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We propose a framework for global-scale canopy height estimation based on satellite data.
Imagenet: A large-scale hierarchical image database
Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., and Fei-Fei, L · 2009
Earlier work this paper cites.
U-Net: Convolutional networks for biomedical image segmentation
Ronneberger, O., Fischer, P., and Brox, T · 2015
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Forest resources assessment of 2015 shows positive global trends but forest loss and degradation persist in poor tropical countries
Sloan, S. and Sayer, J. A · 2015
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Deep residual learning for image recognition
He, K., Zhang, X., Ren, S., and Sun, J · 2016
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Linknet: Exploiting encoder representations for efficient semantic segmentation
Chaurasia, A. and Culurciello, E · 2017
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Rethinking atrous convolution for semantic image segmentation
Chen, L.-C., Papandreou, G., Schroff, F., and Adam, H · 2017
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Feature pyramid networks for object detection
Lin, T.-Y., Dollár, P., Girshick, R., He, K., Hariharan, B., and Belongie, S · 2017
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Fixing weight decay regularization in Adam
Loshchilov, I. and Hutter, F · 2017
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Cyclical learning rates for training neural networks
Smith, L. N · 2017
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Pyramid scene parsing network
Zhao, H., Shi, J., Qi, X., Wang, X., and Jia, J · 2017
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Encoder-decoder with atrous separable convolution for semantic image segmentation
Chen, L.-C., Zhu, Y., Papandreou, G., Schroff, F., and Adam, H · 2018
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Averaging weights leads to wider optima and better generalization
Izmailov, P., Podoprikhin, D., Garipov, T., Vetrov, D., and Wilson, A. G · 2018
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Pyramid attention network for semantic segmentation
Li, H., Xiong, P., An, J., and Wang, L · 2018
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Deep ensembles: A loss landscape perspective
Fort, S., Hu, H., and Lakshminarayanan, B · 2019
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UNet++: Redesigning skip connections to exploit multiscale features in image segmentation
Zhou, Z., Siddiquee, M. M. R., Tajbakhsh, N., and Liang, J · 2019
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The global ecosystem dynamics investigation: High-resolution laser ranging of the Earth’s forests and topography
Dubayah, R., Blair, J. B., Goetz, S., Fatoyinbo, L., Hansen, M., Healey, S., Hofton, M., Hurtt, G., Kellner, J., Luthcke, S., et al · 2020
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Ma-Net: A multi-scale attention network for liver and tumor segmentation
Fan, T., Wang, G., Li, Y., and Wang, H · 2020
Vision transformers, a new approach for high-resolution and large-scale mapping of canopy heights
Fayad, I., Ciais, P., Schwartz, M., Wigneron, J.-P., Baghdadi, N., de Truchis, A., d’Aspremont, A., Frappart, F., Saatchi, S., Pellissier-Tanon, A., and Bazzi, H · 2023
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Forest structure characterization in Germany: Novel products and analysis based on GEDI, Sentinel-1 and Sentinel-2 data
Kacic, P., Thonfeld, F., Gessner, U., and Kuenzer, C · 2023
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A high-resolution canopy height model of the Earth
Lang, N., Jetz, W., Schindler, K., and Wegner, J. D · 2023
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The overlooked contribution of trees outside forests to tree cover and woody biomass across Europe
Liu, S., Brandt, M., Nord-Larsen, T., Chave, J., Reiner, F., Lang, N., Tong, X., Ciais, P., Igel, C., Pascual, A., Guerra-hernandez, J., Li, S., Mugabowindekwe, M., Saatchi, S., Yue, Y., Chen, Z., and Fensholt, R · 2023
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Improving GEDI footprint geolocation using a high-resolution digital elevation model
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Mapping the global mangrove forest aboveground biomass using multisource remote sensing data
