Deep Learning in Urban Diagnostics: A Feature-Based Land Cover Assessment

Abstract

Urbanization is a global phenomenon; with more than half of the world's population residing in urban areas. Sustainable development and efficient management of cities require a comprehensive understanding of urban environments, including accurate assessments of urban land cover (ULC) and land use. In this context, modern urban multi-source high-resolution and heterogeneous data capturing technologies and machine learning techniques have emerged as powerful tools for urban diagnostics. This paper presents a novel approach to urban land cover assessment (ULCA) using deep learning methods. We leverage high-resolution urban data collection sources and state-of-the-art convolutional neural networks (CNNs) to extract rich features from urban landscapes. Unlike traditional methods that rely on handcrafted features, our approach automatically learns discriminative representations, allowing for more accurate and adaptable land cover classification. Some results show that deep learning algorithms significantly improve the accuracy and timeliness of ULCA metrics compared to traditional approaches.