The capability of homing pigeons and passerine migrants to derive navigation-rated information from the
geomagnetic field (GMF), enabling them to navigate
from unfamiliar sites, is a subject of research. Despite
the vast trajectory information available from field experiments, the true accuracy of their map and compass
sense are seldom reported. The recent developments in
bird geo-tagging, precision world magnetic model and
deep machine-learning capabilities enable us to understand the mechanisms underlying their innate abilities
in true navigation. In this article, we machine-learnt
the GMF anomaly in a 10 km2 region and analysed the
flight path efficiency for a 10-km GMF-anomaly-guided
trajectory using the developed deep-learning-based artificial intelligence (AI) algorithm. From the simulated
flight path and comparing the computed efficiencies
with the field-reported results, it is observed that the
sensitivity of the GMF gradient detection sensory system
in pigeons and passerine migrants in familiar and unfamiliar regions are in the range of 1–3 nT and 0.5–2.5 nT
respectively. Identified results shall help implement AIbased solutions for understanding spatiotemporal bird
migration and enacting environmental conservation
policies.