Agile Earth Observation Satellites (AEOSs), with their maneuverability, can flexibly observe point, line and region targets. However, existing research typically requires distinct algorithms for each target type, lacking a unified modeling and solution framework, which hinders the ability to meet the demands of rapid and coordinated observation of multiple target types in complex scenarios. To address these issues, this paper proposes a unified scheduling model for agile Earth observation satellites based on the Degenerate Quadtree Grid (DQG) and Proximal Policy Optimization (PPO), termed AEOSSP-USM. Firstly, the DQG is first employed to enable unified management and integrated modeling of point, line, and area targets; Secondly, traditional time window calculations based on longitude and latitude are replaced with grid code-based computations using DQG; Finally, the PPO algorithm, a deep reinforcement learning method, is introduced to formulate AEOSSP-USM as a Markov Decision Process (MDP), enabling efficient problem solving. Experimental results demonstrate that the proposed method effectively realizes unified scheduling of heterogeneous targets, improving imaging quality about 3 times, reducing energy consumption by 10%, decreasing memory usage more than 90%, and enhancing computational efficiency by 35 times compared to conventional longitude-latitude strip algorithm.

Airspace sectorization is an effective approach to balance increasing air traffic demand and limited airspace resources. It directly impacts the efficiency and safety of airspace operations. Traditional airspace sectorization methods are often based on fixed spatial scales, failing to fully consider the complexity and interrelationships of airspace partitioning across different spatial scales. This makes it challenging to balance large-scale airspace management with local dynamic demands. To address this issue, a multi-scale airspace sectorization framework is proposed, which integrates a multi-resolution grid system and a hierarchical deep reinforcement learning algorithm. First, an airspace grid model is constructed using Quaternary Triangular Mesh (QTM), along with an efficient workload calculation model based on grid encoding. Then, a sector optimization model is developed using hierarchical deep Q-network (HDQN), where the top-level and bottom-level policies coordinate to perform global airspace control area partitioning and local sectorization. The use of multi-resolution grids enhances the interaction efficiency between the reinforcement learning model and the environment. Prior knowledge is also incorporated to enhance training efficiency and effectiveness. Experimental results demonstrate that the proposed framework outperforms traditional models in both computational efficiency and workload balancing performance.

The Voronoi diagram on the Earth’s surface is a significant data model, characterized by natural proximity and dynamic stability, which has emerged as one of the most promising solutions for global spatial dynamic management and analysis. However, traditional algorithms for generating spherical raster Voronoi diagrams find it challenging to dynamically adjust the Voronoi diagram while maintaining precision and efficiency. The efficient and accurate construction of the spherical Voronoi diagram has become one of the bottleneck issues limiting its further large-scale application. To this end, this paper proposes a dynamic construction scheme for the spherical Voronoi diagram based on the QTM (Quaternary Triangular Mesh) system, with the aim of enabling efficient generation, local updates, and multi-scale visualization of the spherical Voronoi diagrams. In this paper, canonical ordering is introduced. Tailored for the properties of the spherical triangular grid, it constructs a unified and standardized sorting strategy for the dilation of the spherical grids. The construction and updating of the spherical Voronoi diagram are achieved through the ordered dilation of sites. Furthermore, the multi-scale visualization of the spherical Voronoi diagram is realized through the hierarchical structure of the QTM. The paper presents our algorithm intuitively through pseudocode, conducts comparative experiments on the feasibility and efficiency, and designs an experiment for the dynamic navigation and management of ocean-going vessels based on the global multi-resolution Voronoi diagram. The experimental results demonstrate that our algorithm effectively controls the error of the generation of the raster Voronoi diagram and has a significant efficiency advantage when processing dynamic environments.