Abstract
Over the last few decades, many countries, especially islands in the Caribbean, have been challenged by the devastating consequences of natural disasters, which pose a significant threat to human health and safety. Timely information related to the distribution of vulnerable population and critical infrastructure is key for effective disaster relief. OpenStreetMap (OSM) has repeatedly been shown to be highly suitable for disaster mapping and management. However, large portions of the world, including countries exposed to natural disasters, remain incompletely mapped. In this study, we propose a methodology that relies on remotely sensed measurements (e.g., Visible Infrared Imaging Radiometer Suite (VIIRS), Sentinel-2 and Sentinel-1) and derived classification schemes (e.g., forest and built-up land cover) to predict the completeness of OSM building footprints in three small island states (Haiti, Dominica and St. Lucia). We find that the combinatorial effects of these predictors explain up to 94% of the variation of the completeness of OSM building footprints. Our study extends the existing literature by demonstrating how remotely sensed measurements could be leveraged to evaluate the completeness of the OSM database, especially in countries with high risk of natural disasters. Identifying areas that lack coverage of OSM features could help prioritize mapping efforts, especially in areas vulnerable to natural hazards and where current data gaps pose an obstacle to timely and evidence-based disaster risk management.
Highlights
Over the last few decades, many countries have been challenged by the devastating consequences of natural disasters which pose a significant threat to human health and safety and impact vulnerable communities and critical infrastructure globally
Caribbean island countries are especially exposed to a wide range of natural disasters [6] and small island developing states—which are frequently characterized by coastal communities, geographic isolation, and limited technical capacity—are among the most vulnerable countries to natural disasters and climate change [7]
We evaluated the correlation between the remotely sensed and the geospatial measures and the area of OSM building footprint in a grid cell using a Pearson Correlation Test, and performed an Ordinary Least Squares (OLS) regression to estimate the potential of the variables, combined, to explain the observed variation in the area of OSM building footprints in a grid cell
Summary
Over the last few decades, many countries have been challenged by the devastating consequences of natural disasters which pose a significant threat to human health and safety and impact vulnerable communities and critical infrastructure globally. Natural disasters impact close to 160 million people worldwide [1], causing destruction of the physical, biological and social environments, impacting food security, and causing global losses that amount to over 100 billion dollars [2]. The frequency of natural disasters has been steadily increasing since 1940 [3] and over the century, climate change will likely amplify the number and severity of such disasters [4]. While the impacts of natural disasters are worldwide, some countries have been more vulnerable to different types of disasters than others [5]. Caribbean island countries are especially exposed to a wide range of natural disasters [6] and small island developing states—which are frequently characterized by coastal communities, geographic isolation, and limited technical capacity—are among the most vulnerable countries to natural disasters and climate change [7]
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