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Mapping New Realities: Ground Truth Image Creation with Pix2Pix Image-to-Image Translation
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Generative Adversarial Networks (GANs) have significantly advanced image processing, with Pix2Pix being a notable framework for image-to-image translation. This paper explores a novel application of Pix2Pix to transform abstract map images into realistic ground truth images, addressing the scarcity of such images crucial for domains like urban planning and autonomous vehicle training. We detail the Pix2Pix model's utilization for generating high-fidelity datasets, supported by a dataset of paired map and aerial images, and enhanced by a tailored training regimen. The results demonstrate the model's capability to accurately render complex urban features, establishing its efficacy and potential for broad real-world applications.
Forward citations
Cited by 2 Pith papers
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Easz: An Agile Transformer-based Image Compression Framework for Resource-constrained IoTs
Easz compresses images by erasing and squeezing patches on the edge, then reconstructs them on a server with a lightweight transformer at flexible ratios and low edge cost.
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Pix2Geomodel: A Next-Generation Reservoir Geomodeling with Property-to-Property Translation
On the Groningen Rotliegend reservoir, a standard Pix2Pix conditional GAN translates between facies and petrophysical property images, with reported accuracy that is partly an artifact of class imbalance.
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