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The Indian territory coastlines, which are some of the most populated areas in the world, are prone to tsunamis generated from subduction zones such as Makran, the Northern part of the Sunda trench and submarine landslides. Within the project “People-centered tsunami early warning for the Indian Coastlines (PCTWIN)”, we are striving to forecast not only the hazard but also the impact, focusing on impact on the population through modelling of human exposure in different spatio-temporal scales.Human exposure maps transform coastal risk into actionable information, enabling smarter planning, appropriate decision-making, and stronger resilience against tsunami hazards. In PCTWIN, human exposure maps are mainly required for developing (1) site-specific Probabilistic Tsunami Risk Analysis (PTRA) maps to estimate the distribution of population at risk for different return periods; (2) Impact Forecasting to define the number of people being affected by the tsunami.To this end, the Human exposure maps for the whole Indian Coastlines are being developed. The national coastal human exposure maps will map the population at domicile, with a resolution of 100 meters. These maps are developed through a top-down census-based approach using the Python-based software Popcorn https://popcorn-population.github.io/. It is a population mapping workflow that employs the globally available satellite images from Sentinel-1 and Sentinel-2, and the number of aggregate population counts over coarse census districts for calibration. The building occupancy is trained through Deep Learning algorithms with coarse census counts. Herein, we have employed the 2011 Indian census data, while the population is projected based on growth rates estimated by the UN World Urbanization Prospects Database (UNPD). Preliminary comparison with the surveyed population data for selected coastal areas by INCOIS (Indian National Centre for Ocean Information Services) are promising. We are also going to compare human exposure maps developed at national scale with other open-source exposure databases.