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Keywords = MVPOD dataset

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23 pages, 35691 KB  
Article
MVPOD: A Dataset and Benchmark for Multi-Vertical-Perspective Object Detection in Multi-Platform Remote Sensing Images
by Haiyan Jin, Jintao Chen, Yuanlin Zhang, Haonan Su and Bin Wang
Remote Sens. 2025, 17(17), 3029; https://doi.org/10.3390/rs17173029 - 1 Sep 2025
Cited by 1 | Viewed by 2363
Abstract
Deep learning-based object detection has achieved remarkable maturity after years of intensive research. However, as multi-platform data acquisition becomes increasingly prevalent, spanning satellite, UAV, and ground-based platforms, a critical challenge emerges involving significant vertical perspective variations in captured images. The current object detection [...] Read more.
Deep learning-based object detection has achieved remarkable maturity after years of intensive research. However, as multi-platform data acquisition becomes increasingly prevalent, spanning satellite, UAV, and ground-based platforms, a critical challenge emerges involving significant vertical perspective variations in captured images. The current object detection literature largely neglects this perspective dimension, particularly the robustness evaluation of single models across diverse viewing angles. To bridge this gap, we first conduct a systematic review categorizing existing approaches into standard and rotated object detection paradigms. Second, we build the Multi-Vertical-Perspective Object Detection (MVPOD) dataset; this dataset is the first comprehensive benchmark integrating spaceborne (nadir), airborne (oblique) and ground-level (horizontal) imagery with dual annotation schemes. Third, rigorous cross-perspective evaluation protocols reveal that vertical viewpoint discrepancies cause measurable performance degradation. Finally, representative methods are benchmarked on the MVPOD dataset, establishing baselines for future research. Full article
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