| --- |
| tags: |
| - videos |
| - video |
| - uav |
| - drones |
| - multitask |
| - multimodal |
| --- |
| # Dronescapes dataset |
|
|
| Visit the official website for more information: [link](https://sites.google.com/view/dronescapes-dataset). This dataset was introduced in our ICCV 2023 workshop paper: [link](https://openaccess.thecvf.com/content/ICCV2023W/LIMIT/papers/Marcu_Self-Supervised_Hypergraphs_for_Learning_Multiple_World_Interpretations_ICCVW_2023_paper.pdf). For citing, see at the end of the page. |
|
|
|  |
|
|
| Note: An fully-automated extended variant of this dataset (generating new modalities as inputs) is available at this repository: [link](https://huggingface.co/datasets/Meehai/dronescapes-2024). |
|
|
| ## 1. Downloading the data |
|
|
| ``` |
| git lfs install # Make sure you have git-lfs installed (https://git-lfs.com) |
| git clone https://huggingface.co/datasets/Meehai/dronescapes |
| ``` |
|
|
| Note: the dataset has about 200GB, so it may take a while to clone it. |
|
|
| ## 1.2 Low level data for the dataset (GPS, camera rotation matrices) |
|
|
| <details> |
| <summary> Click to expand </summary> |
|
|
| ### 1.2.1 Convert Camera Normals to World Normals |
|
|
| This is an optional step, but for some use cases, it may be better to use world normals instead of camera normals, which |
| are provided by default in `normals_sfm_manual202204`. To convert, we provide camera rotation matrices in |
| `raw_data/camera_matrics.tar.gz` for all 8 scenes that also have SfM. |
|
|
| In order to convert, use this function (for each npz file): |
|
|
| ``` |
| def convert_camera_to_world(normals: np.ndarray, rotation_matrix: np.ndarray) -> np.ndarray: |
| normals = (normals.copy() - 0.5) * 2 # [-1:1] -> [0:1] |
| camera_normals = camera_normals @ np.linalg.inv(rotation_matrix) |
| camera_normals = (camera_normals / 2) + 0.5 # [0:1] => [-1:1] |
| return np.clip(camera_normals, 0.0, 1.0) |
| ``` |
|
|
| ### 1.2.2 Raw camera location |
|
|
| The GPS location (lat/long/height) is in the `raw_data/raw_camera_info.tar.gz`. Each file over there is an archive over the |
| original videos, so the train/val/test splits files from `raw_data/txt_files` must be used to map to proper indices. Each |
| archive contains a bunch of raw data, however the key `lat_long_height` should provide the GPS location. |
|
|
| Note: there is no GPS for the norway scene, the log seems to have been lost. |
|
|
| </details> |
|
|
| ## 2. Using the data |
|
|
| As per the split from the paper: |
|
|
| <summary> Split </summary> |
| <img src="split.png" width="500px"> |
|
|
| The data is in the `data*` directory with 1 sub-directory for each split above (and a few more variants). |
|
|
| The simplest way to explore the data is to use the [provided notebook](scripts/dronescapes_viewer.ipynb). Upon running |
| it, you should get a collage with all the default tasks, like this: |
|
|
|  |
|
|
| For a CLI-only method, you can use the provided reader as well: |
|
|
| ``` |
| python scripts/dronescapes_viewer.py data/test_set_annotated_only/ # or any of the 8 directories in data/ |
| ``` |
|
|
| <details> |
