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README.md

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+---
+license: cc-by-nc-sa-4.0
+annotations_creators: []
+language: en
+size_categories:
+- n<1K
+task_categories:
+- image-segmentation
+task_ids:
+- semantic-segmentation
+- instance-segmentation
+pretty_name: SegFly (RGB-T pairs FiftyOne subset)
+tags:
+- fiftyone
+- group
+- aerial
+- thermal
+- rgb-thermal
+- multimodal
+- drone
+- remote-sensing
+---
+
+# SegFly — Aerial RGB-Thermal Segmentation (FiftyOne dataset)
+
+<p align="center">
+    <img
+        height="640"
+        alt="Preview of the SegFly subset in the FiftyOne App."
+        src="assets/SegFly-preview.webp"
+    />
+</p>
+
+A grouped [FiftyOne](https://docs.voxel51.com) dataset — a curated subset of **SegFly**
+(Gross et al., ECCV 2026) of pixel-aligned aerial **RGB-thermal (RGB-T) pairs** with
+semantic-segmentation masks and derived instance detections.
+
+- **This dataset:** [`Voxel51/SegFly`](https://huggingface.co/datasets/Voxel51/SegFly) — load directly with `load_from_hub`
+- **Loader (FiftyOne remote zoo):** [`github.com/Burhan-Q/SegFly`](https://github.com/Burhan-Q/SegFly)
+- **Original dataset:** [`markus-42/SegFly`](https://huggingface.co/datasets/markus-42/SegFly) · [Project page](https://markus-42.github.io/publications/2026/segfly/) · [arXiv](https://arxiv.org/abs/2603.17920) · [Source code](https://github.com/markus-42/SegFly)
+
+> This is an **unofficial** redistribution of a subset of SegFly for use with FiftyOne.
+> All credit for the dataset belongs to the original authors (see [Citation](#citation)).
+
+## Installation
+
+```bash
+pip install fiftyone huggingface_hub
+```
+
+`huggingface_hub` is used to download the media from Hugging Face.
+
+## Usage
+
+```python
+import fiftyone as fo
+from fiftyone.utils.huggingface import load_from_hub
+
+# Load this dataset directly from the Hub (470 RGB-T pair groups)
+dataset = load_from_hub("Voxel51/SegFly", persistent=True)
+
+# per-class instance counts / label filtering come from the `instances` field
+print(dataset.count_values("instances.detections.label"))
+
+session = fo.launch_app(dataset)
+```
+
+Or load it through the FiftyOne remote zoo loader (also supports `max_samples`):
+
+```python
+import fiftyone.zoo as foz
+
+dataset = foz.load_zoo_dataset(
+    "https://github.com/Burhan-Q/SegFly",
+    max_samples=100,  # optional; limits the number of samples
+)
+```
+
+Every group has a base `rgb` slice and a `thermal` slice, so the App's slice
+selector toggles `rgb`↔`thermal` on the **same** sample (like `quickstart-groups`).
+Segmentation masks render with the SegFly benchmark color scheme.
+
+## What's included (curated subset)
+
+A ~0.6 GB set of pixel-aligned RGB-T pairs:
+
+| Scene | Altitude | Modality | Split | Groups |
+| :--- | :--- | :--- | :--- | ---: |
+| `scene_03` | 30m | thermal (RGB-T pairs) | train | 470 |
+
+**Total: 470 groups / 940 samples** (470 `thermal` + 470 `rgb` slice samples).
+(The full SegFly release is 35,613 samples / 191 GB across 9 scenes; this dataset
+is the curated RGB-T pair subset only.)
+
+### Group model
+
+One group per thermal capture, with a **uniform** slice set (like `quickstart-groups`),
+base slice `rgb`:
+
+- slice `rgb` — the pixel-registered RGB frame (base)
+- slice `thermal` — the LWIR frame
+
+Both slices carry two label fields (sharing the aligned mask):
+
+- `ground_truth` — [`fo.Segmentation`](https://docs.voxel51.com/api/fiftyone.core.labels.html#fiftyone.core.labels.Segmentation), the semantic mask
+- `instances` — [`fo.Detections`](https://docs.voxel51.com/api/fiftyone.core.labels.html#fiftyone.core.labels.Detections) **derived** from the mask: countable classes
+  (Vehicle, Truck, Building, Roof, Ground Obstacle, Rock, Cable, Cable Tower, Crane, Person,
+  Bicycle) as one detection per connected region; amorphous classes (Road, Walkway, Dirt,
+  Gravel, Grass, Vegetation, Tree, Water, Parking Lot, Construction) as one per class. This is
+  what enables App per-class **filtering** and per-class **instance counts** (the semantic
+  `Segmentation` field alone cannot be filtered/counted by class).
