Population Estimation using Deep Learning over Gandhinagar Urban Area

• URL: http://arxiv.org/abs/2509.12926v1
• Date: Tue, 16 Sep 2025 10:25:46 GMT
• Title: Population Estimation using Deep Learning over Gandhinagar Urban Area
• Authors: Jai Singla, Peal Jotania, Keivalya Pandya,
• Abstract summary: Population estimation is crucial for various applications, from resource allocation to urban planning.<n>In this study a deep learning solution is proposed to estimate population using high resolution (0.3 m) satellite imagery.<n>The framework provides municipalities with a scalable and replicable tool for optimized resource management in rapidly urbanizing cities.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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