| <summary> Expected output </summary> |
|
|
| ``` |
| [MultiTaskDataset] |
| - Path: '/export/home/proiecte/aux/mihai_cristian.pirvu/datasets/dronescapes/data/test_set_annotated_only' |
| - Tasks (11): [DepthRepresentation(depth_dpt), DepthRepresentation(depth_sfm_manual202204), DepthRepresentation(depth_ufo), ColorRepresentation(edges_dexined), EdgesRepresentation(edges_gb), NpzRepresentation(normals_sfm_manual202204), OpticalFlowRepresentation(opticalflow_rife), ColorRepresentation(rgb), SemanticRepresentation(semantic_mask2former_swin_mapillary_converted), SemanticRepresentation(semantic_segprop8), ColorRepresentation(softseg_gb)] |
| - Length: 116 |
| - Handle missing data mode: 'fill_none' |
| == Shapes == |
| {'depth_dpt': torch.Size([540, 960]), |
| 'depth_sfm_manual202204': torch.Size([540, 960]), |
| 'depth_ufo': torch.Size([540, 960, 1]), |
| 'edges_dexined': torch.Size([540, 960]), |
| 'edges_gb': torch.Size([540, 960, 1]), |
| 'normals_sfm_manual202204': torch.Size([540, 960, 3]), |
| 'opticalflow_rife': torch.Size([540, 960, 2]), |
| 'rgb': torch.Size([540, 960, 3]), |
| 'semantic_mask2former_swin_mapillary_converted': torch.Size([540, 960, 8]), |
| 'semantic_segprop8': torch.Size([540, 960, 8]), |
| 'softseg_gb': torch.Size([540, 960, 3])} |
| == Random loaded item == |
| {'depth_dpt': tensor[540, 960] n=518400 (2.0Mb) x∈[0.043, 1.000] μ=0.341 σ=0.418, |
| 'depth_sfm_manual202204': None, |
| 'depth_ufo': tensor[540, 960, 1] n=518400 (2.0Mb) x∈[0.115, 0.588] μ=0.297 σ=0.138, |
| 'edges_dexined': tensor[540, 960] n=518400 (2.0Mb) x∈[0.000, 0.004] μ=0.003 σ=0.001, |
| 'edges_gb': tensor[540, 960, 1] n=518400 (2.0Mb) x∈[0., 1.000] μ=0.063 σ=0.100, |
| 'normals_sfm_manual202204': None, |
| 'opticalflow_rife': tensor[540, 960, 2] n=1036800 (4.0Mb) x∈[-0.004, 0.005] μ=0.000 σ=0.000, |
| 'rgb': tensor[540, 960, 3] n=1555200 (5.9Mb) x∈[0., 1.000] μ=0.392 σ=0.238, |
| 'semantic_mask2former_swin_mapillary_converted': tensor[540, 960, 8] n=4147200 (16Mb) x∈[0., 1.000] μ=0.125 σ=0.331, |
| 'semantic_segprop8': tensor[540, 960, 8] n=4147200 (16Mb) x∈[0., 1.000] μ=0.125 σ=0.331, |
| 'softseg_gb': tensor[540, 960, 3] n=1555200 (5.9Mb) x∈[0., 0.004] μ=0.002 σ=0.001} |
| == Random loaded batch == |
| {'depth_dpt': tensor[5, 540, 960] n=2592000 (9.9Mb) x∈[0.043, 1.000] μ=0.340 σ=0.417, |
| 'depth_sfm_manual202204': tensor[5, 540, 960] n=2592000 (9.9Mb) NaN!, |
| 'depth_ufo': tensor[5, 540, 960, 1] n=2592000 (9.9Mb) x∈[0.115, 0.588] μ=0.296 σ=0.137, |
| 'edges_dexined': tensor[5, 540, 960] n=2592000 (9.9Mb) x∈[0.000, 0.004] μ=0.003 σ=0.001, |
| 'edges_gb': tensor[5, 540, 960, 1] n=2592000 (9.9Mb) x∈[0., 1.000] μ=0.063 σ=0.102, |
| 'normals_sfm_manual202204': tensor[5, 540, 960, 3] n=7776000 (30Mb) NaN!, |
| 'opticalflow_rife': tensor[5, 540, 960, 2] n=5184000 (20Mb) x∈[-0.004, 0.006] μ=0.000 σ=0.000, |
| 'rgb': tensor[5, 540, 960, 3] n=7776000 (30Mb) x∈[0., 1.000] μ=0.393 σ=0.238, |
| 'semantic_mask2former_swin_mapillary_converted': tensor[5, 540, 960, 8] n=20736000 (79Mb) x∈[0., 1.000] μ=0.125 σ=0.331, |
| 'semantic_segprop8': tensor[5, 540, 960, 8] n=20736000 (79Mb) x∈[0., 1.000] μ=0.125 σ=0.331, |
