---
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},
}
```