metadata
license: apache-2.0
tags:
- terravision
- terramind
- tokenizer
- vqvae
- fsq
- divae
- geospatial
- remote-sensing
- ahn
library_name: terratorch
TerraVision Tokenizer — AHN
A DiVAE (Diffusion VQ-VAE) tokenizer for Fused AHN6 DSM + DTM elevation (2-channel, float32) trained on Dutch national geospatial data. Part of the TerraVision-NL project.
Architecture
| Component | Value |
|---|---|
| Encoder | ViT-B (vit_b_enc) |
| Decoder | Patched UNet (unet_patched) |
| Quantizer | FSQ (codebook: 8-8-8-6-5, vocab: 15,360) |
| Image size | 448×448 px |
| Patch size | 16×16 px |
| Token grid | 28×28 = 784 tokens per image |
| Input channels | 2 (Digital Surface Model, Digital Terrain Model) |
| Latent dim | 5 |
Geospatial Properties
All TerraVision tokenizers produce the same spatial window in 448 pixels, regardless of the underlying raster resolution. This ensures token grids are spatially aligned across modalities for cross-modal pretraining.
- Pixel size: 0.08 m
- Source: Actueel Hoogtebestand Nederland 6 (AHN6) at 7.5 cm resolution
Normalization
Input data should be normalized before encoding:
- Scheme: minmax (clip [-20, 80] → [0, 1])
See config.json for exact normalization parameters.
Usage
import torch
from huggingface_hub import hf_hub_download
from terratorch.models.backbones.terramind.tokenizer.vqvae import DiVAE
# Download weights and config
weights_path = hf_hub_download(repo_id="YOUR_REPO_ID", filename="tokenizer.pt")
# Instantiate model
tokenizer = DiVAE(
image_size=448,
patch_size=16,
n_channels=2,
enc_type="vit_b_enc",
dec_type="unet_patched",
quant_type="fsq",
codebook_size="8-8-8-6-5",
latent_dim=5,
post_mlp=True,
norm_codes=True,
)
# Load weights
state_dict = torch.load(weights_path, map_location="cpu")
tokenizer.load_state_dict(state_dict)
tokenizer.eval()
# Encode: image → tokens
x = torch.randn(1, 2, 448, 448)
quant, code_loss, tokens = tokenizer.encode(x)
print(tokens.shape) # (1, 28, 28)
# Decode: tokens → reconstruction (diffusion sampling)
recon = tokenizer(x, timesteps=50)
Training
Trained with the TerraVision-NL codebase using DiVAE (diffusion-based VQ-VAE) following the TerraMind paper methodology (Section 8.1).
- Checkpoint:
ahn-best-epoch-0002.ckpt - Diffusion: 1000 timesteps, linear schedule, predicts sample