Building Detection in High-Resolution Remote Sensing Images by Enhancing Superpixel Segmentation and Classification Using Deep Learning Approaches

Accurate building detection is a critical task in urban development and digital city mapping. However, current building detection models for high-resolution remote sensing images are still facing challenges due to complex object characteristics and similarities in appearance. To address this issue, this paper proposes a novel algorithm for building detection based on in-depth feature extraction and classification of adaptive superpixel shredding. The proposed approach consists of four main steps: image segmentation into homogeneous superpixels using a modified Simple Linear Iterative Clustering (SLIC), in-depth feature extraction using an variational auto-encoder (VAE) scale on the superpixels for training and testing data collection, identification of four classes (buildings, roads, trees, and shadows) using extracted feature data as input to an Convolutional Neural Network (CNN), and extraction of building shapes through regional growth and morphological operations. The proposed approach offers more stability in identifying buildings with unclear boundaries, eliminating the requirement for extensive prior segmentation. It has been tested on two datasets of high-resolution aerial images from the New Zealand region, demonstrating superior accuracy compared to previous works with an average F1 score of 98.83%. The proposed approach shows potential for fast and accurate urban monitoring and city planning, particularly in urban areas.

• building detection
• high-resolution aerial imagery
• superpixel segmentation
• SLIC
• variational autoencoder
• feature extraction
• superpixel classification
• convolutional neural networks
• regional economic growth
• morphological operations
• urban monitoring
• urban planning

• 11 — مدن ومجتمعات محلية مستدامة

• Automated Road and Building Extraction
• Remote-Sensing Image Classification
• Advanced Neural Network Applications
• building detection
• high-resolution aerial imagery
• superpixel segmentation
• SLIC
• variational autoencoder
• feature extraction
• superpixel classification
• convolutional neural networks
• Ocean Engineering
• Media Technology
• Computer Vision and Pattern Recognition
• Engineering
• Computer Science