Documentation Index
Fetch the complete documentation index at: https://docs.lancedb.com/llms.txt
Use this file to discover all available pages before exploring further.
View on Hugging Face
Source dataset card and downloadable files for lance-format/eurosat-lance.
blanchon/EuroSAT_RGB.
This is the canonical “geo” tile-level classification benchmark, useful for remote sensing pre-training and small-tile retrieval research.
Splits
| Split | Rows |
|---|
train.lance | 16,200 |
validation.lance | 5,400 |
test.lance | 5,400 |
Schema
| Column | Type | Notes |
|---|
id | int64 | Row index within split |
image | large_binary | Inline JPEG bytes (64×64 RGB Sentinel-2) |
label | int32 | Class id (0-9) |
label_name | string | Annual_Crop, Forest, Herbaceous_Vegetation, Highway, Industrial_Buildings, Pasture, Permanent_Crop, Residential_Buildings, River, SeaLake |
image_emb | fixed_size_list<float32, 512> | OpenCLIP ViT-B-32 image embedding (cosine-normalized) |
Pre-built indices
IVF_PQ on image_emb — metric=cosineBTREE on labelBITMAP on label_name
Quick start
import lance
ds = lance.dataset("hf://datasets/lance-format/eurosat-lance/data/train.lance")
print(ds.count_rows(), ds.schema.names, ds.list_indices())
Load with LanceDB
These tables can also be consumed by LanceDB, the multimodal lakehouse and embedded search library built on top of Lance, for simplified vector search and other queries.
import lancedb
db = lancedb.connect("hf://datasets/lance-format/eurosat-lance/data")
tbl = db.open_table("train")
print(f"LanceDB table opened with {len(tbl)} satellite tiles")
Visual similarity search
import lance
import pyarrow as pa
ds = lance.dataset("hf://datasets/lance-format/eurosat-lance/data/train.lance")
emb_field = ds.schema.field("image_emb")
ref = ds.take([0], columns=["image_emb", "label_name"]).to_pylist()[0]
query = pa.array([ref["image_emb"]], type=emb_field.type)
hits = ds.scanner(
nearest={"column": "image_emb", "q": query[0], "k": 5, "nprobes": 16, "refine_factor": 30},
columns=["id", "label_name"],
).to_table().to_pylist()
print(f"reference: {ref['label_name']}")
for h in hits:
print(h)
LanceDB visual similarity search
import lancedb
db = lancedb.connect("hf://datasets/lance-format/eurosat-lance/data")
tbl = db.open_table("train")
ref = tbl.search().limit(1).select(["image_emb", "label_name"]).to_list()[0]
query_embedding = ref["image_emb"]
results = (
tbl.search(query_embedding)
.metric("cosine")
.select(["id", "label_name"])
.limit(5)
.to_list()
)
Filter by class
import lance
ds = lance.dataset("hf://datasets/lance-format/eurosat-lance/data/train.lance")
rivers = ds.scanner(filter="label_name = 'River'", columns=["id"], limit=5).to_table()
Filter by class with LanceDB
import lancedb
db = lancedb.connect("hf://datasets/lance-format/eurosat-lance/data")
tbl = db.open_table("train")
rivers = tbl.search().where("label_name = 'River'").select(["id"]).limit(5).to_list()
Why Lance?
- One dataset for tiles + embeddings + indices — no sidecar TIF folder per class.
- On-disk vector and FTS indices live next to the data, so search works on local copies and on the Hub.
- Schema evolution: add columns (multi-spectral channels, model predictions, fresh embeddings) without rewriting the data.
Source & license
Converted from blanchon/EuroSAT_RGB. EuroSAT is released under the MIT license by Helber et al. The underlying Sentinel-2 imagery is © European Space Agency, made available under the Copernicus open data policy.
Citation
@inproceedings{helber2019eurosat,
title={EuroSAT: A novel dataset and deep learning benchmark for land use and land cover classification},
author={Helber, Patrick and Bischke, Benjamin and Dengel, Andreas and Borth, Damian},
journal={IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing},
year={2019}
}
