Leaving the City: A Large-Scale Aerial Dataset for Cross-Season Localization in Unstructured Environments

Long-term aerial localization requires matching live flightimagery against archival reference maps, demanding feature represen-tations that are invariant to severe appearance changes and perceptualaliasing. However, current benchmarks report only aggregate accuracyover predominantly man-made environments, masking severe terrain-dependent performance gaps. As a result, localization performance overunstructured natural landscapes—where self-similar textures and drasticseasonal changes dominate—remains effectively unmeasured. To addressthis, we introduce Leaving the City, the first large-scale aerial benchmarkdesigned to isolate and quantify terrain-dependent localization gaps. Cap-tured via a microlight aircraft, our dataset comprises 1,379 km of flighttrajectories flown repeatedly to capture distinct seasonal variations. Wepair high-frame-rate imagery and inertial measurements with semanticterrain masks, multi-year-old orthophotos, and precise 6-DoF groundtruth. Evaluating state-of-the-art matchers through our terrain-stratifiedprotocol reveals a systematic bias: methods that succeed on man-madesurfaces degrade sharply over natural terrain undergoing strong appear-ance change. By exposing where current methods fail, our benchmarkprovides a rigorous foundation for developing robust, all-terrain aeriallocalization. The dataset and code are publicly available.4