SegFly: A Dataset and 2D-3D-2D Paradigm for Aerial RGB-Thermal Semantic Segmentation at Scale

        

Following the acceptance of SegFly at the ECCV 2026 computer vision conference, we officially release our dataset on Hugging Face. Note that we also release our Firefly model, specialized for RGB and thermal semantic segmentation from aerial perspectives.

SegFly is a large-scale aerial semantic segmentation dataset featuring 20,606 high-resolution RGB images and 15,007 pixel-aligned RGB-Thermal (RGB-T) pairs. Images are captured across urban, industrial, and rural environments, spanning all seasons and three altitudes (30m, 40m, 50m).

Quickstart

Full dataset documentation available on SegFly GitHub.

from datasets import load_dataset

# Load entire dataset
dataset = load_dataset("markus-42/SegFly")

Features

Feature	Type	Description
image	Image	Raw sensor frame (RGB or LWIR Thermal)
label	Image	8-bit single-channel semantic mask mapped to 15 benchmark classes
RGB_aligned	Image	Registered RGB image (Thermal modality only; returns None for RGB modality)
scene	string	Scene identifier ("scene_01" to "scene_09")
altitude	string	Flight altitude ("30m", "40m", "50m")
modality	string	Sensor modality ("RGB" or "thermal")

Splits and Statistics

• Total Samples: 35,613 (20,606 RGB + 15,007 thermal)

Modality	Split	Scenes	Sample Count
RGB	Train	scene_01, scene_02, scene_03, scene_04, scene_05	14,738
	Val	scene_06, scene_07	1,965
	Test	scene_08, scene_09	3,842
Thermal	Train	scene_03, scene_04, scene_05	12,063
	Val/Test	scene_09	2,944

SegFly Dataset Class Mapping Reference

Class ID	Class Name	RGB Color	Color Preview
0	Unlabeled / Ignored	[0, 0, 0]	#000000
1	Road	[128, 0, 128]	#800080
2	Walkway	[204, 163, 72]	#cca348
3	Dirt	[128, 0, 0]	#800000
4	Gravel	[192, 192, 192]	#c0c0c0
6	Grass	[0, 255, 0]	#00ff00
7	Vegetation	[112, 148, 32]	#709420
8	Tree	[64, 64, 0]	#404000
9	Ground Obstacle	[255, 255, 0]	#ffff00
13	Vehicle	[0, 128, 128]	#008080
14	Water	[0, 0, 255]	#0000ff
16	Building	[255, 0, 0]	#ff0000
17	Roof	[64, 160, 120]	#40a078
33	Parking Lot	[128, 64, 128]	#804080
34	Construction	[240, 120, 120]	#f07878
36	Truck	[128, 128, 64]	#808040

Reference

@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},
}

License

This work is licensed under the CC BY-NC-SA 4.0 license. See the LICENSE file for the full legal terms.