Classification of residential and non-residential buildings based on satellite data using deep learning

TL;DR

This paper presents a deep learning method combining satellite and vector data for accurate residential and non-residential building classification, achieving an F1-score of 0.9936, aiding urban planning and resource management.

Contribution

It introduces a novel deep learning architecture utilizing high-resolution satellite data and feature engineering for efficient and precise building classification.

Findings

Achieved an F1-score of 0.9936 on large-scale dataset.

Demonstrated improved computational efficiency through feature engineering.

Validated effectiveness for urban planning applications.

Abstract

Accurate classification of buildings into residential and non-residential categories is crucial for urban planning, infrastructure development, population estimation and resource allocation. It is a complex job to carry out automatic classification of residential and nonresidential buildings manually using satellite data. In this paper, we are proposing a novel deep learning approach that combines high-resolution satellite data (50 cm resolution Image + 1m grid interval DEM) and vector data to achieve high-performance building classification. Our architecture leverages LeakyReLU and ReLU activations to capture nonlinearities in the data and employs feature-engineering techniques to eliminate highly correlated features, resulting in improved computational efficiency. Experimental results on a large-scale dataset demonstrate the effectiveness of our model, achieving an impressive overall F1 -score of 0.9936. The proposed approach offers a scalable and accurate solution for building classification, enabling informed decision-making in urban planning and resource allocation. This research contributes to the field of urban analysis by providing a valuable tool for understanding the built environment and optimizing resource utilization.