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library_name: diffusers
license: mit
tags:
- remote-sensing
- change-detection
- semantic-segmentation
- diffusion
- earth-observation
pipeline_tag: image-segmentation
---
# Noise2Map — Pretrained Backbones
Pretrained denoising UNet backbones for **Noise2Map: End-to-End Diffusion Model for Semantic Segmentation and Change Detection** (IEEE TGRS 2026).
> Ali Shibli, Andrea Nascetti, Yifang Ban — KTH Royal Institute of Technology
[[GitHub]](https://github.com/alishibli97/noise2map)
---
## Checkpoints
| Subfolder | Description |
|---|---|
| `aid-10k` | Pretrained on 10k AID aerial images (**recommended**) |
| `sat2gen` | Pretrained on MajorTOM Sentinel-2 satellite imagery |
| `imagenet2gen` | ImageNet pretrained |
| `ddpm-church` | Google DDPM church-256 |
---
## Usage
```python
from noise2map import Noise2Map
model = Noise2Map(
in_channels=6, # 3 for semantic segmentation
out_channels=2,
img_scale=256,
pretrained="aid_google_minmaxnorm",
)
```
See the [GitHub repo](https://github.com/alishibli97/noise2map) for full training and evaluation instructions.
---
## Citation
```bibtex
@article{shibli2025noise2map,
title = {Noise2Map: End-to-End Diffusion Model for Semantic Segmentation and Change Detection},
author = {Shibli, Ali and Nascetti, Andrea and Ban, Yifang},
journal = {IEEE Transactions on Geoscience and Remote Sensing},
year = {2026},
}
``` |