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Estimating global tree canopy height is crucial for forest conservation and climate change applications.
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Radford, A., Kim, J.W., Hallacy, C., Ramesh, A., Goh, G., Agarwal, S., Sastry, G., Askell, A., Mishkin, P., Clark, J., et al.: Learning transferable visual models from natural language supervision. In: International conference on machine learning. pp. 8748–8763. PMLR (2021)
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Cha, K., Seo, J., Lee, T.: A billion-scale foundation model for remote sensing images. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing pp. 1–17 (2024). https://doi.org/10.1109/JSTARS.2024.3401772
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Lang, N., Jetz, W., Schindler, K., Wegner, J.D.: A high-resolution canopy height model of the earth. Nature Ecology & Evolution 7
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Lin, J., Gao, F., Shi, X., Dong, J., Du, Q.: Ss-mae: Spatial–spectral masked autoencoder for multisource remote sensing image classification. IEEE Transactions on Geoscience and Remote Sensing 61
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Ke, B., Obukhov, A., Huang, S., Metzger, N., Daudt, R.C., Schindler, K.: Repurposing diffusion-based image generators for monocular depth estimation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 9492–9502 (2024)
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