Evidence map›Paper›PMID 36418443›Full record

ArticleScientific reports2022

Fine-grained population mapping from coarse census counts and open geodata.

Nando Metzger, John E Vargas-Muñoz, Rodrigo C Daudt, Benjamin Kellenberger, Thao Ton-That Whelan, Ferda Ofli, Muhammad Imran, Konrad Schindler, Devis Tuia

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PubMed Full text at PMC Publisher
In one paragraph

Article in Scientific reports, 2022. 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

9 authors.

Nando MetzgerETH Zurich, Zurich , Switzerland. nando.metzger@geod.baug.ethz.ch.
John E Vargas-MuñozÉcole Polytechnique Fédérale de Lausanne, Lausanne, Switzerland.
Rodrigo C DaudtETH Zurich, Zurich , Switzerland.
Benjamin KellenbergerÉcole Polytechnique Fédérale de Lausanne, Lausanne, Switzerland.
Thao Ton-That WhelanInternational Committee of the Red Cross, Geneva , Switzerland.
Ferda OfliHamad Bin Khalifa University, Ar-Rayyan, Qatar.
Muhammad ImranHamad Bin Khalifa University, Ar-Rayyan, Qatar.
Konrad SchindlerETH Zurich, Zurich , Switzerland.
Devis TuiaÉcole Polytechnique Fédérale de Lausanne, Lausanne, Switzerland.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Fine-grained population maps are needed in several domains, like urban planning, environmental monitoring, public health, and humanitarian operations. Unfortunately, in many countries only aggregate census counts over large spatial units are collected, moreover, these are not always up-to-date. We present POMELO, a deep learning model that employs coarse census counts and open geodata to estimate fine-grained population maps with [Formula: see text]m ground sampling distance. Moreover, the model can also estimate population numbers when no census counts at all are available, by generalizing across countries. In a series of experiments for several countries in sub-Saharan Africa, the maps produced with POMELO are in good agreement with the most detailed available reference counts: disaggregation of coarse census counts reaches [Formula: see text] values of 85-89%; unconstrained prediction in the absence of any counts reaches 48-69%.

Indexed as

CensusesEnvironmental Monitoring

Identifiers

PMID36418443
PMCPMC9684450

What OpenQuestion holds

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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.