Spectral-Spatial Feature Integration for Hyperspectral and LiDAR-Based Urban Land Cover Classification

Authors

  • Aapo R. Rhodes Department of Computer Science and Engineering, University of Nevada, Reno, Reno, NV, USA.

Keywords:

hyperspectral imaging, LiDAR, spectral-spatial fusion, urban land cover classification, deep learning, system architecture, fairness, sustainability, remote sensing infrastructure

Abstract

The fusion of hyperspectral imagery and LiDAR data has emerged as a powerful paradigm for urban land cover classification, leveraging complementary spectral and spatial information to achieve high accuracy in complex built environments. This paper presents a systematic examination of spectral-spatial feature integration within the context of large-scale urban monitoring systems, emphasizing structural trade-offs, architectural design, deployment infrastructure, and governance implications. While numerous convolutional and attention-based fusion approaches have demonstrated superior performance on benchmark datasets, their practical deployment raises critical issues concerning computational scalability, sensor alignment, data heterogeneity, and generalization across diverse urban morphologies. This study analyzes the architectural choices that govern fusion effectiveness, including early, intermediate, and late fusion strategies, and assesses their implications for robustness against noise, missing data, and domain shifts. The discussion extends to infrastructure requirements for real-time or near-real-time classification, including on-board processing constraints, cloud-edge coordination, and energy sustainability. From a policy perspective, this paper highlights fairness and interpretability challenges that arise when fused models are deployed in urban planning, resource allocation, and environmental monitoring. It argues that spectral-spatial fusion systems must be designed not only for accuracy but also for transparency, equity, and long-term operational viability. Case studies from recent urban mapping initiatives illustrate the trade-offs between model complexity and practical utility. The paper concludes by outlining future research directions that integrate self-supervised learning, dynamic sensor tasking, and federated governance frameworks to support resilient and inclusive urban land cover classification at scale.

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Published

2026-05-08

How to Cite

Aapo R. Rhodes. (2026). Spectral-Spatial Feature Integration for Hyperspectral and LiDAR-Based Urban Land Cover Classification. Computer Science and Engineering Transactions, 4(1). Retrieved from https://csetx.org/index.php/cset/article/view/128

Issue

Vol. 4 No. 1 (2026): Computer Science and Engineering Transactions

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