Major-TOM/Core-VIIRS-Nighttime-Light

Major TOM Core VIIRS Nighttime Light

Annual radiance composites from the Visible Infrared Imaging Radiometer Suite (VIIRS) Day/Night Band (DNB), packaged on the Major TOM grid.

Source	Modality	Number of Patches	Patch Size	Total Pixels
VIIRS Nighttime Light	Nighttime Light	2,108,341	1056 × 1056 (10 m)	> 2.3 trillion

Authors: Marcin Kluczek (CloudFerro), Mikolaj Czerkawski (Asterisk Labs), Cesar Aybar (Asterisk Labs), Jędrzej S. Bojanowski (CloudFerro)

Content

Variable	Description	Units
median	Median annual radiance	nW/cm²/sr

Additional modalities (average, average-masked, median-masked, min, max, cf_cvg, cvg) will be supplied later.

Source product: Annual VIIRS Nighttime Lights V2

• 2016–2021: Annual VNL V2.1
• 2022–2024: Annual VNL V2.2

Per-year patch counts:

Year	Patches
2016	55,780
2017	20,541
2018	85,109
2019	412,181
2020	384,748
2021	378,299
2022	422,468
2023	348,773
2024	442

Spatial Coverage

Global, on the Major TOM 10 m grid, spanning approximately 75°N to 65°S in latitude and the full 180°W to 180°E longitude range. This is a global monotemporal dataset: nearly every land patch is contained at least once per year, with only marginal overlaps.

Cloud Optimized ZIP (cozip)

This dataset is published as cozip archives — standard ZIP files arranged so that any individual patch can be fetched in a single HTTP range request, without downloading the whole shard. cozip is developed by Asterisk Labs.

Layout on disk:

INDEX.parquet                                  # global catalog of all patches
2016/MAJORTOM-VIIRS-NTL_2016_median_000.zip    # cozip shards (~4 GiB each)
2016/MAJORTOM-VIIRS-NTL_2016_median_001.zip
...
2024/MAJORTOM-VIIRS-NTL_2024_median_000.zip

INDEX.parquet is a GeoParquet catalog with one row per patch. Key columns:

Column	Meaning
name	Patch filename (MT10_<col>D_<row>L_<year>_median.tif)
shard	Relative path to the cozip shard that holds this patch
offset, size	Byte range inside the shard — the raw GeoTIFF payload
id, majortom:code_100km, majortom:code_1000km	Major TOM grid identifiers
geometry, bbox	Patch footprint (WGS84)
admin:country/state/district	Administrative attribution
terrain:elevation, climate:*, soil:*, socio:*	Co-located covariates

Quick start

Install the reader:

pip install cozip rasterio pandas pyarrow

Read the global catalog and stream one patch directly from the Hub — no full-shard download:

import cozip
import rasterio

# Read INDEX.parquet from anywhere (local path or https URL).
df = cozip.read("https://huggingface.co/datasets/Major-TOM/Core-VIIRS-Nighttime-Light/resolve/main/INDEX.parquet")

# Pick a patch by id (or filter by bbox, country, year, etc.).
row = df[df["id"] == "MT10_696D_231L"].iloc[0]

# `cozip:gdal_vsi` is a /vsisubfile/ path GDAL can open directly.
with rasterio.open(row["cozip:gdal_vsi"]) as src:
    arr = src.read(1)
    print(arr.shape, src.crs, src.bounds)

Read a single shard's metadata only (when you already know which shard you want):

df = cozip.read("https://huggingface.co/datasets/Major-TOM/Core-VIIRS-Nighttime-Light/resolve/main/2020/MAJORTOM-VIIRS-NTL_2020_median_000.zip")
print(len(df), "patches in this shard")

Pure-Python access (no GDAL, just an HTTP Range request):

import requests, io, rasterio

url = f"https://huggingface.co/datasets/Major-TOM/Core-VIIRS-Nighttime-Light/resolve/main/{row['shard']}"
off, sz = int(row["offset"]), int(row["size"])
blob = requests.get(url, headers={"Range": f"bytes={off}-{off + sz - 1}"}).content
with rasterio.io.MemoryFile(blob) as mem, mem.open() as src:
    arr = src.read(1)

Cite

@inproceedings{Major_TOM,
  title={Major TOM: Expandable Datasets for Earth Observation},
  author={Alistair Francis and Mikolaj Czerkawski},
  year={2024},
  booktitle={IGARSS 2024 - 2024 IEEE International Geoscience and Remote Sensing Symposium},
  eprint={2402.12095},
  archivePrefix={arXiv},
  primaryClass={cs.CV}
}

VIIRS Nighttime Light products credit

Many thanks to Earth Observation Group (EOG), part of the Payne Institute for Public Policy at Colorado School of Mines, especially Christopher D. Elvidge, for their support and consultation.

- C.D. Elvidge, M. Zhizhin, T. Ghosh, F-C. Hsu, "Annual time series of global VIIRS nighttime lights derived from monthly averages: 2012 to 2019", Remote Sensing, 2021, 13(5), 922. https://doi.org/10.3390/rs13050922.
- C.D. Elvidge, K. Baugh, M. Zhizhin, F.-C. Hsu, and T. Ghosh, "VIIRS night-time lights," International Journal of Remote Sensing, vol. 38, pp. 5860–5879, 2017. https://doi.org/10.1080/01431161.2017.1342050.
- C.D. Elvidge, K.E. Baugh, M. Zhizhin, and F.-C. Hsu, "Why VIIRS data are superior to DMSP for mapping nighttime lights," Asia-Pacific Advanced Network 35, vol. 35, p. 62, 2013. http://dx.doi.org/10.7125/APAN.35.7.