Rip current detection in nearshore areas through UAV video analysis using optical flow and almost local-isometric embedding techniques on the sphere

Rip currents cause most surf-beach lifeguard rescues and most beach drowning incidents, yet automated detection relies on costly instrumentation that scales poorly across the many beaches that lack lifeguards. We propose an end-to-end pipeline producing pixel-level rip-current likelihood maps directly from raw unmanned aerial vehicle (UAV) video without manual ground-control points — to our knowledge the first to do so from uncalibrated drone video, prior automated approaches operating at video-clip granularity or requiring surveyed camera installations. The pipeline composes (i) dense optical-flow estimation with five classical algorithm variants; (ii) a sphere-aware projection mapping pixel coordinates to local-isometric ground coordinates, with a tilt-aware Jacobian correction of per-pixel velocity; (iii) a CNN six-class scene parser (sky/sea/land/foam/structure/void), trained on frames verified by dense pixel-matching to contain no extraction from either evaluation video; (iv) a per-pixel temporal accumulator flagging pixels whose flow direction agrees with the locally-estimated offshore direction within