+
+Because every group has both slices, toggling `rgb`↔`thermal` in the App stays on the
+same sample. Per-sample fields: `scene`, `altitude`, `modality`. Split is a sample tag.
+Note: `instances` are derived from the semantic masks via connected components (not source
+instance annotations); "stuff" classes are stored as a single region per image.
+
+### Reusing the instance derivation (e.g. on the full SegFly release)
+
+The `instances` field ships **precomputed** in this dataset. The derivation is also
+exposed as reusable functions in the loader repo, so you can apply the **same** stuff/thing
+logic to any SegFly semantic mask (including the full 191 GB `markus-42/SegFly`):
+
+```python
+import fiftyone as fo
+from fiftyone.utils.huggingface import load_from_hub
+
+import segfly  # from github.com/Burhan-Q/SegFly (add the cloned repo dir to your path)
+
+# a single mask -> instance detections
+dets = segfly.segmentation_to_instances(sample["ground_truth"])
+
+# or populate an `instances` field across a whole dataset (grouped or flat)
+full = load_from_hub(
+    "markus-42/SegFly",
+    format="ParquetFilesDataset",
+    ...,
+)
+segfly.add_instances(full)  # in_field="ground_truth", out_field="instances"
+
+print(full.count_values("instances.detections.label"))
+```
+
+`MASK_TARGETS` (class map) and `MASK_TYPES` (the stuff/thing split) are module-level
+constants you can inspect or override.
+
+## Classes
+
+The stored masks contain the **raw OccuFly class IDs (0–36)**. `mask_targets` names
+every ID that can appear:
+
+| ID | Name | | ID | Name | | ID | Name |
+| ---: | :-- | -- | ---: | :-- | -- | ---: | :-- |
+| 0 | Unlabeled | | 8 | Tree | | 17 | Roof |
+| 1 | Road | | 9 | Ground Obstacle | | 21 | Cable |
+| 2 | Walkway | | 10 | unknown_10 | | 22 | Cable Tower |
+| 3 | Dirt | | 11 | Person | | 33 | Parking Lot |
+| 4 | Gravel | | 12 | Bicycle | | 34 | Construction |
+| 5 | Rock | | 13 | Vehicle | | 35 | Crane |
+| 6 | Grass | | 14 | Water | | 36 | Truck |
+| 7 | Vegetation | | 16 | Building | | | |
+
+> **Note.** SegFly's published "**15 benchmark classes**" are a documented
+> post-processing remap that is **not** baked into the mask files:
+> `Rock(5)` and `Cable Tower(22)` → `Ground Obstacle(9)`; `Person(11)`,
+> `Bicycle(12)`, `Cable(21)`, `Crane(35)` → `Unlabeled(0)`. Apply this remap if
+> you need the benchmark protocol. `ID 10` appears in the data but is undocumented
+> in the source and is left un-named as `unknown_10`.
+
+## License & attribution
+
+SegFly is released under **[CC BY-NC-SA 4.0](https://creativecommons.org/licenses/by-nc-sa/4.0/)**
+(non-commercial, share-alike, attribution). This redistribution keeps the same license.
+Use is **non-commercial** only; you must attribute the original authors and share
+derivatives under the same terms.
+
+## Citation
+
+```bibtex
+@inproceedings{gross2026segfly,
+    title={{SegFly: A Dataset and 2D-3D-2D Paradigm for Aerial RGB-Thermal Semantic Segmentation at Scale}},
+    author={Markus Gross and Sai Bharadhwaj Matha and Rui Song and Viswanathan Muthuveerappan and Conrad Christoph and Julius Huber and Daniel Cremers},
+    booktitle = {Proceedings of the European Conference on Computer Vision (ECCV)},
+    year={2026},
+}
+```