Data · dataset · 2026
A 20-city benchmark for single-channel nighttime CLDMSK emulation from VIIRS Day/Night Band imagery
Listed in ScienceDB
Nighttime cloud masking normally relies on thermal infrared channels, which many visible-only small-satellite missions do not carry.
Description
This dataset provides a reproducible cross-city benchmark for the question of whether a single VIIRS Day/Night Band (DNB) channel can reproduce the operational VIIRS CLDMSK cloud-mask decision. Collocating DNB Level-1 radiance with CLDMSK Level-2 within 1 km over 2023–2024 yields patches from 20 cities across 712 distinct overpass nights; array-level quality screening retains 14,149 paired 200×200-pixel patches (53 removed for scan-edge fill bands, CLDMSK "no result" pixels, or fill lunar metadata).
Under a strict leave-one-city-out protocol with five seeds (100 trained models), a radiance-only ResNet34 U-Net reaches 0.8079 mean pixel-level Emulation Agreement (EA), with city means spanning 16.8 percentage points (Sydney 0.723 to Cairo 0.892) against a seed-to-seed standard deviation of 0.003. Non-learned baselines fall far behind (majority class 0.563, single-pixel radiance threshold 0.552, pixel-statistics random forest 0.639), and three paired ablations — illumination/geometry conditioning via FiLM (−0.73 pp), per-city z-score normalisation (+0.21 pp), and explicit local-uniformity channels (−0.13 pp) — all fail to improve cross-city transfer.
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The deposit contains the collocation index and exclusion list, the fold definitions and seeds, all per-fold result files, the preprocessing and evaluation code, and the sources of every figure and table, so that every number in the associated paper can be reproduced. Contents: ScienceDB_deposit_core_20260914.zip (code, benchmark index, per-fold results, figures, tables, documentation) and dataset_20city_clean.zip (14,149 .npz patches, layout <city>/<City>_YYYYMMDD_HHMMSSS.npz; field dictionary in DATA_DICTIONARY.md).
Links
Where it is published
- DOI doi.org/10.57760/sciencedb.012p4 ↗
DOI / persistent id · from scidb cn
Catalogue records · 1
- OAI-PMH record scidb.cn/oai?verb=GetRecord&metadataPrefix=oai_dc&identifier=10.57760%2… ↗
metadata API · from scidb cn
Topics
- From keywords
- Computer Science & AI · Earth & Environmental Science · Engineering · Humanities · Life Sciences · Social Science
- Inferred from text
- Geomatic engineering 71% · Tabular 65%
Provenance · 1 source records, 13 field assertions
| Source | Key | Last seen | Raw |
|---|---|---|---|
| ScienceDB | 10.57760/sciencedb.012p4 | 11 d ago | JSON v1 |
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|---|---|---|---|
| access_level | source · scidb cn | connector:scidb_cn@1.0.0 | |
| concepts[field].anzsrc:group:4013 | enrichment · scidb cn | taxonomy-embedding@1.0.0 | title+keywords+description (71%) |
| concepts[field].local:field:computer-science-ai | mapping · scidb cn | connector:scidb_cn@1.0.0 | |
| concepts[field].local:field:earth-environmental | mapping · scidb cn | connector:scidb_cn@1.0.0 | |
| concepts[field].local:field:engineering | mapping · scidb cn | connector:scidb_cn@1.0.0 | |
| concepts[field].local:field:humanities | mapping · scidb cn | connector:scidb_cn@1.0.0 | |
| concepts[field].local:field:life-sciences | mapping · scidb cn | connector:scidb_cn@1.0.0 | |
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| concepts[modality].local:modality:tabular | enrichment · scidb cn | keyword-concept-rules@1.0.0 | title+description (65%) |
| description | source · scidb cn | connector:scidb_cn@1.0.0 | /metadata/dc/description |
| license | source · scidb cn | connector:scidb_cn@1.0.0 | /metadata/dc/rights |
| publication_date | source · scidb cn | connector:scidb_cn@1.0.0 | |
| title | source · scidb cn | connector:scidb_cn@1.0.0 | /metadata/dc/title |