## 4 Core Insights and Critical Reflections
### Challenges Facing UAV-RS
#### 4.3.2 Data Processing and Analysis Challenges  
UAV-RS technology has made continuous breakthroughs in high-resolution imaging, low-altitude flexible observation, and intelligent data processing. However, its large-scale deployment still faces deep-rooted data-related challenges. With the continuous improvement of sensor resolution and acquisition frequency, the volume of UAV-RS generated data is growing exponentially, resulting in an increasingly prominent issue of \"data overload.\" From data acquisition to transmission, storage, processing, integration, and sharing, each stage imposes greater demands on computational resources, algorithm efficiency, system architecture, and regulatory oversight.  
(1) Massive Data Storage and Management. High-resolution optical, multispectral, and hyperspectral sensors often generate data measured in gigabytes (GB) or even terabytes (TB). Traditional storage systems and network bandwidth struggle to support real-time data transmission and processing, especially in remote monitoring scenarios [188; 189; 190]. The costs and complexity of data storage have significantly increased, making efficient compression and rapid retrieval essential while ensuring data integrity.  
(2) Data Quality and Noise Suppression. Flight attitude jitter, illumination variation, sensor calibration errors, and adverse weather conditions introduce noise and image distortion, which undermine down-stream analysis accuracy [44; 46]. Sensor discrepanciescan lead to systematic biases [191; 192; 193], and improper exposure or white balance settings may reduce image quality [194; 195]. Therefore, the development of adaptive radiometric calibration, motion compensation, and multi-source noise suppression algorithms is crucial.  
(3) Massive Data Processing Pressure and Algorithm Complexity. UAV-RS generates data with high temporal frequency and spatial resolution--hundreds of GBs from a single flight. Traditional image processing methods are inadequate for automated, rapid response and large-scale analysis [196; 197; 198; 199]. Although deep learning has shown promise in remote sensing, it requires large annotated datasets and high computational power [200], making it resource-intensive and not yet fully suitable for real-time field operations. This calls for more efficient algorithms and advanced hardware to handle large-scale remote sensing data in real time.  
(4) Multi-Source Data Fusion and Heterogeneous Processing. Discrepancies in spatial and temporal resolution, coordinate systems, and data formats among optical, infrared, LiDAR, and SAR data increase the difficulty of geometric correction and fusion [201; 202; 203; 204]. Processing heterogeneous data demands seamless integration across platforms, necessitating improved data standardization and protocol unification. Real-time applications like disaster monitoring require efficient multi-source fusion frameworks and parallel computing strategies [205; 31; 206], as well as enhanced quality assessment and uncertainty analysis [207; 208; 209]. The development of standardized multi-source remote sensing frameworks is imperative [24; 199; 200; 201]. Solving these challenges requires interdisciplinary collaboration, novel algorithms and techniques, and the promotion of interoperability standards.  
Additionally, the diversity of UAV-RS application scenarios and sector-specific data standards have led to data silos and redundant efforts. Unifying data formats, metadata specifications, and quality assessment procedures is essential for enabling cross-platform and cross-domain data sharing and reuse. Moreover, UAVs can easily collect personal and sensitive geospatial information, raising legal and ethical concerns [23; 210; 211]. Most countries' legal frameworks remain underdeveloped, and UAV-RS use in urban areas lacks clear regulation [212; 213; 214; 215; 216]. It is therefore urgent to establish technical standards and legal frameworks covering data encryption, access control, and accountability.