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Detection and vectorization of windows from building fa&#x00E7;ades are important for building energy modeling, civil engineering, and architecture design. However, current applications still face the challenges of low accuracy and lack of automation. In this article we propose a new two-steps workflow for window segmentation and vectorization from fa&#x00E7;ade images. First, we propose a cross field learning-based neural network architecture, which is augmented by a grid-based self-attention module for window segmentation from rectified fa&#x00E7;ade images, resulting in pixel-wise window blobs. Second, we propose a regression neural network augmented by squeeze-and-excitation (SE) attention blocks for window vectorization. The network takes the segmentation results together with the original fa&#x00E7;ade image as input, and directly outputs the position of window corners, resulting in vectorized window objects with improved accuracy. In order to validate the effectiveness of our method, experiments are carried out on four public fa&#x00E7;ades image datasets, with results usually yielding a higher accuracy for the final window prediction in comparison to baseline methods on four datasets in terms of intersection over union score, F1 score, and pixel accuracy.

Cross Field-Based Segmentation and Learning-Based Vectorization for Rectangular Windows