DOI: 10.5281/zenodo.14994429
title: Pretraining data of SkySense++

description:  

This repository contains the data description and processing for the paper titled "SkySense++: A Semantic-Enhanced Multi-Modal Remote Sensing Foundation Model for Earth Observation." The code is in here



📢 Latest Updates


🔥🔥🔥 Last Updated on 2025.03.23 🔥🔥🔥



[2025.3.23] updated pretraining data list of representation-enhanced pretraining

[2025.3.14] updated optical images of JL-16 dataset (https://huggingface.co/datasets/KKKKKKang/JL-16)

[2025.3.12] updated sentinel-1 images and labels of JL-16 dataset 

[2025.3.9] updated pretrain and evaluation data




Pretrain Data




RS-Semantic Dataset


We conduct semantic-enhanced pretraining on the RS-Semantic dataset, which consists of 13 datasets with pixel-level annotations. Below are the specifics of these datasets.




Dataset
Modalities
GSD(m)
Size
Categories
Download Link




Five Billion Pixels
Gaofen-2
4
6800x7200
24
Download


Potsdam
Airborne
0.05
6000x6000
5
Download


Vaihingen
Airborne
0.05
2494x2064
5
Download


Deepglobe
WorldView
0.5
2448x2448
6
Download


iSAID
Multiple Sensors
-
800x800 to 4000x13000
15
Download


LoveDA
Spaceborne
0.3
1024x1024
7
Download


DynamicEarthNet
WorldView
0.3
1024x1024
7
Download


 
Sentinel-2*
10
32x32
 
 


 
Sentinel-1*
10
32x33
 
 


Pastis-MM
WorldView
0.3
1024x1024
18
Download


 
Sentinel-2*
10
32x32
 
 


 
Sentinel-1*
10
32x33
 
 


C2Seg-AB
Sentinel-2*
10
128x128
13
Download


 
Sentinel-1*
10
128x128
 
 


FLAIR
Spot-5
0.2
512x512
12
Download


 
Sentinel-2*
10
40x40
 
 


DFC20
Sentinel-2
10
256x256
9
Download


 
Sentinel-1
10
256x256
 
 


S2-naip
NAIP
1
512x512
32
Download


 
Sentinel-2*
10
64x64
 
 


 
Sentinel-1*
10
64x64
 
 


JL-16
Jilin-1
0.72
512x512
16
Download


 
Sentinel-1*
10
40x40
 
 




* for time-series data.

RS-Representation Dataset

The pretraining list is in the record rep_data_list.tar. The download and process scripts are in [Github](https://github.com/kang-wu/SkySensePlusPlus).



EO Benchmark


We evaluate our SkySense++ on 12 typical Earth Observation (EO) tasks across 7 domains: agriculture, forestry, oceanography, atmosphere, biology, land surveying, and disaster management. The detailed information about the datasets used for evaluation is as follows.




Domain
Task type
Dataset
Modalities
GSD
Image size
Download Link
Notes




Agriculture
Crop classification
Germany
Sentinel-2*
10
24x24
Download
 


Foresetry
Tree species classification
TreeSatAI-Time-Series
Airborne,
0.2
304x304
Download
 


 
 
 
Sentinel-2*
10
6x6
 
 


 
 
 
Sentinel-1*
10
6x6
 
 


 
Deforestation segmentation
Atlantic
Sentinel-2
10
512x512
Download
 


Oceanography
Oil spill segmentation
SOS
Sentinel-1
10
256x256
Download
 


Atmosphere
Air pollution regression
3pollution
Sentinel-2
10
200x200
Download
 


 
 
 
Sentinel-5P
2600
120x120
 
 


Biology
Wildlife detection
Kenya
Airborne
-
3068x4603
Download
 


Land surveying
LULC mapping
C2Seg-BW
Gaofen-6
10
256x256
Download
 


 
 
 
Gaofen-3
10
256x256
 
 


 
Change detection
dsifn-cd
GoogleEarth
0.3
512x512
Download
 


Disaster management
Flood monitoring
Flood-3i
Airborne
0.05
256 × 256
Download
 


 
 
C2SMSFloods
Sentinel-2, Sentinel-1
10
512x512
Download
 


 
Wildfire monitoring
CABUAR
Sentinel-2
10
5490 × 5490
Download
 


 
Landslide mapping
GVLM
GoogleEarth
0.3
1748x1748 ~ 10808x7424
Download
 


 
Building damage assessment
xBD
WorldView
0.3
1024x1024
Download
 




* for time-series data.