Automated, quick, and precise building extraction from aerial images using ll-unet model

Abstract

The subject matter of this article is the detection and semantic segmentation of buildings from high-resolution aerial images. It extracts building images from similar characteristics of roads and soil objects. It is used for various applications such as urban planning, infrastructure development, and disaster management. The goal of this study is to develop a fast, accurate, and automatic building detection model based on the semantic segmentation LL-UNET architecture that is optimized and tuned with proper hyper parameter settings. The tasks to be solved are as follows: collect remote sensing building dataset that is divided into three parts of training, validation, and testing; apply data augmentation on the training dataset by vertical flip, horizontal flip, and rotation methods; further pass into the bilateral filter to remove noises from the images; optimize LL-UNET model by various optimizer methods and tuned hyper parameter by proper selection value, the method is compared by the performance metrics recall, precision, and accuracy. The following results were obtained: the model is evaluated under the training loss curve and accuracy curve of different optimizers SGD, ASGD, ADAM, ADAMW, and RMSProp; it measures the training time, mean accuracy, and mean IOU parameters during the training phase; the testing phase is evaluated by precision and recall; the method is compared by visualizing the result of LL-UNET + different optimizers; and the proposed method is compared with the existing method by common evolution parameters metric. Conclusions. The scientific novelty of the results obtained is as follows: 1) the LL-UNET effectively segmented the building remotely sensed images in the limited number of training samples available; 2) the loss function of the model observed under hyper parameter selection of the optimizer, learning rate, batch size, and epochs; which makes an optimal model to extract the building in an accurate and fast manner from the complex background; 3) the proposed model results compared with a well-known model of the building extraction under the common evaluation metrics of F1 score.

Keywords

• deep learning
• semantic segmentation
• ll-unet
• buildings