In order to improve the accuracy and running speed of segmentation of building remote sensing images, a threshold segmentation method based on the 2-D Tsallis cross entropy image threshold selection using chaotic cuckoo search optimization is proposed. Firstly, the formula of 2-D Tsallis cross entropy threshold selection based on the histogram is derived. Next, in order to improve the convergence rate, logistic chaotic map is applied to the cuckoo search algorithm. Finally, the proposed chaotic cuckoo search algorithm is utilized for precise optimization of thresholds based on the 2-D Tsallis cross entropy, so as to realize the threshold segmentation of building remote sensing images with optimal threshold. A large number of experiments show that, compared with 2-D reciprocal cross entropy thresholding method, 2-D Tsallis entropy thresholding method, 2-D Tsallis gray entropy thresholding method based on chaotic particle swarm optimization and so on, the objects in the images segmented by the proposed method are more accurate, the details are more explicit, in addition, its running time is shorter.