Abstract
Problem: Bird migration (eye): Georeferencing procedure with clues, rules, functionalities, and restrictions, for avian navigation and nest nidification.
 Literature Knowledge: Computer vision (sensor): Robot self-referencing with the Perspective-n- Point pose estimation technique.
 Aim: Hypothesis introduction and proving (“The birds also follow the same georeferencing procedure like robots in avian navigation and nest nidification”).
 Methodology: (a) Reference data, images, and photography acquisition and 4-means layering (eBird dataset, Flickr imagery, CORINE land covering, and Volunteered Geographic Information);
 (b) Image processing; and (c) GIS spatial overlay analysis.
 Results: Statistical spatial analysis using data of the GIS overlays (the 4 layers). Correlation matrix (Avian navigation and nest nidification in low-density urban areas as these are affected by spatial linear geometries and land cover types).
 Conclusion: A statistically satisfactory approach to the introduced hypothesis.
 Potential Applications: Human spatial cognition and movement behavior; Children’s motor control and coordination.
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