Evidence map›Paper›PMID 40368894›Full record

ArticleScientific data2025

A sub-meter resolution urban surface albedo dataset for 34 U.S. cities based on deep learning.

Shengao Yi, Xiaojiang Li, Yixuan Liu, Xinyu Dong, Wei Tu

Abstract read
In one paragraph

Article in Scientific data, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Article
  2. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Shengao YiDepartment of City and Regional Planning, University of Pennsylvania, Philadelphia, PA, 19104, USA. shengao@upenn.edu.
Xiaojiang LiDepartment of City and Regional Planning, University of Pennsylvania, Philadelphia, PA, 19104, USA.
Yixuan LiuJacobs Technion-Cornell Institute at Cornell Tech, Cornell University, New York, NY, USA.
Xinyu DongLandscape Ecology Lab, Geography Department, Humboldt-Universität zu Berlin, Berlin, 10099, Germany.
Wei TuGuangdong Key Laboratory for Urban Informatics, Guangdong-Hong Kong-Macao Joint Laboratory for Smart Cities, and Shenzhen Key Laboratory of Spatial Information Smart Sensing and Services, and Department of Urban Informatics, School of Architecture and Urban Planning, Shenzhen University, Shenzhen, 518060, China.

Funding

National Science Foundation (NSF) 2314709
6 · The paper itself

Abstract

Surface albedo is a key determinant of urban heat islands, which modulates the amount of solar energy absorbed or reflected by urban surfaces, influencing microclimate and thermal comfort. However, high-resolution albedo is usually not available, which makes the understanding of the urban thermal environment at hyperlocal difficult. This study presents the first high-resolution urban albedo maps for 34 major U.S. cities using advanced deep learning models and multisource remote sensing data. By differentiating between impervious and pervious surfaces using a combination of NAIP imagery, roof albedo data, building footprints, land cover classifications, and Sentinel-2 imagery, this work achieves sub-meter resolution in albedo mapping. Employing U-Net for impervious surface classification along with impervious (ISA) and pervious surface albedo (PSA) prediction, these models were validated in selected cities, with ISA showing an R

Identifiers

PMID40368894
PMCPMC12078637

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.