GSD (grain size distribution), constitutes a paramount parameter for comprehending the behavior and dynamic mechanics of mass movements, such as debris flows, rock avalanches, sediment transport etc. Alongside traditional sieving methodologies, the past few decades have witnessed a growing interest in photo-sieving, the technique of deducing GSD directly from photographic data. Photo-sieving holds promise for augmenting the spatial and temporal resolution of superficial GSD analysis by virtue of its accessibility, reduced labor intensity, and non-invasive nature. Moreover, the integration of aerial photography within the discipline enables to include the coarse-grained fraction, expanding the scope of particle size analysis beyond the capabilities of traditional sieving. This study introduces a novel algorithm for extracting the coarse-grained fraction using UAV (unmanned aerial vehicle) photography. This novel approach enables us to analyze hectare-scale extents, probing tens of thousands of clasts - surpassing previous similar techniques by two orders of magnitude - and generates a detailed map of the position and dimensions of each particle within the sedimentary system. Furthermore, the algorithm exhibits remarkable resilience in navigating real-world complexities, effectively discerning clasts from vegetation, anthropogenic artifacts, and handling exceptionally large boulders, rendering it suitable for application in diverse field settings. We anticipate that this technique could become a valuable tool for advancing our understanding of debris flow and rock avalanche dynamics, sediment transport processes, and the stability of landslide dams.