Revisiting Aerial Scene Classification on the AID Benchmark
Abstract
Aerial images play a vital role in urban planning and environmental preservation, as they consist of various structures, representing different types of buildings, forests, mountains, and unoccupied lands. Due to its heterogeneous nature, developing robust models for scene classification remains a challenge. In this study, we conduct a literature review of various machine learning methods for aerial image classification. Our survey covers a range of approaches from handcrafted features (e.g., SIFT, LBP) to traditional CNNs (e.g., VGG, GoogLeNet), and advanced deep hybrid networks. In this connection, we have also designed Aerial-Y-Net, a spatial attention-enhanced CNN with multi-scale feature fusion mechanism, which acts as an attention-based model and helps us to better understand the complexities of aerial images. Evaluated on the AID dataset, our model achieves 91.72% accuracy, outperforming several baseline architectures.
Cite
@article{arxiv.2601.18263,
title = {Revisiting Aerial Scene Classification on the AID Benchmark},
author = {Subhajeet Das and Susmita Ghosh and Abhiroop Chatterjee},
journal= {arXiv preprint arXiv:2601.18263},
year = {2026}
}
Comments
Presented at the IEEE India Geoscience and Remote Sensing Symposium 2025 and accepted for publication in IEEE Xplore