| 'softseg_gb': tensor[5, 540, 960, 3] n=7776000 (30Mb) x∈[0., 0.004] μ=0.002 σ=0.001} |
| == Random loaded batch using torch DataLoader == |
| {'depth_dpt': tensor[5, 540, 960] n=2592000 (9.9Mb) x∈[0.025, 1.000] μ=0.216 σ=0.343, |
| 'depth_sfm_manual202204': tensor[5, 540, 960] n=2592000 (9.9Mb) x∈[0., 1.000] μ=0.562 σ=0.335 NaN!, |
| 'depth_ufo': tensor[5, 540, 960, 1] n=2592000 (9.9Mb) x∈[0.100, 0.580] μ=0.290 σ=0.128, |
| 'edges_dexined': tensor[5, 540, 960] n=2592000 (9.9Mb) x∈[0.000, 0.004] μ=0.003 σ=0.001, |
| 'edges_gb': tensor[5, 540, 960, 1] n=2592000 (9.9Mb) x∈[0., 1.000] μ=0.079 σ=0.116, |
| 'normals_sfm_manual202204': tensor[5, 540, 960, 3] n=7776000 (30Mb) x∈[0.000, 1.000] μ=0.552 σ=0.253 NaN!, |
| 'opticalflow_rife': tensor[5, 540, 960, 2] n=5184000 (20Mb) x∈[-0.013, 0.016] μ=0.000 σ=0.004, |
| 'rgb': tensor[5, 540, 960, 3] n=7776000 (30Mb) x∈[0., 1.000] μ=0.338 σ=0.237, |
| 'semantic_mask2former_swin_mapillary_converted': tensor[5, 540, 960, 8] n=20736000 (79Mb) x∈[0., 1.000] μ=0.125 σ=0.331, |
| 'semantic_segprop8': tensor[5, 540, 960, 8] n=20736000 (79Mb) x∈[0., 1.000] μ=0.125 σ=0.331, |
| 'softseg_gb': tensor[5, 540, 960, 3] n=7776000 (30Mb) x∈[0., 0.004] μ=0.002 σ=0.001} |
| ``` |
| </details> |
|
|
| ## 3. Evaluation for semantic segmentation |
|
|
| We evaluate in the paper on the 3 test scenes (unsees at train) as well as the semi-supervised scenes (seen, but |
| different split) against the human annotated frames. The general evaluation script is in |
| `scripts/evaluate_semantic_segmentation.py`. |
|
|
| General usage is: |
| ``` |
| python scripts/evaluate_semantic_segmentation.py y_dir gt_dir -o results.csv --classes C1 C2 .. Cn |
| [--class_weights W1 W2 ... Wn] [--scenes s1 s2 ... sm] |
| ``` |
|
|
| <details> |
| <summary> Script explanation </summary> |
| The script is a bit convoluted, so let's break it into parts: |
|
|
| - `y_dir` and `gt_dir` Two directories of .npz files in the same format as the dataset |
| - y_dir/1.npz, ..., y_dir/N.npz |
| - gt_dir/1.npz, ..., gt_dir.npz |
| - `classes` A list of classes in the order that they appear in the predictions and gt files |
| - `class_weights` (optional, but used in paper) How much to weigh each class. In the paper we compute these weights as |
| the number of pixels in all the dataset (train/val/semisup/test) for each of the 8 classes resulting in the numbers |
| below. |
| - `scenes` if the `y_dir` and `gt_dir` contains multiple scenes that you want to evaluate separately, the script allows |
| you to pass the prefix of all the scenes. For example, in `data/test_set_annotated_only/semantic_segprop8/` there are |
| actually 3 scenes in the npz files and in the paper, we evaluate each scene independently. Even though the script |
| outputs one csv file with predictions for each npz file, the scenes are used for proper aggregation at scene level. |
| </details> |
|
|
| <details> |
| <summary> Reproducing paper results for Mask2Former </summary> |
|
|
| ``` |
| python scripts/evaluate_semantic_segmentation.py \ |
| data/test_set_annotated_only/semantic_mask2former_swin_mapillary_converted/ \ # Mask2Former example, use yours here! |
