde, J. Skaloud, A. Gressin]
+    In Proceedings of NeurIPS 2025.

split.json

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+{
+    "train": [
+        "t1z6a",
+        "t1z6b",
+        "t2z5",
+        "t3z3",
+        "t3z6",
+        "t3z7",
+        "t5a1",
+        "t5a3",
+        "t5a4",
+        "t5a5",
+        "t5b2",
+        "t5b3",
+        "t5b4",
+        "t5b6",
+        "t5c1",
+        "t5c2",
+        "t5c3",
+        "t6z2",
+        "t6z3",
+        "t6z4",
+        "t6z6"
+    ],
+    "val": [
+        "t1z5b",
+        "t1z8",
+        "t3z4",
+        "t4z1",
+        "t5b1",
+        "t5b5"
+    ],
+    "test": [
+        "t1z4",
+        "t1z5a",
+        "t1z7",
+        "t3z1",
+        "t3z2",
+        "t3z5",
+        "t5a2",
+        "t6z1",
+        "t6z5"
+    ]
+}