---
license: cc-by-4.0
task_categories:
  - feature-extraction
tags:
  - geospatial
  - location-encoder
  - embeddings
  - distillation
  - geoparquet
pretty_name: MINDSET
size_categories:
  - 10M<n<100M
---

# MINDSET — MIND Spatial-Embedding Teachers

The pretraining dataset for **MIND** (Matryoshka Implicit Neural Distillation), a lat/lon-only location
encoder distilled from four geospatial teachers. MINDSET caches the teachers' location-conditioned
embeddings so the pretraining is **reproducible** and the location-only teachers do not need to be
recomputed each epoch.

Points are the urban-dense global training sample (12,099,072 land coordinates, WGS84). Two files,
joinable on `point_id`, both **GeoParquet 1.1** (Hilbert-sorted, `bbox` covering column → spatial range
queries read only the relevant row groups):

| file | grain | rows | columns |
|---|---|---|---|
| `mindset_teachers.parquet` | one row per point | 12,099,072 | `point_id`, `geometry` (Point), `bbox`, `climplicit` [1024], `geoclip` [512], `sinr` [256] — **float16** |
| `mindset_aef.parquet` | one row per (point, year) | 108,891,648 | `point_id`, `year`, `geometry`, `bbox`, `aef` [64] — **int8** |

## Conventions

- **CRS:** OGC:CRS84 (lon, lat) WGS84 — geometry is `Point(lon, lat)`. (Not EPSG:4326, which is lat,lon.)
- **Join** the two files on `point_id`. The three teachers are location-only (one vector per point);
  AlphaEarth is temporal, so it has one row per (point, year), years 2017–2025.
- **dtypes / dequantization.** Teachers are stored **raw** as float16 (the embedder output). AEF is the
  **native int8** with `signed_square` quantization — dequantize with
  `f = sign(x) * (|x| / 127.5) ** 2` (nodata = −128). Both are turned into training targets by
  **L2-normalizing at use** (MIND also optionally PHI-S-standardizes the teachers). float16/int8 are
  downstream-lossless for distillation targets.

## Teachers (provenance)

| column | teacher | dim | source |
|---|---|---|---|
| `aef` | AlphaEarth Foundations | 64 | Google AEF v1 annual (source.coop `tge-labs/aef`), CC-BY-4.0 |
| `climplicit` | Climplicit | 1024 | `Jobedo/climplicit` (CHELSA climate, ReSIREN) |
| `geoclip` | GeoCLIP | 512 | GeoCLIP location encoder |
| `sinr` | SINR | 256 | `MVRL/sinr-location-encoder-1000-cls` (Cole et al. 2023) |

Embeddings are derived from these pretrained models; respect each upstream license.

## Load

```python
import geopandas as gpd, pandas as pd
teachers = gpd.read_parquet("mindset_teachers.parquet")  # geometry + climplicit/geoclip/sinr
aef = gpd.read_parquet("mindset_aef.parquet")            # per (point, year); join on point_id
# spatial subset without a full download (cloud-optimized): DuckDB spatial / pyarrow dataset filters
```

## Reproduce

Built with `scripts/build_mindset.py` from the neuralftw repo (AEF from the existing int8 shards; the
three location-only teachers computed on GPU).