Large-Scale Vegetation Height Mapping from Sentinel Data Using Deep Learning
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The deep learning revolution in computer vision has enabled a potential for creating new value chains for Earth observation that significantly enhances the analysis of satellite data for tasks like land cover mapping, change analysis, and object detection. We demonstrate a deep learning based value chain for the task of mapping vegetation height in the Liwale region in Tanzania using Sentinel-1 and −2 data. As ground truth data we use lidar measurements, which are processed to provide the average vegetation height per Sentinel-2 pixel grid (10 m). We apply the UNet, which is a widely used neural network for segmentation tasks in computer vision, to estimate average vegetation height from the Sentinel data. Preliminary results show that we are able to map the forest extent with high accuracy, with an RMSE of 3.5 m for Sentinel-2 data and 4.6 m for the Sentinel-1 data.