Kochura, YuriyTrochun, YevheniiTaran, VladyslavGordienko, YuriRokovyi, OleksandrStirenko, Sergii2026-02-062026-02-062025UAeroNet: domain-specific dataset for automation of unmanned aerial vehicles / Yuriy Kochura, Yevhenii Trochun, Vladyslav Taran, Yuri Gordienko, Oleksandr Rokovyi, Sergii Stirenko // Information, Computing and Intelligent systems. – 2025. – No. 7. – P. 83-95. – Bibliogr.: 16 ref.https://ela.kpi.ua/handle/123456789/78688This paper addresses the challenges and key principles of designing domain-specific datasets that canbe used especially for automation of unmanned aerial vehicles. Such datasets play a key role in buildingintelligent systems that enable autonomous operation and support data-driven decisions. The study presentsapproaches we used for data collection, analysis and annotation, highlighting their importance and practicalimpact on real-world application. The preparation of a domain-specific dataset for automating unmannedaerial vehicles operations (such as navigation and environmental monitoring) is a challenging task due tofrequently low image resolution, complex weather conditions, a wide range of object scales, backgroundnoise and heterogeneous terrain landscapes. Existing open datasets typically cover only a limited variety ofunmanned aerial vehicles use cases, which restricts the ability of deep learning models to perform adequatelyunder non-standard or unpredictable conditions.The object of the study is video data acquired by unmanned aerial vehicles for creating domain-specificdatasets that enable machine learning models to perform autonomous object recognition, navigation, obstacleavoidance and interaction with an environment with minimal operator involvement. The subject focuseson the collection, preparation and annotation of video data acquired by unmanned aerial vehicles. Thepurpose of the study is to develop and systematize workflow for creating specialized datasets to trainrobust models capable of autonomously recognizing objects in real-time video captured by unmanned aerialvehicles. To achieve this goal, a workflow was designed for collecting and annotating video data, raw videodata were acquired from unmanned aerial vehicles sensors and manually annotated using the ComputerVision Annotation Tool.As a result of this work, we developed a domain-specific dataset (UAeroNet) using an open-sourceannotation tool for object tracking task in real scenarios.UAeroNetconsists of 456 annotated tracks and atotal of 131 525 labeled instances that belong to 13 distinct classes.enunmanned aerial vehiclesUAeroNetobject detectionautonomous navigationcomputer visionбезпілотні літальні апаративиявлення об'єктівавтономна навігаціякомп'ютерний зірArticleP. 83-95https://doi.org/10.20535/2786-8729.7.2025.341779004.80000-0002-4217-81520000-0002-2744-66810000-0003-2493-72390000-0003-2682-46680000-0001-6934-75020000-0001-5478-0450