# AeroRIT: A New Scene for Hyperspectral Image Analysis

arXiv: https://arxiv.org/abs/1912.08178 · HTML: https://mlsanity.com/paper/1912.08178/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Dec 2019.

## Built on by (most cited first, 10 of 10)

- [EarthNets: Empowering AI in Earth Observation](https://mlsanity.com/paper/2210.04936/) (2022)
- [Semantic Segmentation with Active Semi-Supervised Learning](https://mlsanity.com/paper/2203.10730/) (2022)
- [SPECIAL: Zero-shot Hyperspectral Image Classification With CLIP](https://mlsanity.com/paper/2501.16222/) (2025)
- [HyperCap: Hyperspectral Land Cover Captioning Dataset for Vision Language Models](https://mlsanity.com/paper/2505.12217/) (2025)
- [CEU-Net: Ensemble Semantic Segmentation of Hyperspectral Images Using Clustering](https://mlsanity.com/paper/2203.04873/) (2022)
- [SpecAware: A Spectral-Content Aware Foundation Model for Unifying Multi-Sensor Learning in Hyperspectral Remote Sensing Mapping](https://mlsanity.com/paper/2510.27219/) (2025)
- [spectrai: A deep learning framework for spectral data](https://mlsanity.com/paper/2108.07595/) (2021)
- [Self-Supervised Pretraining for Aerial Road Extraction](https://mlsanity.com/paper/2503.24326/) (2025)
- [From Pixels to Images: A Structural Survey of Deep Learning Paradigms in Remote Sensing Image Semantic Segmentation](https://mlsanity.com/paper/2505.15147/) (2025)
- [Prior-aware and Context-guided Group Sampling for Active Probabilistic Subsampling](https://mlsanity.com/paper/2607.07083/) (2026)

## Abstract (arXiv)

We investigate applying convolutional neural network (CNN) architecture to facilitate aerial hyperspectral scene understanding and present a new hyperspectral dataset-AeroRIT-that is large enough for CNN training. To date the majority of hyperspectral airborne have been confined to various sub-categories of vegetation and roads and this scene introduces two new categories: buildings and cars. To the best of our knowledge, this is the first comprehensive large-scale hyperspectral scene with nearly seven million pixel annotations for identifying cars, roads, and buildings. We compare the performance of three popular architectures - SegNet, U-Net, and Res-U-Net, for scene understanding and object identification via the task of dense semantic segmentation to establish a benchmark for the scene. To further strengthen the network, we add squeeze and excitation blocks for better channel interactions and use self-supervised learning for better encoder initialization. Aerial hyperspectral image analysis has been restricted to small datasets with limited train/test splits capabilities and we believe that AeroRIT will help advance the research in the field with a more complex object distribution to perform well on. The full dataset, with flight lines in radiance and reflectance domain, is available for download at https://github.com/aneesh3108/AeroRIT. This dataset is the first step towards developing robust algorithms for hyperspectral airborne sensing that can robustly perform advanced tasks like vehicle tracking and occlusion handling.