In this paper, we research multi-robot coverage path planning (MCPP), which generates paths for agents to visit all target areas or points. This problem is common in various fields, such as agriculture, rescue, 3D scanning, and data collection. Algorithms to solve MCPP are generally categorized into online and offline methods. Online methods work in an unknown area, while offline methods generate a path for the known. Recently, offline MCPP has been researched through various approaches, such as graph clustering, DARP, genetic algorithms, and deep learning models. However, many previous algorithms can only be applied on grid-like environments. Therefore, this study introduces an offline MCPP algorithm that applies graph-adapted K-means and spanning tree coverage for robust operation in non-grid-structure maps such as road networks. To achieve this, we modify a cost function based on the travel distance by adjusting the referenced clustering algorithm. Moreover, we apply bipartite graph matching to reflect the initial positions of agents. We also introduce a cluster-level graph to alleviate local minima during clustering updates. We compare the proposed algorithm with existing methods in a grid environment to validate its stability, and evaluation on a road network digital twin validates its robustness across most environments.

Accurate flood risk assessments are critical for mitigating the impacts of pluvial flooding in densely populated urban areas. However, conventional flood modeling approaches often face limitations due to the lack of measurement information. To address this challenge, we develop, implement, and evaluate a novel framework that integrates crowdsourced data, such as citizen observations, with process-based modeling to enhance the accuracy of urban flood risk assessments. The proposed method utilizes indicator co-kriging techniques to merge citizen-sourced data with auxiliary variables, including inundation maps generated from 1D-2D urban flood models driven by high-resolution radar rainfall estimates. The framework is applied to the Oncheon River catchment in Busan, South Korea, a region highly vulnerable to pluvial flooding due to its urbanization and complex hydrological conditions. To evaluate the method, synthetic citizen observation data were generated based on inundation maps. These synthetic experiments assess the influence of the spatial distribution and quality of citizen observations on urban flood risk predictions. This study examines the integration of citizen observations into urban flood modeling workflows to address uncertainties in models and observations. In particular, we investigate the extent to which distributed citizen observations enhance prediction accuracy and analyze the effects of model bias on the reliability of flood risk assessments. The study quantitatively evaluates the effects of citizen data quality and spatial distribution on the accuracy of urban flood risk mapping. Furthermore, a sensitivity analysis is conducted for co-kriging parameters, focusing on semivariogram model selection and its influence on prediction accuracy.