Void Filling of Digital Elevation Models with Deep Generative Models
This work addresses void-filling in DEMs for geospatial applications, but it is incremental as it applies an existing method to a new domain.
The paper tackled the problem of filling voids in digital elevation models (DEMs) by transferring a state-of-the-art image inpainting model, a Wasserstein Generative Adversarial Network with contextual attention, to generate semantically plausible data, achieving successful results on GeoTIFF data from Norway.
In recent years, advances in machine learning algorithms, cheap computational resources, and the availability of big data have spurred the deep learning revolution in various application domains. In particular, supervised learning techniques in image analysis have led to superhuman performance in various tasks, such as classification, localization, and segmentation, while unsupervised learning techniques based on increasingly advanced generative models have been applied to generate high-resolution synthetic images indistinguishable from real images. In this paper we consider a state-of-the-art machine learning model for image inpainting, namely a Wasserstein Generative Adversarial Network based on a fully convolutional architecture with a contextual attention mechanism. We show that this model can successfully be transferred to the setting of digital elevation models (DEMs) for the purpose of generating semantically plausible data for filling voids. Training, testing and experimentation is done on GeoTIFF data from various regions in Norway, made openly available by the Norwegian Mapping Authority.