More Data, More Problems?
Exploring the Impacts of Using External Datasets when Training Deep Learning Models for Remote Sensing Applications
Copyright (c) 2026 Hammas Attila
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
Absztrakt
The resurgence of large-scale conventional warfare, exemplified by the ongoing conflict in Ukraine, has highlighted the critical importance of modern technologies in enhancing situational awareness and target acquisition on the battlefield. In particular, the integration of unmanned aerial vehicles (UAVs) and artificial intelligence (AI)-driven computer vision systems has emerged as a key enabler of real-time intelligence and precision engagement. This paper presents an approach to enhance object detection models on aerial imagery for military applications. Initial experiments revealed shortcomings in detecting certain object classes, particularly in complex environments and under variable lighting conditions. To address these issues, the paper investigates impacts of cross-dataset training to improve the robustness and accuracy of object detection models. Through selective label integration and careful dataset curation, the paper demonstrates that incorporating assets form external sources significantly enhances generalisation and detection performance. The results underline the potential of leveraging large-scale annotated datasets to augment domain-specific applications with minimal additional labelling cost.
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