• Heung, B., Hodúl, M., and Schmidt, M.G., 2017. Comparing the use of legacy soil pits and soil survey polygons as training data for mapping soil classes. Geoderma 290: 51-68.
• Freeland, T., Heung, B., Burley, D.V., Clark, G., and Knudby, A., 2016. Using airborne LiDAR for prospection and analysis of monumental architecture and settlement patterns in the Kingdom of Tonga. Journal of Archaeological Science 69: 64-74.
• Bulmer, C. E., Schmidt, M.G., Heung, B., Scarpone, C., Zhang, J., Filatow, D., Finvers, M., Berch, S., and Smith, C.A.S., 2016. Improved soil mapping in British Columbia, Canada with legacy soil data and Random Forest. In Digital Soil Mapping Across Paradigms, Scales and Boundaries. Springer Environmental Science and Engineering, pp. 291-303.
• Heung, B., Zhang, J., Ho, H.C., Knudby, A., Bulmer, C.E., and Schmidt, M.G., 2016. An overview and comparison of machine-learning techniques for classification purposes in digital soil mapping. Geoderma 265: 62-77.
• Heung, B., Bulmer, C.E., and Schmidt, M.G., 2014. Predictive soil parent material mapping at a regional-scale: A Random Forest approach. Geoderma 214-215: 141-154.
• Heung, B., Bakker, L., Schmidt, M.G., and Dragićević, S., 2013. Modelling the dynamics of soil redistribution induced by sheet erosion using the Universal Soil Loss Equation and cellular automata. Geoderma 202-203: 112-125.