## 5 Research Gaps and Prospects  
### Data Ecosystem Development: From Ad Hoc Integration to a Standardized Data Framework  
Lack of unified data standards, metadata specifications, and reliable data-sharing mechanisms often leads to the proliferation of \"data silos.\" How can we establish a UAV-RS data-sharing mechanism and technical framework that balances data sovereignty, commercial confidentiality, and the principles of open science? How can we define a set of open standards encompassing data acquisition, processing, and metadata to enable true interoperability across global platforms? Future efforts must prioritize enhancing the interoperability and integrated analysis capabilities of UAV-RS systems--this is a critical step toward advancing UAV-RS applications. Research must overcome key obstacles in integrating multi-source and heterogeneous data across access, registration, fusion, and modeling stages. To this end, two main avenues are proposed: standardization of data frameworks and the development of fusion algorithms. This dual approach--merging standardization with algorithmic innovation--is key to unlocking the full value of UAV-RS data in global environmental monitoring and decision-making. (1) Standardized Data Interfaces and Metadata Management. To address inconsistencies in resolution, spectral bands, and coordinate systems across various sensors (optical, multispectral, thermal infrared, LiDAR, SAR), it is essential to establish unified data description and metadata standards (e.g., OGC SensorML, ISO 19156). This will enable plug-and-play access to different sensors and facilitate automated calibration and registration during preprocessing of multi-source data [145; 146; 231]. (2) Multi-Source Data Fusion Algorithms and Spatiotemporal Interpolation. Research should combine traditional pixel-level fusion methods (e.g., weighted averaging, principal component analysis) with feature-level fusion based on deep learning. Developing modelssuch as Cross-Modal Transformers and Graph Neural Networks (GNNs) can enable synergistic extraction and complementary enhancement of multi-source information [201; 202; 203]. In terms of spatiotemporal continuity, time-series models (e.g., temporal convolutional networks, autoregressive models) can be used for data interpolation and forecasting, improving the timeliness and accuracy of dynamic monitoring. Breakthroughs in Core Technology Platforms: Integration of Intelligent Flight Platforms and Edge Computing  
Platform endurance, payload capacity, and autonomous navigation are currently the primary bottlenecks in UAV-based remote sensing (UAV-RS) platforms. As UAV-RS application scenarios grow increasingly complex and task demands escalate, the technology is shifting from a \"flight-centered observational tool\" to a \"cooperative system driven by intelligent sensing and decision-making.\" This transition highlights the urgent need to address the lag in fundamental platform technologies exposed by the \"application-driven\" evolution model. Focused innovation is essential in three core areas: platform performance, intelligent computing, and system integration. (1) What is the optimal technological pathway for next-generation UAV power systems (e.g., solid-state batteries, hydrogen fuel cells) across diverse application scenarios, balancing energy density, safety, and cost? Future UAV platforms must simultaneously enhance payload capacity and flight endurance while reducing airframe weight and energy consumption. High specific energy batteries--such as solid-state lithium, lithium-sulfur, and hydrogen fuel cells--offer promising potential, but require breakthroughs in cost-effectiveness, reliability, and safety [171; 172]. Meanwhile, airframe designs should adopt carbon-fiber composites and topology optimization to minimize structural weight and enhance wind resistance.