| data/test_set_annotated_only/semantic_segprop8/ \ |
| -o results.csv \ |
| --classes land forest residential road little-objects water sky hill \ |
| --class_weights 0.28172092 0.30589653 0.13341699 0.05937348 0.00474491 0.05987466 0.08660721 0.06836531 \ |
| --scenes barsana_DJI_0500_0501_combined_sliced_2700_14700 comana_DJI_0881_full norway_210821_DJI_0015_full |
| ``` |
|
|
| Should output: |
| ``` |
| scene iou f1 |
| barsana_DJI_0500_0501_combined_sliced_2700_14700 63.371 75.338 |
| comana_DJI_0881_full 60.559 73.779 |
| norway_210821_DJI_0015_full 37.986 45.939 |
| mean 53.972 65.019 |
| |
| ``` |
|
|
| Not providing `--scenes` will make an average across all 3 scenes (not average after each metric individually): |
|
|
| ``` |
| iou f1 |
| scene |
| all 60.456 73.261 |
| ``` |
| </details> |
|
|
| ### 3.1 Official benchmark |
|
|
| #### Semantic Segmentation |
|
|
| ##### Weighted Mean IoU (official metric) |
|
|
| | Model | #paramters | Score (↑) | Barsana (scene 1) | Comana (scene 2) | Norway (scene 3) | |
| |:-|:-|:-|:-|:-|:-| |
| | [PHG-MAE-Distil](https://arxiv.org/pdf/2510.10068){^2} | 4.4M | 56.27 | 66.34 | 61.11 | 37.69 | |
| | [PHG-MAE](https://arxiv.org/pdf/2510.10068) | 4.4M | 55.32 | 63.80 | 63.18 | 38.98 | |
| | [Mask2Former](https://openaccess.thecvf.com/content/CVPR2022/papers/Cheng_Masked-Attention_Mask_Transformer_for_Universal_Image_Segmentation_CVPR_2022_paper.pdf) | 216M | 53.97 | 63.37 | 60.55 | 37.98 | |
| | [NHG(LR)](https://openaccess.thecvf.com/content/ICCV2023W/LIMIT/papers/Marcu_Self-Supervised_Hypergraphs_for_Learning_Multiple_World_Interpretations_ICCVW_2023_paper.pdf) | 32M | 40.76 | 46.51 | 45.59 | 30.17 | |
| | [NHG-Distil](https://openaccess.thecvf.com/content/ICCV2023W/LIMIT/papers/Marcu_Self-Supervised_Hypergraphs_for_Learning_Multiple_World_Interpretations_ICCVW_2023_paper.pdf) | 4.4M | 40.31 | n/a | n/a | n/a | |
| | [CShift](https://www.bmvc2021-virtualconference.com/assets/papers/0455.pdf){^1} | n/a | 39.67 | 46.27 | 43.67 | 29.09 | |
| | [NGC](https://cdn.aaai.org/ojs/16283/16283-13-19777-1-2-20210518.pdf){^1} | 32M | 35.32 | 44.34 | 38.99 | 22.63 | |
| | [SafeUAV](https://openaccess.thecvf.com/content_ECCVW_2018/papers/11130/Marcu_SafeUAV_Learning_to_estimate_depth_and_safe_landing_areas_for_ECCVW_2018_paper.pdf){^1} | 1.1M | 32.79 | n/a | n/a | n/a | |
|
|
| ##### F1 Score |
|
|
| | Model | #paramters | Score (↑) | Barsana (scene 1) | Comana (scene 2) | Norway (scene 3) | |
| |:-|:-|:-|:-|:-|:-| |
| | [PHG-MAE](https://arxiv.org/pdf/2510.10068) | 4.4M | 66.09 | 75.98 | 76.12 | 46.18 | |
| | [PHG-MAE-Distil](https://arxiv.org/pdf/2510.10068)^{2} | 4.4M | 65.60 | 77.21 | 74.47 | 45.13 | |
| | [Mask2Former](https://openaccess.thecvf.com/content/CVPR2022/papers/Cheng_Masked-Attention_Mask_Transformer_for_Universal_Image_Segmentation_CVPR_2022_paper.pdf) | 216M | 65.01 | 75.33 | 73.77 | 45.93 | |
|
|
| ##### Mean IoU (simplified) |
|
|
| TODO: provide a simplified (non-weighted) metric which can be used on the entire dataset at once :) |
|
|
| #### Depth estimation |
|
|
| Score is L1 error in meters. |
|
|
| | Model | #paramters | Score (↓) | |
| |:-|:-|:-| |
