Population Estimation using Deep Learning over Gandhinagar Urban Area
2025-09-17
Summary
A recent study proposes using deep learning techniques to estimate the population of Gandhinagar, India, utilizing high-resolution satellite imagery and digital elevation models. The approach combines Convolutional Neural Networks (CNN) for classifying buildings as residential or non-residential, and Artificial Neural Networks (ANN) to estimate population numbers. The model achieved a high F1-score of 0.9936, suggesting a promising alternative to traditional census methods, and estimated the city's population at 278,954.
Why This Matters
This research demonstrates the potential of AI-driven geospatial analytics to offer quicker, scalable, and more accurate population estimates compared to traditional census methods, which are often costly and slow. For rapidly urbanizing areas like Gandhinagar, such automated systems could significantly enhance urban planning and resource management, providing timely data for emergency response, infrastructure development, and public service delivery.
How You Can Use This Info
Urban planners and policymakers can leverage this deep learning approach to gain more accurate insights into population distribution, aiding in efficient resource allocation and infrastructure development. Non-technical professionals involved in urban development and governance can adopt these AI techniques to improve decision-making processes through more timely and precise data, potentially leading to better service delivery and urban management.