Hu, T., Zhang, Y., Su, Y., Zheng, Y., Lin, G., and Guo, Q · 2020
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Sparse model soups: A recipe for improved pruning via model averaging
Zimmer, M., Spiegel, C., and Pokutta, S · 2020
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Mapping global forest canopy height through integration of GEDI and Landsat data
Potapov, P., Li, X., Hernandez-Serna, A., Tyukavina, A., Hansen, M. C., Kommareddy, A., Pickens, A., Turubanova, S., Tang, H., Silva, C. E., Armston, J., Dubayah, R., Blair, J. B., and Hofton, M · 2021
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A climate risk analysis of Earth’s forests in the 21st century
Anderegg, W. R., Wu, C., Acil, N., Carvalhais, N., Pugh, T. A., Sadler, J. P., and Seidl, R · 2022
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Global carbon budget 2021
Friedlingstein, P., Jones, M. W., O’Sullivan, M., Andrew, R. M., Bakker, D. C. E., Hauck, J., Le Quéré, C., Peters, G. P., Peters, W., Pongratz, J., Sitch, S., Canadell, J. G., Ciais, P., Jackson, R. B., Alin, S. R., Anthoni, P., Bates, N. R., Becker, M., Bellouin, N., Bopp, L., Chau, T. T. T., Chevallier, F., Chini, L. P., Cronin, M., Currie, K. I., Decharme, B., Djeutchouang, L. M., Dou, X., Evans, W., Feely, R. A., Feng, L., Gasser, T., Gilfillan, D., Gkritzalis, T., Grassi, G., Gregor, L., Gruber, N., Gürses, O., Harris, I., Houghton, R. A., Hurtt, G. C., Iida, Y., Ilyina, T., Luijkx, I. T., Jain, A., Jones, S. D., Kato, E., Kennedy, D., Klein Goldewijk, K., Knauer, J., Korsbakken, J. I., Körtzinger, A., Landschützer, P., Lauvset, S. K., Lefèvre, N., Lienert, S., Liu, J., Marland, G., McGuire, P. C., Melton, J. R., Munro, D. R., Nabel, J. E. M. S., Nakaoka, S.-I., Niwa, Y., Ono, T., Pierrot, D., Poulter, B., Rehder, G., Resplandy, L., Robertson, E., Rödenbeck, C., Rosan, T. M., Schwinger, J., Schwingshackl, C., Séférian, R., Sutton, A. J., Sweeney, C., Tanhua, T., Tans, P. P., Tian, H., Tilbrook, B., Tubiello, F., van der Werf, G. R., Vuichard, N., Wada, C., Wanninkhof, R., Watson, A. J., Willis, D., Wiltshire, A. J., Yuan, W., Yue, C., Yue, X., Zaehle, S., and Zeng, J · 2022
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Ensemble deep learning: A review
Ganaie, M., Hu, M., Malik, A., Tanveer, M., and Suganthan, P · 2022
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Carbon fluxes from land 2000–2020: bringing clarity to countries’ reporting
Grassi, G., Conchedda, G., Federici, S., Abad Viñas, R., Korosuo, A., Melo, J., Rossi, S., Sandker, M., Somogyi, Z., Vizzarri, M., and Tubiello, F. N · 2022
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Schleich, A., Durrieu, S., Soma, M., and Vega, C · 2023
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FORMS: Forest multiple source height, wood volume, and biomass maps in France at 10 to 30 m resolution based on Sentinel-1, Sentinel-2, and Global Ecosystem Dynamics Investigation (GEDI) data with a deep learning approach
Schwartz, M., Ciais, P., De Truchis, A., Chave, J., Ottlé, C., Vega, C., Wigneron, J.-P., Nicolas, M., Jouaber, S., Liu, S., Brandt, M., and Fayad, I · 2023
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Evaluating and mitigating the impact of systematic geolocation error on canopy height measurement performance of GEDI
Tang, H., Stoker, J., Luthcke, S., Armston, J., Lee, K., Blair, B., and Hofton, M · 2023
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How I Learned To Stop Worrying And Love Retraining
Zimmer, M., Spiegel, C., and Pokutta, S · 2023
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Sentinel-2 Cloud Masking with s2cloudless
Braaten, J · 2024
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Sentinel-1 – radar vision for Copernicus
European Space Agency · 2024
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Sentinel-2 – colour vision for Copernicus
European Space Agency · 2024
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High-resolution canopy height map in the Landes forest (France) based on GEDI, Sentinel-1, and Sentinel-2 data with a deep learning approach
Schwartz, M., Ciais, P., Ottlé, C., de Truchis, A., Vega, C., Fayad, I., Brandt, M., Fensholt, R., Baghdadi, N., Morneau, F., Morin, D., Guyon, D., Dayau, S., and Wigneron, J.-P · 2024
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