1 Introduction In recent years sustainable development has become a topic of great concern among various sectors of society. Sustainable development seeks to meet the needs and aspira- tions of the present without compromising the ability of future generations to meet their own needs (World Commission on Environment and Development, 1987). In order to maintain sustainable development, the whole ecosystem (air, water, land, energy, flora, and fauna) needs tobe taken care of. Land is essential, not only because it is the habitat of human beings and because our food and raw materials originate from it, but also because any disturbance to land by way of a change in land use (eg conversion of forestland or agricultural into built-up land) is irreversible. Under the umbrella of sustainable development, and stimulated by the joint inter- national Land Use/Cover Change project of the International Geosphere^ Biosphere Program and the International Human Dimensions Program on Global Environmental Change (Turner et al, 1995), detecting, monitoring, understanding, modeling, and projections of land-use change from the global to the regional scale have attracted many research interests. In the past two decades substantial work has been done with regard to land-use-change modeling. Various models have been developed to help ecologists, urban planners, sociologists, administrators, and policy makers under- stand better the complexity of land-use-change patterns and to evaluate the impact of land-use change on the environment. While these models have demonstrated different Land-use-change modeling using unbalanced support-vector machines Bo Huang Department of Geography and Resource Management, The Chinese University of Hong Kong, Shatin, NT, Hong Kong; e-mail: bohuang@cuhk.edu.hk Chenglin Xie NorthWest Geomatics Ltd, 5438-11 Street NE, Calgary, AB T2E 7E9, Canada; e-mail: chenglin.xie@nwgeo.com Richard Tay Department of Civil Engineering, University of Calgary, Calgary, AB T2N 1N4, Canada; e-mail: rtay@ucalgary.ca Bo Wu Spatial Information Research Center, Fuzhou University, 523 Gongye Road, Fuzhou, PR China; e-mail: wavelet778@sohu.com Received 8 May 2006; in revised form 6 August 2007; published online 15 December 2008 Environment and Planning B: Planning and Design 2009, volume 36, pages 398 ^ 416 Abstract. Modeling land-use change is a prerequisite to understanding the complexity of land-use- change patterns. This paper presents a novel method to model urban land-use change using support-vector machines (SVMs), a new generation of machine learning algorithms used in classifica- tion and regression domains. An SVM modeling framework has been developed to analyze land-use change in relation to various factors such as population, distance to roads and facilities, and surrounding land use. As land-use data are generally unbalanced, in the sense that the unchanged data overwhelm the changed data, traditional methods are incapable of classifying relatively minor land-use changes with high accuracy. To circumvent this problem, an unbalanced SVM has been adopted by enhancing the standard SVMs. A case study of Calgary land-use change demonstrates that the unbalanced SVMs can achieve high and reliable performance for land-use-change modeling. doi:10.1068/b33047