Image cross‐correlation techniques, such as particle image velocimetry (PIV), can estimate water surface velocity (v _surf) of streams. However, discharge estimation requires water depth and the depth‐averaged vertical velocity (U _m ). The variability of the ratio U _m /v _surf introduces large errors in discharge estimates. We demonstrate a method to estimate v _surf from Unmanned Aerial Systems (UASs) with PIV technique. This method does not require any ground control point (GCP): the conversion of velocities from pixels per frame into length per time is performed by informing a camera pinhole model; the range from the pinhole to the water surface is measured by the drone‐borne radar. For approximately uniform flow, U _m is a function of the Gauckler‐Manning‐Strickler coefficient (K _s ) and v _surf. We implement an approach that can be used to jointly estimate K _s and discharge by informing a system of two unknowns (K _s and discharge) and two nonlinear equations: i) Manning's equation and ii) mean‐section method for computing discharge from U _m . This approach relies on bathymetry, acquired in situ a priori, and on UAS‐borne v _surf and water surface slope measurements. Our joint (discharge and K _s ) estimation approach is an alternative to the widely used approach that relies on estimating U _m as 0.85·v _surf. It was extensively investigated in 27 case studies, in different streams with different hydraulic conditions. Discharge estimated with the joint estimation approach showed a mean absolute error of 19.1% compared to in situ discharge measurements. K _s estimates showed a mean absolute error of 3 m^1/3/s compared to in situ measurements.

The evolution of multi-constellation Global Navigation Satellite Systems (GNSS) has presented an opportunity to enhance user positioning accuracy. However, practical constraints, such as limited receiver channels and power consumption, necessitate judicious satellite selection. Geometric Dilution of Precision (GDOP) serves as a critical indicator for optimizing positioning performance, but determining the subset with the optimal GDOP value involves solving an impractical combinatorial optimization problem. A compromise solution seeks to balance computational complexity while sacrificing some optimality. Consequently, finding an optimal combination of satellites with a low computational burden yet quasi-optimal GDOP value remains a challenge. In response to this challenge, we introduce a pioneering approach in this paper: a super-fast satellite selection algorithm based on power series expansion (SF-PSE). This paper derives a “Xiao-Liang formula” based on power series expansion and the Sherman-Morrison formula. Using this formula, we propose two low-computational-cost rapid iterative algorithms (F-PSE and SF-PSE). Among these algorithms, SF-PSE notably reduces the computational burden through an approximate iterative inverse matrix-guided search method. Experimental results demonstrate that satellite combinations identified by F-PSE and SF-PSE yield nearly the same accuracy while reducing computation time by 70% and 87%, respectively, compared to the SMALLER method.

As urban aerial mobility (UAM) infrastructure development accelerates globally, cities like Shenzhen are planning large-scale vertiport networks (e.g., 1,200+ facilities by 2026). Existing planning frameworks remain inadequate for this complexity due to historical limitations in data granularity and real-world applicability. This paper addresses these gaps by first proposing the Capacitated Dynamic Maximum Covering Location Problem (CDMCLP), a novel optimization framework that simultaneously models urban-scale spatial-temporal demand, heterogeneous user behaviors, and infrastructure capacity constraints. Building on this foundation, we introduce an Integrated Planning Recommendation System that combines CDMCLP with socio-economic factors and dynamic clustering initialization. This system leverages adaptive parameter tuning based on empirical user behavior to generate practical planning solutions. Validation in a Chinese center city demonstrates the effectiveness of the new optimization framework and recommendation system. Under the evaluation and optimization of CDMCLP, the quantitative performance of traditional location methods are exposed and can be improved by 38\%--52\%, while the recommendation system shows user-friendliness and the effective integration of complex elements. By integrating mathematical rigor with practical implementation considerations, this hybrid approach bridges the gap between theoretical location modeling and real-world UAM infrastructure planning, offering municipalities a pragmatic tool for vertiport network design.