Image Segmentation
Diffusers
Safetensors
remote-sensing
change-detection
semantic-segmentation
diffusion
earth-observation
import torch
from diffusers import DiffusionPipeline
# switch to "mps" for apple devices
pipe = DiffusionPipeline.from_pretrained("ali97/noise2map", dtype=torch.bfloat16, device_map="cuda")
prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k"
image = pipe(prompt).images[0]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
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
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 for full training and evaluation instructions.
Citation
@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},
}
- Downloads last month
- 8