## 4 Core Insights and Critical Reflections
### Challenges Facing UAV-RS
#### 4.3.1 Platform Performance Limitations  
Despite the notable advantages of UAV-RS in platform flexibility, data precision, and low-altitude observation capabilities, its practical deployment still encounters multiple technical and engineering challenges. This study further identifies four core bottlenecks that restrict the large-scale, high-frequency, and high-precision application of UAVs:  
1. Limited Flight Time. For electric UAV platforms, most commercial and industrial-grade drones currently support flight durations of only 30-60 min, constrained by the energy density limits of existing battery technologies [166; 167; 168]. In large-scale or long-term monitoring tasks, the need for frequent returns to replace batteries not only slows mission progress, but also increases safety risks, especially in remote areas or under extreme weather conditions [169]. Insufficient flight time directly affects spatial coverage and mission depth [170]; for instance, when monitoring large-scale regions at high frequencies, limited endurance significantly hampers continuous observation efficiency. To address this, researchers are exploring advanced energy solutions such as lithium-sulfur batteries, hydrogen fuel cells, and hybrid power systems to improve energy density and cycle life [171; 172]. Additionally, flight paths and mission planning are being optimized using genetic algorithms and reinforcement learning to maximize battery efficiency and reduce unnecessary energy consumption [173]. For example, in agricultural monitoring--such as pest infestation assessment--UAV flight routes must account for wind conditions and light availability. However, strong winds or rainy weather can prevent takeoff and landing, further exacerbating the risk of mission disruption due to limited endurance. Currently, research has evolved from single-pass, single-site monitoring to multi-pass, multi-site continuous monitoring. While this enhances monitoring frequency, it also significantly increases operational workload and charging demands [174], placing greater pressure on backend data processing workflows [175]. 2. Limited Payload Capacity. High-resolution optical, multispectral, thermal infrared, and LiDAR sensors are essential for detailed surface monitoring. However, these sensors are often bulky and heavy, placing strict demands on the payload capacity of small UAVs [176]. Currently, lightweight UAVs can typically only carry basic optical cameras, making it difficult to simultaneously conduct multi-modal and multi-angle data acquisition tasks [177]. Although larger UAV platforms are capable of supporting combinations of advanced sensors (e.g., multispectral + LiDAR + thermal infrared), their high cost, operational complexity, and stricter requirements for takeoff and landing areas limit their application in urban or confined environments [176]. While modular designs have improved the flexibility of sensor replacement and integration [178], they have not fundamentally resolved the contradiction between \begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Technological Bottlenecks** & **Specific Manifestations** & **Impact and Challenges** \\ \hline Ability to operate in complex environments & Navigation positioning accuracy decreases in urban canyons, dense forests, and other environments; communication links are easily interfered or blocked; special terrain or lighting conditions affect imaging quality. & Limits its application in complex environments; affects the reliability and integrity of data acquisition. \\ \hline Data management, storage, and sharing & Massive data places high demands on storage and management; the lack of unified data standards and formats makes sharing and interoperability difficult. & Increase data management costs; limit the comprehensive utilization and value mining of data. \\ \hline \hline \end{tabular}
\end{table} 
Table 10: _Cont._payload capacity and energy constraints.