| --- |
| 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). |
|
|
