Forest fuels are an essential component of wildfire behavior and risk assessment, but difficult to quantify accurately. As fire spreads into new regions such as Central Europe, the demand for fuels information is increasing. The aim of this study was to develop a methodology to map canopy fuels over large areas and at high spatial resolution, using only open and accessible data. We propose a two-step approach that i) predicts five canopy fuel variables at point-level using measurements from NASA's Global Ecosystem Dynamics Investigation (GEDI) instrument, before ii) interpolating them to wall-to-wall raster maps. Instead of conducting field measurements we generate reference data for Canopy (Base) Height (CH, CBH), Cover (CC), Bulk Density (CBD), and Fuel Load (CFL) by segmenting point clouds from airborne laser scanning and processing tree-level metrics to plot-level fuels estimates via allometric crown biomass models. Point-level Random Forest models were tuned to select optimal hyperparameters and cross-validated using k-fold Nearest Neighbor Distance Matching. Predictions at extgreater1.6 M GEDI measurement locations and multiple biophysical raster covariates serve as input to Universal Kriging to produce 20 meter wall-to-wall maps. Random Forest models showed favourable results, especially for CC (R² = 0.76; RMSE = 9.95%), followed by CBH (R² = 0.68; RMSE = 2.41 m) and CH (R² = 0.67; RMSE = 3.86 m). Performance was slightly weaker for CFL (R² = 0.63; RMSE = 4.03 t/ha) and CBD (R² = 0.55; RMSE = 0.03 kg/m³). Wall-to-wall interpolation achieved less accurate yet acceptable results (0.24 < R² > 0.42), except for CC(R² = 0.65). All models could benefit from residual Kriging over only applying linear regression. We show that canopy fuel layers can be sufficiently derived from openly accessible data at country level without extensive field measurements. The proposed workflow may support regions where wildfire is an emerging issue and fuels information is scarce.