Bird Migration with Visual Avian Navigation & Nest Nidification: The Spatial Linear Geometries Georeferencing Functionality

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.