Retro-Remote Sensing With Doc2Vec Encoding
Mesay Belete Bejiga, Genc Hoxha, Farid Melgani · 2020
In this work, we attempt to address the issue of developing a sophisticated text encoder for retro-remote sensing application. The encoder converts ancient landscape descriptions into a fixed-size vector that, adequately, represents the available information. This vector is then used as a conditioning data to a Generative adversarial network (GAN) that synthesizes the equivalent image. We propose using a pre-trained Doc2Vec encoder for text encoding and train a Wasserstein GAN (a variant of GAN) to convert landscape descriptions written by travelers and geographers into the equivalent image. Qualitative and quantitative analysis of the generated images signify usefulness of the proposed method.