  We propose a novel flow synthesis based visual servoing framework enabling
long-range obstacle avoidance for Micro Air Vehicles (MAV) flying amongst tall
skyscrapers. Recent deep learning based frameworks use optical flow to do
high-precision visual servoing. In this paper, we explore the question: can we
design a surrogate flow for these high-precision visual-servoing methods, which
leads to obstacle avoidance? We revisit the concept of saliency for identifying
high-rise structures in/close to the line of attack amongst other competing
skyscrapers and buildings as a collision obstacle. A synthesised flow is used
to displace the salient object segmentation mask. This flow is so computed that
the visual servoing controller maneuvers the MAV safely around the obstacle. In
this approach, we use a multi-step Cross-Entropy Method (CEM) based servo
control to achieve flow convergence, resulting in obstacle avoidance. We use
this novel pipeline to successfully and persistently maneuver high-rises and
reach the goal in simulated and photo-realistic real-world scenes. We conduct
extensive experimentation and compare our approach with optical flow and
short-range depth-based obstacle avoidance methods to demonstrate the proposed
framework's merit. Additional Visualisation can be found at
https://sites.google.com/view/monocular-obstacle/home
