UAV (Unmanned Aerial Vehicles): Diverse Applications of UAV Datasets in Segmentation, Classification, Detection, and Tracking

TL;DR

The paper assesses the landscape of UAV datasets and their diverse applications in segmentation, classification, detection, and tracking from aerial data. It synthesizes 15 key datasets, detailing their data modalities, tasks, and the state-of-the-art methods applied, including multimodal fusion, transfer learning, and novel trackers. Core contributions include a taxonomy of UAV dataset uses, an analysis of data diversity and methodological trends, and a discussion of constraints (data quality, annotation, computation, and ethics) alongside future research directions. The study highlights how UAV datasets enable robust vision systems for disaster management, surveillance, traffic analysis, urban and forestry applications, and nighttime/all-day tracking with real-time capabilities, thereby accelerating practical deployment. Overall, UAV datasets are central to advancing computer vision in aerial contexts, with growing emphasis on multimodal data, standardization, and open collaboration to realize their full potential across industries.

Abstract

Unmanned Aerial Vehicles (UAVs), have greatly revolutionized the process of gathering and analyzing data in diverse research domains, providing unmatched adaptability and effectiveness. This paper presents a thorough examination of Unmanned Aerial Vehicle (UAV) datasets, emphasizing their wide range of applications and progress. UAV datasets consist of various types of data, such as satellite imagery, images captured by drones, and videos. These datasets can be categorized as either unimodal or multimodal, offering a wide range of detailed and comprehensive information. These datasets play a crucial role in disaster damage assessment, aerial surveillance, object recognition, and tracking. They facilitate the development of sophisticated models for tasks like semantic segmentation, pose estimation, vehicle re-identification, and gesture recognition. By leveraging UAV datasets, researchers can significantly enhance the capabilities of computer vision models, thereby advancing technology and improving our understanding of complex, dynamic environments from an aerial perspective. This review aims to encapsulate the multifaceted utility of UAV datasets, emphasizing their pivotal role in driving innovation and practical applications in multiple domains.

Figures (17)

• Figure 1: Workflow of this Study
• Figure 2: Diverse Applications of UAV Datasets in Computer Vision Research
• Figure 3: Aerial Image Dataset for Applications in Emergency Response (AIDER): A selection of pictures from the Augmented Database
• Figure 4: Illustrations of the flapping-wing UAV used for data collection and the representative data of BioDrone. Different flight attitudes for various scenes under three lighting conditions are included in the data acquisition process, ensuring that BioDrone can fully reflect the robust visual challenges of the flapping-wing UAVs.
• Figure 5: Overview of the ERA dataset. Overall, they have collected 2,864 labeled video snippets for 24 event classes and 1 normal class: post-earthquake, flood, fire, landslide, mudslide, traffic collision, traffic congestion, harvesting, ploughing, constructing, police chase, conflict, baseball, basketball, boating, cycling, running, soccer, swimming, car racing, party, concert, parade/protest, religious activity, and non-event. For each class, we show the first (left) and last (right) frames of a video. Best viewed zoomed in color.