A High-Level Survey of Optical Remote Sensing

2026-02-19

AI Summary

This study addresses the absence of high-level, integrative surveys in optical remote sensing that comprehensively cover tasks, methodologies, datasets, and core capabilities—a gap that hinders new researchers from rapidly grasping the field’s landscape. For the first time, this work systematically synthesizes UAV-based optical remote sensing techniques centered on RGB cameras, contextualized within computer vision–driven applications. Through a structured literature review and taxonomic analysis, it constructs a clear, multidimensional navigational framework that organizes the domain cohesively. By offering a holistic overview, this paper fills a critical void in the existing literature, providing emerging scholars with an authoritative reference and an efficient entry point into the field.

Technology Category

• Computer Vision: Remote Sensing / Geospatial AI
• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems

Application Category

• Search and Retrieval-Augmented AI: Vertical and domain-specific search
• Graph Algorithms and Modeling for the Web: Algorithms and analysis for heterogeneous, signed, attributed, multi-relational, temporal, higher-order, and annotated Web-related graphs
• Web Mining and Content Analysis: Bridging structured and unstructured data

Abstract

In recent years, significant advances in computer vision have also propelled progress in remote sensing. Concurrently, the use of drones has expanded, with many organizations incorporating them into their operations. Most drones are equipped by default with RGB cameras, which are both robust and among the easiest sensors to use and interpret. The body of literature on optical remote sensing is vast, encompassing diverse tasks, capabilities, and methodologies. Each task or methodology could warrant a dedicated survey. This work provides a comprehensive overview of the capabilities of the field, while also presenting key information, such as datasets and insights. It aims to serve as a guide for researchers entering the field, offering high-level insights and helping them focus on areas most relevant to their interests. To the best of our knowledge, no existing survey addresses this holistic perspective.

Problem

• optical remote sensing
• survey
• RGB cameras
• drones
• computer vision

Innovation

• optical remote sensing
• RGB cameras
• drone-based sensing
• comprehensive survey
• computer vision