README.md

---
tags:
- videos
- video
- uav
- drones
- multitask
- multimodal
---
# Dronescapes dataset

As 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)

![Logo](logo.png)

Note: We are extending this dataset in another repository. GT data for benchmarking is the same, but we are generating 
modalities as inputs: [dronescapes-2024](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.

## 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:

![Collage](collage.png)

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

#### IoU

| method | #paramters |  average | barsana_DJI_0500_0501_combined_sliced_2700_14700 | comana_DJI_0881_full |  norway_210821_DJI_0015_full |
|:-|:-|:-|:-|:-|:-|
| [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 |
| [NGC(LR)](https://openaccess.thecvf.com/content/ICCV2023W/LIMIT/papers/Marcu_Self-Supervised_Hypergraphs_for_Learning_Multiple_World_Interpretations_ICCVW_2023_paper.pdf) | 32M | 40.75  | 46.51 | 45.59 | 30.17 |
| [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 |

[^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).

#### F1 Score

| method | #paramters |  average | barsana_DJI_0500_0501_combined_sliced_2700_14700 | comana_DJI_0881_full |  norway_210821_DJI_0015_full |
|:-|:-|:-|:-|:-|:-|
| [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 |