Stock assessment models aim to get reliable estimates of population abundance for the provision of science advice to fisheries managers. Many of these models are strong at dealing with various types of autocorrelations, but often underutilize alternative data (e.g., drop-camera surveys and seascape maps) to help improve their prediction and estimation capacities. Furthermore, how to include these novel sources of information is an open question. We focus here on the development of a spatially explicit habitat based assessment model (SEHBAM) that accounts for habitat through the inclusion of seascape maps using the Maritimes Inshore Sea Scallop as a case study. Results show improvements in estimating probabilities of encounter and more realistic catchabilities, providing an improved understanding of population dynamics. Finally, methods for the further inclusion of a drop-camera survey are proposed to account for the different resolution and precision associated with these new data.