| Sumario: | 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.
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