This research discusses the development of a new model for task change detection. Siamese Neural networks with U-Net as basic architecture are combined with spatial attention modules to perform task change detection. This model is developed to get a lightweight model with good performance. In the implementation, there is no need to use enormous resources. To benchmark the model, we used the LEVIR-CD dataset, where this dataset has two paired images taken at different times. The information contained in the two paired images is that there are changes such as the presence of buildings such as houses that increase or decrease in a certain area during the time of taking the two images. We compared the proposed model with U-Net and Siamese U-Net without spatial attention modules to see how they differ in performance. Then, We also compared the F1 Score with the baseline model of the LEVIR-CD dataset. After hyperparameter tuning with epochs of 100 is performed, the result is that the F1 Scores tested can balance the baseline model with a faster training time.