| | [PHG-MAE](https://arxiv.org/pdf/2510.10068) | 4.4M | 16.13 | |
| | [NHG-Distil](https://arxiv.org/pdf/2510.10068) | 4.4M | 16.64 | |
| | [SafeUAV](https://arxiv.org/pdf/2510.10068) | 4.4M | 21.66 | |
|
|
| #### Camera normals estimation |
|
|
| Score is L1 score (in angles) * 100. |
|
|
| | Model | #paramters | Score (↓) | |
| |:-|:-|:-| |
| | [NHG-Distil](https://arxiv.org/pdf/2510.10068) | 4.4M | 11.71 | |
| | [PHG-MAE](https://arxiv.org/pdf/2510.10068) | 4.4M | 12.35 | |
| | [SafeUAV](https://arxiv.org/pdf/2510.10068) | 4.4M | 12.40 | |
|
|
|
|
| #### Multi-task results |
|
|
| One model, three tasks at once. For semantic segmentation, we use Weighted Mean IoU. For the others, the same metric as above. |
|
|
| TODO: combine the three metrics into a single unweighted number. (semantic is [0:100], depth is [0:300] ? and normals is [0:180] or [0:360]). |
|
|
| | Model | #paramters | Semantic Segmentation (↑) | Depth Estimation (↓) | Camera Normals Estimaton (↓) |
| |:-|:-|:-|:-|:- |
| | [PHG-MAE](https://arxiv.org/pdf/2510.10068) | 4.4M | 49.74 | 21.60 | 12.80 | |
|
|
| #### Real-time semantic segmentation results |
|
|
| For the real time models, the authors need to provide a self-contained Google Colab notebook. The models must run at >=5FPS on a T4 GPU (free Google Colab instance) for the Dronescapes-Test (or similar) images at 960x540 resolution. We provide an example for the PHG-MAE-Distil 430k parameters model: [link](https://colab.research.google.com/drive/1-Zc7qEC7k7KnCuq-LOnNy9hoMfQBJA9O?usp=sharing). |
|
|
| | Model | #paramters | Score (↑) | FPS (↓) | Google Colab |
| |:-|:-|:-|:-|:- |
| | [PHG-MAE](https://arxiv.org/pdf/2510.10068) | 430k | 55.06 | 5.40 | [link](https://colab.research.google.com/drive/1-Zc7qEC7k7KnCuq-LOnNy9hoMfQBJA9O?usp=sharing) | |
|
|
| Notes: |
| - 1: reported in the [Dronescapes paper](https://openaccess.thecvf.com/content/ICCV2023W/LIMIT/papers/Marcu_Self-Supervised_Hypergraphs_for_Learning_Multiple_World_Interpretations_ICCVW_2023_paper.pdf). |
| - 2: Google colab [link](https://colab.research.google.com/drive/1-Zc7qEC7k7KnCuq-LOnNy9hoMfQBJA9O#scrollTo=TR5G9bBhY9_k) for inference (only distiled version). |
|
|
| ## 4. Citation |
|
|
| To cite this work, use this: |
|
|
| ``` |
| @InProceedings{Marcu_2023_ICCV, |
| author = {Marcu, Alina and Pirvu, Mihai and Costea, Dragos and Haller, Emanuela and Slusanschi, Emil and Belbachir, Ahmed Nabil and Sukthankar, Rahul and Leordeanu, Marius}, |
| title = {Self-Supervised Hypergraphs for Learning Multiple World Interpretations}, |
| booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) Workshops}, |
| month = {October}, |
| year = {2023}, |
| pages = {983-992} |
| } |
| ``` |
|
|
| and |
|
|
| ``` |
| @misc{mihaicristian2025probabilistichypergraphsusingmultiple, |
| title={Probabilistic Hyper-Graphs using Multiple Randomly Masked Autoencoders for Semi-supervised Multi-modal Multi-task Learning}, |
| author={Pîrvu Mihai-Cristian and Leordeanu Marius}, |
| year={2025}, |
| eprint={2510.10068}, |
| archivePrefix={arXiv}, |
| primaryClass={cs.CV}, |
| url={https://arxiv.org/abs/2510.10068}, |
| } |
| ``` |
|
|
