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Seasonal terrain texture synthesis via Köppen periodic conditioning

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Abstract

This paper presents the first method for synthesizing seasonal transition of terrain textures for an input heightfield. Our method reproduces a seamless transition of terrain textures according to the seasons by learning measured data on the earth using a convolutional neural network. We attribute the main seasonal texture transition to vegetation and snow, and control the texture synthesis not only with the input heightfield but also with the annual temperature and precipitation based on Köppen’s climate classification as well as insolation at the location. We found that month-by-month synthesis yields incoherent transitions, while a naïve conditioning with explicit temporal information (e.g., month) degrades generalizability due to the north–south hemisphere difference. To address these issues, we introduce a simple solution—periodic conditioning on the annual data without explicit temporal information. Our experiments reveal that our method can synthesize plausible seasonal transitions of terrain textures. We also demonstrate large-scale texture synthesis by tiling the texture output.

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Data availability

Data are provided within the manuscript. The source codes and pre-trained models will be released upon acceptance.

Notes

  1. We used pvlib for calculating the solar altitude from the latitude.

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Authors and Affiliations

  1. University of Tsukuba, Tsukuba, Japan

    Toshiki Kanai, Yuki Endo & Yoshihiro Kanamori

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  1. Toshiki Kanai
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All authors wrote the main manuscript text and reviewed the manuscript. T.K. prepared Figures 1, 4, 5, 6, 7, and 8. Y.K. prepared Figures 2, 3.

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Correspondence to Toshiki Kanai.

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Appendix A: A large-scale texture synthesis by tiling outputs

Appendix A: A large-scale texture synthesis by tiling outputs

To obtain a large terrain texture corresponding to a heightfield larger than the input/output resolution (i.e., \(256 \times 256\)) of our texture generation CNN, we seamlessly connect adjacent output textures using alpha blending. We divide an input heightfield into small tiles of \(256 \times 256\) pixels with a stride of 128 pixels and feed them to the texture generation CNN to obtain small texture tiles corresponding to each heightfield tile. To blend the neighboring texture tiles, we blend the quarters (i.e., \(128 \times 128\) pixel regions) of each texture tile, which are covered by four neighboring tiles except for the heightfield boundaries. By applying an alpha map whose alpha values are defined by normalized distances from the center pixel, we can obtain a large terrain texture without noticeable seams. Figure 1 shows the rendered result with an alpha-blended texture corresponding to a heightfield of approximately \(5500 \times 5500\) pixels. For the temperature and precipitation in generating large-scale terrain textures, the user can choose whether to specify them on a per-tile basis or to use a constant value for the entire area, depending on the quality desired by the user. In the result of Fig. 1, the temperature is varied by \(-0.6\,{}^\circ \)C per 100 m elevation difference (standard temperature reduction rate) between the entire elevation average and the per-tile elevation average. While we used the same value for precipitation for simplicity, high-quality textures were obtained.

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Kanai, T., Endo, Y. & Kanamori, Y. Seasonal terrain texture synthesis via Köppen periodic conditioning. Vis Comput 40, 4857–4868 (2024). https://doi.org/10.1007/s00371-024-03485-1

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