Publication:
Leveraging Imagery Data in Evaluations: Applications of Remote-Sensing and Streetscape Imagery Analysis

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dc.contributor.authorZiulu, Virginia
dc.date.accessioned2024-03-05T15:39:36Z
dc.date.available2024-03-05T15:39:36Z
dc.date.issued2024-03-05
dc.description.abstractImagery data offer the potential to answer critical questions regarding the relevance and effectiveness of development initiatives, providing a factual basis for decision-making and the refinement of policies and programs. Imagery data, encompassing a diverse array of sources from remote-sensing imagery to digital photos, offer a vast and underused resource for understanding the dynamics of change in urban development and other geospatial phenomena. Despite their ubiquity, imagery data remain relatively neglected in the evaluation of international development interventions, primarily on account of perceived barriers in relation to computation and expertise. However, recent advances in machine learning and increased computational resources have made imagery data more accessible. This paper explores the potential of imagery data in evaluations and presents various data types and methodologies, demonstrating their advantages and limitations. An Independent Evaluation Group case study on a World Bank urban development project in Bathore, Albania, illustrates the practical application of different imagery data and methodologies. By leveraging imagery data, evaluators can gain insights into the geographical impact of development interventions. Moreover, integrating imagery data with other information sources, such as surveys and socioeconomic statistics, offers strong potential for deepening the understanding of complex phenomena.en
dc.identifierhttp://documents.worldbank.org/curated/en/099058502232465839/IDU132a92d101900d14c9519b57101495e9d4106
dc.identifier.doi10.1596/IEG187567
dc.identifier.urihttps://hdl.handle.net/10986/41152
dc.languageEnglish
dc.language.isoen_US
dc.publisherWorld Bank
dc.relation.ispartofseriesIEG Methods and Evaluation Capacity Development Working Paper Series
dc.rightsCC BY-NC 3.0 IGO
dc.rights.holderWorld Bank
dc.rights.urihttps://creativecommons.org/licenses/by-nc/3.0/igo/
dc.subjectIMAGERY DATA
dc.subjectREMOTE-SENSING IMAGERY
dc.subjectMACHINE LEARNING
dc.subjectACCESSIBILITY
dc.subjectPRATICAL APPLICATION
dc.titleLeveraging Imagery Data in Evaluationsen
dc.title.subtitleApplications of Remote-Sensing and Streetscape Imagery Analysisen
dc.typeReport
dspace.entity.typePublication
okr.date.disclosure2024-03-05
okr.date.lastmodified2024-02-27T00:00:00Zen
okr.doctypeWorking Paper
okr.doctypePublications & Research
okr.docurlhttp://documents.worldbank.org/curated/en/099058502232465839/IDU132a92d101900d14c9519b57101495e9d4106
okr.guid099058502232465839
okr.identifier.docmidIDU-32a92d10-900d-4c95-9b57-01495e9d4106
okr.identifier.doi10.1596/IEG187567
okr.identifier.doihttps://doi.org/10.1596/IEG187567
okr.identifier.externaldocumentum34266286
okr.identifier.internaldocumentum34266286
okr.identifier.report187567
okr.import.id3357
okr.importedtrueen
okr.language.supporteden
okr.pdfurlhttp://documents.worldbank.org/curated/en/099058502232465839/pdf/IDU132a92d101900d14c9519b57101495e9d4106.pdfen
okr.region.geographicalWorld
okr.topicInformation and Communication Technologies::ICT Applications
okr.unitMethods Advisory Function (IEGMA)
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