FlyingDrones: A Dataset and Benchmark for Optical Flow Estimation from UAV motion

Optical flow is an important cue for drone analysis in aerial surveillance, airspace monitoring, and long-range target tracking, where motion remains informative when appearance is weak. Estimating the motion of small Unmanned Aerial Vehicles (UAVs), however, is difficult because distant targets occupy few pixels, have low contrast, and move against complex dynamic backgrounds. Standard optical-flow benchmarks emphasize large, object-centric motions and therefore do not characterize distant aerial targets well. We introduce FlyingDrones, a generated benchmark with dense ground-truth optical flow and segmentation masks derived from physically based rendering, and define resolution-normalized metrics (EPE Norm) that normalize flow by image dimensions. We evaluate 16 state-of-the-art models on small-target motion estimation. Standard RAFT degrades substantially on small targets at low resolution, whereas VideoFlow's multi-frame variant achieves the best drone-region EPE at every tested resolution. Aerial-pretrained two-frame models such as Sea-RAFT and WAFT are competitive at 270$\times$480 but lose their advantage at 1080$\times$1920, and the best fine-tuning domain depends on resolution: KITTI helps when drone motion is sub-pixel-to-few-pixels, while Sintel-style fine-tuning helps once displacements grow with image size. Overall, long-range drone analysis benefits most from multi-frame temporal context, scale-aware evaluation, and resolution-matched fine-tuning.