Seafloor sediment mapping traditionally relies on the experience and expertise of practitioners to classify sediment classes based on acoustic backscatter data and ground-truth information. However, recent advancements in automated seafloor mapping present a major development in objective methods that offer practical application for seafloor mapping and monitoring campaigns. In this study, a class-specific approach of ensemble modelling (ensemble mapping) was used to classify the sediment classes of a large-scale seafloor area (1550 km2) in the western Sylt Outer Reef, German North Sea. A pixel-by-pixel comparison of the modelled map and manually digitized map was also conducted to assess the efficiency of the ensemble mapping approach. The resulting seafloor sediment map, with an overall accuracy of 73%, demonstrates five sediment classes that represent most of the seabed of the German North Sea. The manually classified and ensembled maps were 63% identical, but mismatches were observed in the transitional boundaries of soft sediment classes and in stony areas that were not predicted in manual classification. The inconsistencies between the two maps was attributed to the different interpretation of sediment boundaries, the simplification of the sediment classification scheme, and the ability of ensemble mapping to classify more areas than manual classification. This study found that ensemble mapping performs better in characterizing coarse materials and produces maps that are comparable to the maps produced by manual classification, while the production time and degree of subjectivity in the analysis are minimal. Hence, ensemble mapping is a viable alternative to create baseline seafloor sediment maps that can be used for environmental monitoring and resource planning.