Driven by the cross-domain synergy of unmanned aerial vehicle （UAV） technology and artificial intelligence， and supported by national low-altitude economic policies and pilot reforms for airspace opening， the low-altitude visual perception has played a significant role in smart cities， inspection， rescue， and other applications. High-quality low-altitude visual data serve as the crucial foundational resource in the field of low-altitude intelligent perception， and the release and application of public datasets have been pivotal in advancing low-altitude perception technologies. Despite the proposal of numerous datasets for low-altitude visual perception， systematic organization and analysis of these datasets remain inadequate. To address this issue， this paper conducts a comprehensive survey of publicly released low-altitude UAV vision-related datasets over the past 11 years， categorizes and explores them based on different data characteristics and application scenarios， and selects representative datasets for detailed analysis. This review covers multiple domains， including single-UAV perception， multi-UAV cooperative perception， multi-task perception， multi-source perception， complex environmental characteristics， and UAV embodied intelligence. To facilitate researchers’ understanding and use， the paper summarizes the basic information of all datasets in graphical form and systematically analyzes their development trends from two main dimensions： （1） metadata analysis， including dataset size distribution， scenario distribution， and supported task types； and （2） basic information analysis， involving total image and video counts， target category distribution， and annotation instance numbers. The analysis fully demonstrates the significant progress in the quality of low-altitude visual perception datasets. Meanwhile， it points out that， despite the initial formation of a systematic framework for low-altitude data， issues such as the imbalance between cost and efficiency in low-altitude data annotation， insufficient reusability of multi-source data， inadequate coverage of extreme environments， and fragmented embodied intelligence data still exist. Finally， this paper proposes outlooks for the future development of low-altitude datasets.