Spatial - Sat2Map Model

Satellite-to-map prediction model trained on OlmoEarth data using the PlanB/nanochat framework.

WandB Run

Training Run

Training Progress

Step	Val Loss
1000	0.3207
1500	0.4950
2000	1.0681

8-GPU Evaluation Results (DDP Aggregated)

Evaluation using all 8 GPUs with --eval-batches 200 and --batch-size 2 (400 examples per split), aggregating totals across ranks.

Step 1000 (Recommended)

Metric	Value
Val Loss	0.3668
Val Accuracy	0.8813
WorldCover Accuracy	0.8892
CDL Accuracy	0.8483

Step 1500

Metric	Value
Val Loss	0.5635
Val Accuracy	0.8680
WorldCover Accuracy	0.8755
CDL Accuracy	0.8364

Conclusion: Use step 1000 checkpoint (better val loss + accuracy). Step 1500 is fitting train harder but generalizing worse.

Repository Contents

• sat2map_checkpoints/d20_sat2map/ - Model checkpoints (steps 500, 1000, 1500, 2000)
• sat2map_dataset/sat2map_g16_t12_target64_k1024/ - Training and test dataset

Usage

from huggingface_hub import hf_hub_download

# Download best checkpoint (step 1000)
model_path = hf_hub_download(
    repo_id="Viharikvs/spatial",
    filename="sat2map_checkpoints/d20_sat2map/model_001000.pt"
)
meta_path = hf_hub_download(
    repo_id="Viharikvs/spatial",
    filename="sat2map_checkpoints/d20_sat2map/meta_001000.json"
)