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README.md

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+---
+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).

mindset_aef.parquet

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+size 7845382933

mindset_teachers.parquet

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