​Assessing the exposure of buildings to long-term sea level rise across the Global South

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Figure 2 in paper. Cumulative count and regional percentage of Global South buildings inundated at high tide with increasing levels of LSLR.
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Figure 4 in paper. Building inundation by country. a-d The percentage of each country's current building stock centroids inundated at high tide per meter of LSLR for (a) 0.5 m, (b) 2 m, (c) 5 m, and (d) 15 m scenarios.
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Figure 1 in paper. Exposure to building inundation as a function of LSR. In (a), the colour of each 50 km grid cell represents the amount of SLR that causes > 25% of the cell's total building count to be inundated at high tide.
Background

Sea level rise is a long-term and unavoidable consequence of climate change that will continue for centuries as oceans warm and ice sheets respond slowly. Coastal communities in the Global South face especially high risks due to rapid population growth, limited adaptive capacity, and extensive low-lying development. Yet most existing assessments focus on short-term projections or summarize impacts at broad regional scales, obscuring which individual buildings and neighborhoods are physically vulnerable. These limitations make it difficult to compare risks across regions or to plan for infrastructure that may persist far beyond the year 2100. We recognized the need for a spatially explicit, building-level assessment that isolates exposure to sea level rise itself, independent of uncertain climate timelines.

Approach

We developed a large-scale framework to assess long-term exposure of buildings to local sea level rise across Africa, Southeast Asia, and South and Central America. We combined satellite-derived maps of building footprints, covering roughly 840 million structures, with high-resolution elevation data that represent true ground height. To reflect real coastal conditions, we incorporated local high-tide levels using global tidal information. A building was classified as exposed when its central point fell below the high-tide level plus a specified amount of sea level rise. By repeating this analysis across a wide range of rise magnitudes, from 0.5 to 20 meters, we evaluated exposure without relying on specific emissions scenarios or timelines.

Key Findings
Our analysis revealed clear and consistent patterns of long-term coastal vulnerability:
  • Early and Widespread Exposure: Even a 0.5-meter increase in sea level places approximately 3 million buildings at risk of inundation across the study regions, demonstrating that exposure begins at relatively low levels of rise. This number increases to approximately 45 million with 5 m of Local Sea Level Rise (LSLR), and ~136 million with 20 m LSLR. 
  • Strong Nonlinear Growth: Exposure increases rapidly beyond about 2 meters of sea level rise, reaching roughly 45 million buildings at 5 meters and approximately 136 million buildings at 20 meters.
  • Geographic Concentration: The highest risks are concentrated in low-lying coastal plains, deltas, and estuaries, including parts of Northeast Africa, Southeast Asia, the Caribbean, and major river systems such as the Nile, Amazon, and Río de la Plata.
The results highlight geographic variability in exposure and the implication of low-emissions pathways for preserving build environments.

Impact
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This work provides a high-resolution baseline for understanding long-term coastal exposure in regions where detailed data have been limited. By focusing on physical inundation rather than uncertain timelines, we support clearer comparisons across regions and future pathways. The results can inform long-term adaptation strategies, infrastructure investment, and land-use planning, particularly in countries where today’s development decisions will shape vulnerability for centuries. Our approach offers a transparent, scalable foundation for equitable and forward-looking coastal resilience planning.

Resources

Published Paper: Willard-Stepan, M., Gomez, N., Cardille, J.A. et al. Assessing the exposure of buildings to long-term sea level rise across the Global South. npj Urban Sustain, 5, 72 (2025). DOI : https://doi.org/10.1038/s42949-025-00259-z 

Source Code Repository: We made all raw data and scripts used for this research, which were conducted through Python 3.10.9, publicly available here : Zenodo Repository (Scripts and Data).

Data Repository: In addition to the Zenodo archive, we provide an interactive global map on Google Earth Engine that allows users to explore seawater inundation masks and building locations across various sea level scenarios : Sea-Level Submergence Explorer (Interactive Map).

Figures available via license : Creative Commons Attribution-NonCommercial-NoDerivatives-4.0 International