# A UAV-Based VNIR Hyperspectral Benchmark Dataset for Landmine and UXO Detection

arXiv: https://arxiv.org/abs/2510.02700 · HTML: https://mlsanity.com/paper/2510.02700/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 11 references that are in the map.
- First posted on arXiv 3 Oct 2025.

## Builds on (most cited first, 1 of 1)

- [MineInsight: A Multi-sensor Dataset for Humanitarian Demining Robotics in Off-Road Environments](https://mlsanity.com/paper/2506.04842/) (2025)

## Built on by (most cited first, 2 of 2)

- [Human-in-the-Loop Signature Bootstrapping for UAV Hyperspectral PFM-1 Mine Detection](https://mlsanity.com/paper/2607.25310/) (2026)
- [SULAND v2: A Refined RGB Dataset and Deep Learning Object Detection Benchmark for UAV/UGV-Based SUrface LANDmine Detection Under Domain Shift](https://mlsanity.com/paper/2607.28996/) (2026)

## Abstract (arXiv)

This paper introduces a novel benchmark dataset of Visible and Near-Infrared (VNIR) hyperspectral imagery acquired via an unmanned aerial vehicle (UAV) platform for landmine and unexploded ordnance (UXO) detection research. The dataset was collected over a controlled test field seeded with 143 realistic surrogate landmine and UXO targets, including surface, partially buried, and fully buried configurations. Data acquisition was performed using a Headwall Nano-Hyperspec sensor mounted on a multi-sensor drone platform, flown at an altitude of approximately 20.6 m, capturing 270 contiguous spectral bands spanning 398-1002 nm. Radiometric calibration, orthorectification, and mosaicking were performed followed by reflectance retrieval using a two-point Empirical Line Method (ELM), with reference spectra acquired using an SVC spectroradiometer. Cross-validation against six reference objects yielded RMSE values below 1.0 and SAM values between 1 and 6 degrees in the 400-900 nm range, demonstrating high spectral fidelity. The dataset is released alongside raw radiance cubes, GCP/AeroPoint data, and reference spectra to support reproducible research. This contribution fills a critical gap in open-access UAV-based hyperspectral data for landmine detection and offers a multi-sensor benchmark when combined with previously published drone-based electromagnetic induction (EMI) data from the same test field.