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In this letter, we propose a “conditional texton forest” (CTF) method to utilize widely available historical land cover (HLC) maps in land use/cover classification on high-resolution images. The CTF is based on texton forest (TF), which is a popular and powerful method in image semantic segmentation due to its effective use of spatial contextual information, its high accuracy, and its fast speed in multiclass classification. The proposed CTF method nonparametrically aggregates a bank of TFs according to HLC information and uses the fact that different types of HLC follow different transition rules. The performance of CTF is compared to support vector machine (SVM), Markov random field (MRF), and a naive TF method which uses historical data directly as a feature channel. On average, CTF results in a 2%-5% higher classification accuracy than other classifiers in our experiment. The classifying speed of CTF is similar with TF, five times faster than MRF, and hundreds of times faster than SVM. Given the abundance of HLC data, the proposed method can be expected to be useful in a wide range of socioeconomic and environmental studies.