|
Land cover classification products such as the Corine Land Cover (CLC) project provide Europe-wide maps at a resolution that is relatively coarse in comparison to many remote sensing instruments. Features such as roads, individual buildings, small rivers, and many others are missed due to this. In this work, we present a method to increase the resolution of land cover maps using a combination of the existing CLC product, and high resolution optical time series. The RapidAI4EO corpus is an open-data collection of half a million time series over Europe, and includes a 3 m/pixel resolution Planet Fusion product, spanning two years (2018-2019). A standard supervised learning approach with this data, using the CLC labels as direct ground-truth over each pixel, would lead to many incorrect labels, because of the inexact delineation of classes at the comparatively lower resolution of the CLC map. With this in mind, we have developed a land cover classification model which is trained using a novel loss function—ambiguous cross-entropy—that takes into account the fuzzy nature of the CLC labels. The ambiguous cross-entropy loss allows the model to learn from imprecise labels. Models are trained for each of the three CLC class levels, and compared. Statistical metrics for agreement between CLC, the trained models, and a high-resolution land cover map derived from OpenStreetMap are measured in a set of validation sites across Europe. This work demonstrates how machine learning can enhance an existing product’s resolution, without the need for time-consuming labeling at such a fine scale.
|
