MIND

MIND (Matryoshka Implicit Neural Distillation) maps a coordinate to a geospatial embedding. The released main checkpoint takes (lat, lon) in degrees and returns a 3,072-dimensional trunk without imagery or labels at inference. The first 64 dimensions are the default deployment embedding, and other leading prefixes can be selected without retraining the encoder.

The model is distilled on the MINDSET dataset which is composed of four teacher sources: AlphaEarth Foundations, Climplicit, GeoCLIP, and SINR

Files

• mind.safetensors — recommended fp16 weights (226,980,656 bytes; safe tensor format).
• mind.pt — fp32 PyTorch weights (453,967,275 bytes; pickle-based loading).
• mind.onnx and mind.onnx.data — ONNX graph and external weights.
• mind.pt2 — PyTorch ExportedProgram.

Usage

The public ONNX release can be loaded directly from the Hub. Install numpy, onnxruntime, and huggingface_hub.

import os
import numpy as np
import onnxruntime as ort

session = ort.InferenceSession(os.path.join(folder, "mind.onnx"), providers=["CPUExecutionProvider"])
coordinates = np.array([[37.77, -122.42], [51.51, -0.13]], dtype=np.float32)  # (lat, lon)
embedding = session.run(None, {"latlon": coordinates})[0]  # shape [2, 3072]
deploy_embedding = embedding[:, :64]

The ONNX graph expects latlon with shape [N, 2] in (lat, lon) order and returns the full 3,072-dimensional trunk. Use the leading 64 columns as the deployment embedding. The safetensors and PyTorch files are also available for users who have a compatible loader.