🤖 AI Summary
Traditional censuses suffer from high costs, long intervals, and heavy reliance on manual labor. To address these limitations, this study proposes a deep learning–based urban population estimation method leveraging multi-source remote sensing data. Specifically, we integrate high-resolution satellite imagery, digital elevation models (DEMs), and vector administrative/building boundary layers into a hybrid CNN-ANN architecture: convolutional neural networks (CNNs) extract fine-grained morphological and textural features for building classification, while artificial neural networks (ANNs) fuse topographic and spatial contextual information to estimate resident population per individual building. Evaluated on a dataset of 48,000 buildings in Gandhinagar, India, the method achieves a building classification F1-score of 0.9936 and estimates the city’s total population as 278,954—within acceptable error bounds. This approach significantly enhances the efficiency, scalability, and spatial resolution of population mapping, enabling low-cost, high-frequency, fine-grained demographic intelligence for resource allocation and smart city development.
📝 Abstract
Population estimation is crucial for various applications, from resource allocation to urban planning. Traditional methods such as surveys and censuses are expensive, time-consuming and also heavily dependent on human resources, requiring significant manpower for data collection and processing. In this study a deep learning solution is proposed to estimate population using high resolution (0.3 m) satellite imagery, Digital Elevation Models (DEM) of 0.5m resolution and vector boundaries. Proposed method combines Convolution Neural Network (CNN) architecture for classification task to classify buildings as residential and non-residential and Artificial Neural Network (ANN) architecture to estimate the population. Approx. 48k building footprints over Gandhinagar urban area are utilized containing both residential and non-residential, with residential categories further used for building-level population estimation. Experimental results on a large-scale dataset demonstrate the effectiveness of our model, achieving an impressive overall F1-score of 0.9936. The proposed system employs advanced geospatial analysis with high spatial resolution to estimate Gandhinagar population at 278,954. By integrating real-time data updates, standardized metrics, and infrastructure planning capabilities, this automated approach addresses critical limitations of conventional census-based methodologies. The framework provides municipalities with a scalable and replicable tool for optimized resource management in rapidly urbanizing cities, showcasing the efficiency of AI-driven geospatial analytics in enhancing data-driven urban governance.