  The intrinsic capability to perceive depth of field and extract salient
information by the Human Vision System (HVS) stimulates a pilot to perform
manual landing over an autoland approach. However, harsh weather creates
visibility hindrances, and a pilot must have a clear view of runway elements
before the minimum decision altitude. To help a pilot in manual landing, a
vision-based system tailored to localize runway elements likewise gets
affected, especially during crosswind due to the projective distortion of
aircraft camera images. To combat this, we propose to integrate a prompt-based
climatic diffusion network with a weather distillation model using a novel
diffusion-distillation loss. Precisely, the diffusion model synthesizes
climatic-conditioned landing images, and the weather distillation model learns
inverse mapping by clearing those visual degradations. Then, to tackle the
crosswind landing scenario, a novel Regularized Spatial Transformer Networks
(RuSTaN) learns to accurately calibrate for projective distortion using
self-supervised learning, which minimizes localization error by the downstream
runway object detector. Finally, we have simulated a clear-day landing scenario
at the busiest airport globally to curate an image-based Aircraft Landing
Dataset (AIRLAD) and experimentally validated our contributions using this
dataset to benchmark the performance.
