A humble proposal for GeoAI: Epistemology, methodology, and relevance in curb ramp classification.
This paper critically examines how data availability and algorithmic constraints reshape research design, relevance, and outcomes in GIScience. Through a case study of automated curb ramp detection in Seattle, Washington, I demonstrate how the limitations of data, tools, and computational frameworks...
| Publicado en: | Canadian Geographer Vol. 69; no. 4; pp. 1 - 14 |
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| Formato: | Artículo |
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Wiley-Blackwell
Winter2025
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ssf&AN=190223687&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 190223687 longDbName: Social Sciences Full Text (H.W. Wilson) uiTag: AN controlInfo: bkinfo: jinfo: jid: 00083658 CGE jtl: Canadian Geographer issn: 00083658 maglogo: Y pubinfo: dt: Winter2025 vid: 69 iid: 4 pid: 480 pub: Wiley-Blackwell artinfo: ui: 190223687 10.1111/cag.70043 ppf: 1 ppct: 13 formats: tig: atl: A humble proposal for GeoAI: Epistemology, methodology, and relevance in curb ramp classification. aug: au: Deitz, Shiloh L. affil: Department of Sociology and Anthropology, Saint Louis University, 3700 Lindell Blvd, Saint Louis 63108, MO, , United States su: Seattle (Wash.) Geographic spatial analysis Geospatial data Scientific method Geographic information systems Machine learning sug: subj: Seattle (Wash.) Geographic spatial analysis Geospatial data Scientific method Geographic information systems Machine learning keyword: explainable AI (XAI) GeoAI reflexive methods scientific relevance spatial accessibility accessibilité spatiale IA explicable (XAI) méthodes réflexives pertinence scientifique explainable AI (XAI) GeoAI reflexive methods scientific relevance spatial accessibility accessibilité spatiale IA explicable (XAI) méthodes réflexives pertinence scientifique ab: This paper critically examines how data availability and algorithmic constraints reshape research design, relevance, and outcomes in GIScience. Through a case study of automated curb ramp detection in Seattle, Washington, I demonstrate how the limitations of data, tools, and computational frameworks frequently shape the ambitions of early‐stage geographic inquiry. While GeoAI and machine learning offer new possibilities for spatial analysis, they also embed assumptions, value hierarchies, and technical limitations that influence what questions can be asked and what answers can be obtained. Using a random forest model and open‐source LiDAR and imagery data, I show how data sparsity, class imbalance, and aspatial training techniques complicate both accuracy and utility. Drawing on visual methods and explainable AI, I interrogate how the algorithm "learned" patterns and where it failed, revealing that AI‐identified relevance often diverges from socially meaningful goals. I argue for a reflexive approach to GeoAI—one that embraces error, foregrounds relevance, and resists the allure of algorithmic objectivity. The paper ultimately calls for centering research relevance alongside reproducibility and replicability in spatial data science, advocating for humility in the face of technological complexity. Key messages: AI and big data reshape not only how we analyze geographic problems, but also what questions we believe are answerable.Scientific relevance necessitates a reflexive consideration of how data, algorithms, and methodological shortcuts influence research outcomes.GeoAI can aid spatial analysis, but its limitations demand humility, transparency, and human judgment to ensure meaningful results. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: N holdings: @attributes: islocal: N |
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