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},
}