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Datasets:
dcher95
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dense500

Tasks:
Image-to-Image
Unconditional Image Generation
Feature Extraction
Modalities:
Image
Text
Geospatial
Formats:
parquet
Languages:
English
Size:
10K - 100K
Tags:
satellite-imagery
remote-sensing
hierarchical
benchmark
mapbox
pyramid-generation
Libraries:
Datasets
Dask
Polars
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dense500 / README.md
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Initial upload: Dense500 (5 zoom windows x 100 sites, source-shift filtered)
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metadata 
license: cc-by-nc-4.0
task_categories:
  - image-to-image
  - unconditional-image-generation
  - feature-extraction
language:
  - en
tags:
  - satellite-imagery
  - remote-sensing
  - hierarchical
  - benchmark
  - mapbox
  - pyramid-generation
size_categories:
  - 10K<n<100K
pretty_name: Dense500
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/train-*.parquet
dataset_info:
  features:
    - name: image
      dtype: image
    - name: location
      dtype: string
    - name: site_id
      dtype: string
    - name: tag
      dtype: string
    - name: continent
      dtype: string
    - name: site_lon
      dtype: float64
    - name: site_lat
      dtype: float64
    - name: zoom
      dtype: int32
    - name: tile_x
      dtype: int32
    - name: tile_y
      dtype: int32
    - name: site_start_zoom
      dtype: int32
    - name: site_leaf_zoom
      dtype: int32
    - name: site_depth
      dtype: int32
    - name: is_root
      dtype: bool
    - name: is_leaf
      dtype: bool
    - name: zoom_window
      dtype: int32
    - name: site_index_in_window
      dtype: int32
  splits:
    - name: train
      num_bytes: 1087489040
      num_examples: 42500
  download_size: 1087489040
  dataset_size: 1087489040

Dense500

500-site hierarchical satellite imagery benchmark for evaluating pyramid generation across multiple zoom windows.

500 sites × 85 tiles each (≈42,500 tiles), structured as 100 sites per each of 5 zoom windows (W1–W5). Designed as the evaluation corpus for the unified pyramid-generation strategy described in evaluate_pyramid.md.

Two distinguishing properties vs sibling global-dense-satellite:

  1. Fixed depth-4 quadtree per site — exactly 85 tiles (1 + 4 + 16 + 64) at zooms l, l+1, l+2, l+3. No variable-depth sites.
  2. Source-shift filtered — sites where the imagery source changes mid-hierarchy (e.g., Mapbox satellite → aerial transition around z15–z16 in NA/EU) are dropped. Every published site shows visually coherent imagery across all four zooms.

Like global-dense-satellite, sites are also disjoint from Git-10M.

Zoom windows

Window l h = l+3 Root tile covers Sites
W1 11 14 ~78 km 100
W2 12 15 ~39 km 100
W3 13 16 ~20 km 100
W4 14 17 ~10 km 100
W5 15 18 ~5 km 100

Each site contributes 85 tiles → 42,500 tiles total.

What's in here

Each row is one satellite tile. Group by site_id to recover a complete depth-4 quadtree; use zoom_window to filter by window.

Column Type Description
image image 256×256 JPEG bytes
location string "{zoom}_{x}_{y}" (Web Mercator)
site_id string Stable id grouping all 85 tiles of one quadtree
tag string naeu_urban or global_urban
continent string NA, EU, Asia, Africa, SA, Oceania
site_lon, site_lat float64 Site center coords
zoom, tile_x, tile_y int32 Web Mercator tile coords
site_start_zoom, site_leaf_zoom int32 Bounds of this site's hierarchy (l, h)
site_depth int32 Always 4
is_root, is_leaf bool Tile position in its hierarchy
zoom_window int32 1..5 corresponding to W1..W5
site_index_in_window int32 0..99 within each window

Source-shift filter

A site is dropped if any adjacent-zoom pair within its window has:

Bhattacharyya(hist(z), hist(z+1)) > 0.25  AND
Pearson(hist(z), hist(z+1))      < 0.875

Histograms are 8×8×8 RGB, aggregated across all tiles at each zoom within the site. Thresholds were calibrated by spot-check on a 50-site W5 probe to sit halfway between a loose default (≥0.30 / <0.85) and a tight alternative (≥0.20 / <0.90).

This filter catches Mapbox's well-known satellite→aerial composite transition in NA/EU cities, which we verified is also present in the upstream Git-10M Google Maps source. The trade-off: about 11–24% of pre-filter sites are dropped, mostly at deeper zoom windows.

Geographic distribution

Window Strategy Why
W1 (z11→z14) Global, all continents Satellite imagery is reliable globally at these zooms
W2 (z12→z15) Global, all continents Same, with mild aerial-shift risk near z15
W3 (z13→z16) NA/EU-biased, other continents represented z16 outside NA/EU often runs into Mapbox overzoom
W4 (z14→z17) Heavily NA/EU z17 needs Mapbox's aerial layer (NA/EU only)
W5 (z15→z18) NA/EU only Same reason — z18 is aerial, satellite would be overzoomed

Loading 

from datasets import load_dataset

ds = load_dataset("dcher95/dense500", split="train")

# Group tiles by site to recover one full quadtree
from collections import defaultdict
sites = defaultdict(list)
for row in ds:
    sites[row["site_id"]].append(row)

# Or pick all sites in a single zoom window
w3 = ds.filter(lambda r: r["zoom_window"] == 3)

For training/eval, use the GlobalDenseSatelliteDataset class — its (zoom, tile_x, tile_y) based descendant logic works against this schema unchanged.

Limitations and caveats

  • Mapbox tile imagery: redistribution of Mapbox tiles is restricted by Mapbox's Terms of Service. This dataset is published under CC-BY-NC for non-commercial research use only.
  • NA/EU concentration in deeper windows: W4 and W5 are heavily NA/EU because that's where Mapbox aerial imagery exists at z17/z18. W1 and W2 are global; W3 has reasonable global coverage but is also NA/EU-biased.
  • Source-shift filter is a heuristic: it uses color histograms, so a same-source hierarchy with strong seasonal lighting drift across zooms could occasionally trigger a false positive. We checked borderline cases manually during threshold calibration.

Pipeline

The full generation pipeline is at github.com/dcher95/genesis (paths scripts/dataset/dense500/):

  1. sample_sites_dense500.py — 5 windows × continent-stratified candidate sampling.
  2. download_tiles.py --full — concurrent fetch with overzoom validation + site integrity (shared with global_dense).
  3. filter_source_shifts.py — middle-threshold cross-zoom histogram filter.
  4. topup_window.py — iterative top-up for underfilled windows (different seed per wave).
  5. finalize_dense500.py — cap each window at 100, add zoom_window + site_index_in_window columns.
  6. convert_to_parquet.py, publish_to_hf.py — packaging.

Citation 

@misc{dense500_2026,
  title = {Dense500: A 500-site hierarchical satellite imagery benchmark for pyramid generation evaluation},
  author = {Cher, Dan},
  year = {2026},
  publisher = {HuggingFace},
  url = {https://huggingface.co/datasets/dcher95/dense500},
}