Quality Assessment of Global Ocean Island Datasets

Abstract

Ocean Island data are essential to the conservation and management of islands and coastal ecosystems, and have also been adopted by the United Nations as a sustainable development goal (SDG 14). Currently, two categories of island datasets, i.e., global shoreline vector (GSV) and OpenStreetMap (OSM), are freely available on a global scale. However, few studies have focused on accessing and comparing the data quality of these two datasets, which is the main purpose of our study. Specifically, these two datasets were accessed using four 100 × 100 (km2) study areas, in terms of three aspects of measures, i.e., accuracy (including overall accuracy (OA), precision, recall and F1), completeness (including area completeness and count completeness) and shape complexity. The results showed that: (1) Both the two datasets perform well in terms of the OA (98% or above) and F1 (0.9 or above); the OSM dataset performs better in terms of precision, but the GSV dataset performs better in terms of recall. (2) The area completeness is almost 100%, but the count completeness is much higher than 100%, indicating the total areas of the two datasets are almost the same, but there are many more islands in the OSM dataset. (3) In most cases, the fractal dimension of the OSM dataset is relatively larger than the GSV dataset in terms of the shape complexity, indicating that the OSM dataset has more detail in terms of the island boundary or coastline. We concluded that both of the datasets (GSV and OSM) are effective for island mapping, but the OSM dataset can identify more small islands and has more detail.

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