MLAE: A Pretraining Method for Automatic Identification of Urban Public Space

This paper proposes a deep learning-based remote sensing image segmentation method for estimating the proportion of urban public space, which is an important urban planning problem. Remote sensing images contain diverse landforms and different scales of objects, making image segmentation a challenging task. Most current image segmentation methods use convolutional neural networks, which are deep neural networks that can automatically learn image features and perform classification or regression. However, existing convolutional neural networks are usually pre-trained on natural image datasets such as ImageNet, which are very different from remote sensing images, resulting in pre-trained models that cannot fully exploit the characteristics of remote sensing images. To address this issue, this paper proposes a Mixlabel Autoencoder (MLAE) to further pre-train remote sensing images by image reconstruction. Unlike natural images, remote sensing images are complex and difficult to reconstruct; therefore, we use partial labels to guide the reconstruction process. Our method involves replacing random patches of the input image with corresponding labels and reconstructing the patches using an encoder-decoder architecture. Experimental results show that our method achieves higher segmentation accuracy and better visual effects in downstream tasks. Our method provides valuable guidance for urban planning and construction by identifying the proportion of pixels within each type of area in an image.