With the emergence of new technologies in the field of airborne platforms and
imaging sensors, aerial data analysis is becoming very popular, capitalizing on
its advantages over land data. This paper presents a comprehensive review of
the computer vision tasks within the domain of aerial data analysis. While
addressing fundamental aspects such as object detection and tracking, the
primary focus is on pivotal tasks like change detection, object segmentation,
and scene-level analysis. The paper provides the comparison of various hyper
parameters employed across diverse architectures and tasks. A substantial
section is dedicated to an in-depth discussion on libraries, their
categorization, and their relevance to different domain expertise. The paper
encompasses aerial datasets, the architectural nuances adopted, and the
evaluation metrics associated with all the tasks in aerial data analysis.
Applications of computer vision tasks in aerial data across different domains
are explored, with case studies providing further insights. The paper
thoroughly examines the challenges inherent in aerial data analysis, offering
practical solutions. Additionally, unresolved issues of significance are
identified, paving the way for future research directions in the field of
aerial data analysis.

通过综合回顾计算机视觉任务，本文全面介绍了航空数据分析领域。主要关注对象检测、变化检测、对象分割和场景级分析等关键任务，并比较了不同架构和任务中使用的各种超参数。还对具有不同领域专业知识的库进行了深入讨论，包括航空数据集、采用的架构细节以及与航空数据分析中所有任务相关的评估指标。通过案例研究，探索了计算机视觉任务在各个领域中的航空数据应用，并提出实用解决方案来应对航空数据分析中固有的挑战。此外，还提出了未解决的重要问题，为未来的航空数据分析研究方向铺平了道路。