Evidence map›Paper›PMID 38486939›Full record

ArticlePhotogrammetric engineering and remote sensing2023

Classifying Building Roof Damage Using High Resolution Imagery for Disaster Recovery.

Elaina Gonsoroski, Yoonjung Ahn, Emily W Harville, Nathaniel Countess, Maureen Y Lichtveld, Ke Pan, Leslie Beitsch, Samendra P Sherchan, Christopher K Uejio

Open access · hybridAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Photogrammetric engineering and remote sensing, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
1.5field-weighted citation impact, top 20% of its field
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

0 citing papers in PubMed, 7 citations in OpenAlex.

No citing paper in PubMed yet.

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 at 4 institutions in 1 country.

Elaina GonsoroskiDepartment of Geography, College of Social Sciences and Public Policy, Florida State University, Tallahassee, FL 32306.
Yoonjung AhnInstitute of Behavioral Science, University of Colorado, Boulder, CO 80309.
Emily W HarvilleDepartment of Epidemiology, School of Public Health and Tropical Medicine, Tulane University, New Orleans, LA 70112.
Nathaniel CountessDepartment of Geography, College of Social Sciences and Public Policy, Florida State University, Tallahassee, FL 32306.
Maureen Y LichtveldDepartment of Environmental and Occupational Health, School of Public Health, University of Pittsburgh, Pittsburgh, PA, 15261.
Ke PanDepartment of Epidemiology, School of Public Health and Tropical Medicine, Tulane University, New Orleans, LA 70112.
Leslie BeitschDepartment of Behavioral Sciences and Social Medicine, College of Medicine, Florida State University, Tallahassee, FL 32306.
Samendra P SherchanDepartment of Epidemiology, School of Public Health and Tropical Medicine, Tulane University, New Orleans, LA 70112.
Christopher K UejioDepartment of Geography, College of Social Sciences and Public Policy, Florida State University, Tallahassee, FL 32306.
Florida State University · USTulane University · USUniversity of Colorado Boulder · USUniversity of Pittsburgh · US

Funding

Environmental influences on pregnancy outcomes after Hurricane MichaelR21ES031020 · NIEHS · TULANE UNIVERSITY OF LOUISIANA · PI HARVILLE, EMILY WHEELER, LICHTVELD, MAUREEN Y. · 2019 to 2020
$433k
NIEHS NIH HHS R21 ES031020
6 · The paper itself

Abstract

Post-hurricane damage assessments are often costly and time-consuming. Remotely sensed data provides a complementary method of data collection that can be completed comparatively quickly and at relatively low cost. This study focuses on 15 Florida counties impacted by Hurricane Michael (2018), which had category 5 strength winds at landfall. The present study evaluates the ability of aerial imagery collected to cost-effectively measure blue tarps on buildings for disaster impact and recovery. A support vector machine model classified blue tarp, and parcels received a damage indicator based on the model's prediction. The model had an overall accuracy of 85.3% with a sensitivity of 74% and a specificity of 96.7%. The model results indicated approximately 7% of all parcels (27 926 residential and 4431 commercial parcels) in the study area as having blue tarp present. The study results may benefit jurisdictions that lacked financial resources to conduct on-the-ground damage assessments.

Identifiers

PMID38486939
PMCPMC10939134
OpenAlexW4382699905

What OpenQuestion holds

Textmetadata
LicenceTDM
Read underepoch 390

Registered trials

None linked

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.