Doodleverse/Segmentation Zoo/Seg2Map Res-UNet models for DeepGlobe/7-class segmentation of RGB 512x512 high-res. images

These Residual-UNet model data are based on the [DeepGlobe dataset](https://www.kaggle.com/datasets/balraj98/deepglobe-land-cover-classification-dataset)

Models have been created using Segmentation Gym* using the following dataset**: https://www.kaggle.com/datasets/balraj98/deepglobe-land-cover-classification-dataset

Image size used by model: 512 x 512 x 3 pixels

classes:
1. urban
2. agricultural
3. rangeland
4. forest
5. water
6. bare
7. unknown

File descriptions

For each model, there are 5 files with the same root name:

1. '.json' config file: this is the file that was used by Segmentation Gym* to create the weights file. It contains instructions for how to make the model and the data it used, as well as instructions for how to use the model for prediction. It is a handy wee thing and mastering it means mastering the entire Doodleverse.

2. '.h5' weights file: this is the file that was created by the Segmentation Gym* function `train_model.py`. It contains the trained model's parameter weights. It can called by the Segmentation Gym* function `seg_images_in_folder.py`. Models may be ensembled.

3. '_modelcard.json' model card file: this is a json file containing fields that collectively describe the model origins, training choices, and dataset that the model is based upon. There is some redundancy between this file and the `config` file (described above) that contains the instructions for the model training and implementation. The model card file is not used by the program but is important metadata so it is important to keep with the other files that collectively make the model and is such is considered part of the model

4. '_model_history.npz' model training history file: this numpy archive file contains numpy arrays describing the training and validation losses and metrics. It is created by the Segmentation Gym function `train_model.py`

5. '.png' model training loss and mean IoU plot: this png file contains plots of training and validation losses and mean IoU scores during model training. A subset of data inside the .npz file. It is created by the Segmentation Gym function `train_model.py`

Additionally, BEST_MODEL.txt contains the name of the model with the best validation loss and mean IoU

References
*Segmentation Gym: Buscombe, D., & Goldstein, E. B. (2022). A reproducible and reusable pipeline for segmentation of geoscientific imagery. Earth and Space Science, 9, e2022EA002332. https://doi.org/10.1029/2022EA002332 See: https://github.com/Doodleverse/segmentation_gym

**Demir, I., Koperski, K., Lindenbaum, D., Pang, G., Huang, J., Basu, S., Hughes, F., Tuia, D. and Raskar, R., 2018. Deepglobe 2018: A challenge to parse the earth through satellite images. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops (pp. 172-181).

Dataset

This dataset was created by hehe

Released under Data files © Original Authors

Contents

Dataset

This dataset was created by huada huang

Released under Database: Open Database, Contents: © Original Authors

Contents

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

🔥🔥🔥 Last Updated on 2025.03.14 🔥🔥🔥

• [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

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.

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.