Optimizing rural waste management: Leveraging high-resolution remote sensing and GIS for efficient collection and routing

Abstract

Accurate assessment of distribution patterns and dynamic insights into rural populations is pivotal for comprehending domestic waste generation, recycling, and transportation in rural territories. Given that the dispersion of rural inhabitants exhibits minimal variation and maintains stability, this research endeavors to establish a pragmatic model for rural domestic waste collection and routing, leveraging the capabilities of very high-resolution remote sensing combined with geographic information system (GIS) techniques. Specifically, the Dilated LinkNet model was employed to discern features such as buildings, roads, water bodies, farmlands, and forests from the high-resolution remote sensing imagery. A novel multiple K-means clustering approach was devised for building segmentation. Within these clusters, an assortment of spatial regulations and evaluations facilitated the judicious selection of environmentally-conscious waste collection sites (WCSs). The Pointer Network, augmented with reinforcement learning, executed a traveling salesman analysis on these chosen WCSs, yielding the optimal collection trajectory. Validated in Huangtu Town, a quintessential rural region in China, our model manifested superior recognition precision, recording IoU accuracies of 0.902, 0.926, 0.933, 0.891, and 0.849 for buildings, roads, water bodies, farmlands, and forests respectively. Notably, when compared to our field survey data, the optimized daily collection route in a rural context decreased from 256.40 km before optimization to 140.44 km, reflecting a substantial reduction of 45.23% in total distance. This study furnishes an effective model that relies solely on information from remote-sensing images for efficient rural waste collection and extends invaluable insights to planners and administrators in the realm of rural and township waste management.