## 6 Conclusions  
With the continuous advancement of remote sensing technologies and the rapid development of UAV platforms, unmanned aerial vehicle remote sensing (UAV-RS) has become a vital component of the global Earth observation system. Based on a systematic scientometric analysis of relevant literature from the Web of Science database, this study presents a diagnostic review of the global research landscape, paradigm evolution, and underlying challenges in the UAV-RS field. It goes beyond traditional descriptive reviews by offering deeper analytical insights. The main findings are as follows:  
1. Since 2015, UAV-RS research has entered a phase of rapid growth. Research priorities have expanded from early focuses such as image acquisition and platform performance to more advanced areas, including multi-source data fusion, deep learning-based interpretation, 3D modeling, and real-time monitoring--reflecting a paradigm shift from \"image acquisition\" to \"intelligent sensing.\" The field is highly interdisciplinary, encompassing remote sensing, environmental science, geographic information systems (GIS), engineering, and agricultural sciences. Keyword clustering analysis highlights prominent research hotspots such as vegetation indices, 3D modeling, image recognition, deep learning, and ecological stress, characterized by a high degree of technological integration and application-driven development. 2. A key finding of this study is that while the UAV-RS field is experiencing exponential growth, it is also facing three major structural imbalances: 1. A dual-core but weakly coupled global collaboration structure: China and the United States are the two major innovation hubs, yet direct cooperation between them remains limited, raising concerns about the formation of technological silos. We suggest fostering innovative collaboration models, such as: establishing multilateral alliances centered on global challenges (e.g., climate change, biodiversity, food security); creating unified data-sharing protocols and interoperability standards; initiating cross-border mobility programs for early-career researchers and joint field observation projects to promote knowledge exchange and methodological synergy. 2. An application-heavy, foundation-light research pattern: The academic agenda is heavily skewed toward immediate application needs--such as precision agriculture--while foundational technologies critical for long-term development, including endurance, sensor calibration, and data standardization, are comparatively neglected. We recommend that research funding agencies rebalance priorities and support key technological directions such as: high-efficiency power systems (e.g., fuel cells, hybrid UAVs); ultralight, high-resolution multi-sensor integration (e.g., LiDAR + hyperspectral); AI-enhanced autonomous navigation and flight planning; and lightweight real-time processing architectures for edge applications. Long-term investment is also needed in open-source toolchains, multi-environment test platforms, and interdisciplinary research infrastructures to avoid shallow innovation and redundant development. 3. A cognitive gap in the data ecosystem: Although researchers show strong interest in multi-source data fusion, there is a systemic neglect of the necessary data standards and interoperability frameworks. We recommend that international remote sensing standardization bodies (e.g., OGC, ISO/TC 211) take the lead in developing open standards covering sensor calibration, spatial accuracy, and data exchange formats. 3. The unique contribution of this study lies in advancing scientometric analysis from a descriptive inquiry of \"what is\" to a diagnostic critique of \"why it matters.\" Through quantitative evidence, this study reveals the overwhelming dominance of application-driven research, the hidden impact of cost-effectiveness on technology adoption (e.g., SfM vs. LiDAR), and the notable absence of critical discourse around data standards and interoperability in the literature.