A Route Optimization Framework for Vehicle-Based Mobile Remote Sensing

Optimizing the informational return of measurements along existing road networks is a big challenge for real life data acquisition. Here, informational return describes the effectiveness of a survey in capturing the spatial variability of the target variable, ensuring that measurements provide maximal knowledge and minimize uncertainty when used to generate spatial maps or inform predictive models. In this study we develop a two-stage survey design framework that fuses auxiliary spatial data with road network data and formulates route planning as a combinatorial optimization problem. By integrating a fuzzy-clustered representation of the survey area heterogeneity with the road network, we identify map grid nodes reachable by a vehicle. Information values are assigned to individual road segments using fuzzy membership values and Shannon Entropy. Informative segments are selected, and the most informative pathways between them are constructed using Dijkstra’s Algorithm. An Information-rich initial route is then generated using Ant Colony Optimization (ACO).

To further economize this initial route, spatial coverage is characterized by computing a convex hull in the auxiliary data space. A subset of map grid nodes is heuristically selected to preserve most of the convex hull volume while keeping the computational cost manageable. The shortest paths between road segments covering these nodes are determined with the A* algorithm.  The ACO is applied to construct the final economized route that is both information-rich and distance-optimized.

The framework is evaluated using a large-scale case study based on mobile Cosmic Ray Neutron Sensing (CRNS) soil moisture measurements over a 4500 km² area in northeastern Germany. Compared to an empirically designed route of similar length, the optimized route substantially reduces uncertainty in regression-based soil moisture regionalization (i.e., map generation) while significantly improving spatial coverage of the survey area, data collection efficiency, and data quality. The proposed approach provides a systematic alternative to convenience-based sampling strategies commonly used in Earth and environmental sciences.