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The worldwide variation in vegetation height is fundamental to the global carbon cycle and central to the functioning of ecosystems and their biodiversity.
Quantifying the carbon emissions of machine learning
Lacoste, A., Luccioni, A., Schmidt, V., Dandres, T., 2019 · 1910
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On bird species diversity
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Pitfalls of in-domain uncertainty estimation and ensembling in deep learning
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Animal species diversity driven by habitat heterogeneity/diversity: the importance of keystone structures
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Monitoring and estimating tropical forest carbon stocks: making redd a reality
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Data Fitting and Uncertainty: A Practical Introduction to Weighted Least Squares and Beyond
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High-resolution global maps of 21st-century forest cover change
Hansen, M.C., Potapov, P.V., Moore, R., Hancher, M., Turubanova, S.A., Tyukavina, A., Thau, D., Stehman, S.V., Goetz, S.J., Loveland, T.R., et al., 2013 · 2013
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Adam: A method for stochastic optimization, in: Bengio, Y., LeCun, Y. (Eds.), Proceedings of the International Conference on Learning Representations
Kingma, D.P., Ba, J., 2015 · 2015
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A global, remote sensing-based characterization of terrestrial habitat heterogeneity for biodiversity and ecosystem modelling
Tuanmu, M.N., Jetz, W., 2015 · 2015
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Mapping tree height distributions in Sub-Saharan Africa using Landsat 7 and 8 data
Hansen, M.C., Potapov, P.V., Goetz, S.J., Turubanova, S., Tyukavina, A., Krylov, A., Kommareddy, A., Egorov, A., 2016 · 2016
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Xception: Deep learning with depthwise separable convolutions, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1251–1258
Chollet, F., 2017 · 2017
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Google Earth Engine: Planetary-scale geospatial analysis for everyone
Gorelick, N., Hancher, M., Dixon, M., Ilyushchenko, S., Thau, D., Moore, R., 2017 · 2017
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On calibration of modern neural networks, in: International Conference on Machine Learning, pp. 1321–1330
Guo, C., Pleiss, G., Sun, Y., Weinberger, K.Q., 2017 · 2017
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Allometric equations for integrating remote sensing imagery into forest monitoring programmes
Jucker, T., Caspersen, J., Chave, J., Antin, C., Barbier, N., Bongers, F., Dalponte, M., van Ewijk, K.Y., Forrester, D.I., Haeni, M., et al., 2017 · 2017
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What uncertainties do we need in bayesian deep learning for computer vision?
Kendall, A., Gal, Y., 2017 · 2017
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Simple and scalable predictive uncertainty estimation using deep ensembles, in: Advances in Neural Information Processing Systems, pp. 6402–6413
Lakshminarayanan, B., Pritzel, A., Blundell, C., 2017 · 2017
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Quantifying forest biomass carbon stocks from space
Rodríguez-Veiga, P., Wheeler, J., Louis, V., Tansey, K., Balzter, H., 2017 · 2017
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Mapped aboveground carbon stocks to advance forest conservation and recovery in Malaysian Borneo
Asner, G.P., Brodrick, P.G., Philipson, C., Vaughn, N.R., Martin, R.E., Knapp, D.E., Heckler, J., Evans, L.J., Jucker, T., Goossens, B., et al., 2018 · 2018
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Processing of NASA LVIS elevation and canopy (LGE, LCE and LGW) data products, version 1.0
Blair, J., 2018 · 2018
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Multiple forest attributes underpin the supply of multiple ecosystem services
Felipe-Lucia, M.R., Soliveres, S., Penone, C., Manning, P., van der Plas, F., Boch, S., Prati, D., Ammer, C., Schall, P., Gossner, M.M., et al., 2018 · 2018
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Canopy structure and topography jointly constrain the microclimate of human-modified tropical landscapes
Jucker, T., Hardwick, S.R., Both, S., Elias, D.M., Ewers, R.M., Milodowski, D.T., Swinfield, T., Coomes, D.A., 2018 · 2018
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The biomass mission: objectives and requirements, in: IGARSS 2018-2018 IEEE International Geoscience and Remote Sensing Symposium, IEEE. pp. 8563–8566
Le Toan, T., Chave, J., Dall, J., Papathanassiou, K., Paillou, P., Rechstein, M., Quegan, S., Saatchi, S., Seipel, K., Shugart, H., et al., 2018 · 2018
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Redefining ecosystem multifunctionality
Manning, P., Van Der Plas, F., Soliveres, S., Allan, E., Maestre, F.T., Mace, G., Whittingham, M.J., Fischer, M., 2018 · 2018
Becker, A., Russo, S., Puliti, S., Lang, N., Schindler, K., Wegner, J.D., 2021 · 2021
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Global carbon budget 2021
Friedlingstein, P., Jones, M.W., O’Sullivan, M., Andrew, R.M., Bakker, D.C., Hauck, J., Le Quéré, C., Peters, G.P., Peters, W., Pongratz, J., et al., 2021 · 2021
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Mapping the deforestation footprint of nations reveals growing threat to tropical forests
Hoang, N.T., Kanemoto, K., 2021 · 2021
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Highly accurate protein structure prediction with alphafold
Jumper, J., Evans, R., Pritzel, A., Green, T., Figurnov, M., Ronneberger, O., Tunyasuvunakool, K., Bates, R., Žídek, A., Potapenko, A., et al., 2021 · 2021
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Review on convolutional neural networks (cnn) in vegetation remote sensing
Kattenborn, T., Leitloff, J., Schiefer, F., Hinz, S., 2021 · 2021
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Cited alongside, same era.
Global buffering of temperatures under forest canopies
De Frenne, P., Zellweger, F., Rodriguez-Sanchez, F., Scheffers, B.R., Hylander, K., Luoto, M., Vellend, M., Verheyen, K., Lenoir, J., 2019 · 2019
Cited alongside, same era.
Guided super-resolution as pixel-to-pixel transformation, in: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 8829–8837
de Lutio, R., D’Aronco, S., Wegner, J.D., Schindler, K., 2019 · 2019
Cited alongside, same era.
Global canopy top height estimates from GEDI LIDAR waveforms for 2019
Lang, N., Kalischek, N., Armston, J., Schindler, K., Dubayah, R., Wegner, J.D., 2021a · 2019
Cited alongside, same era.
Country-wide high-resolution vegetation height mapping with Sentinel-2
Lang, N., Schindler, K., Wegner, J.D., 2019 · 2019
Cited alongside, same era.
Can you trust your model’s uncertainty? evaluating predictive uncertainty under dataset shift, in: Advances in Neural Information Processing Systems, pp. 13991–14002
Ovadia, Y., Fertig, E., Ren, J., Nado, Z., Sculley, D., Nowozin, S., Dillon, J., Lakshminarayanan, B., Snoek, J., 2019 · 2019
Cited alongside, same era.
Deep learning and process understanding for data-driven earth system science
Reichstein, M., Camps-Valls, G., Stevens, B., Jung, M., Denzler, J., Carvalhais, N., et al., 2019 · 2019
Cited alongside, same era.
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 · 2020
Cited alongside, same era.
The three major axes of terrestrial ecosystem function
Migliavacca, M., Musavi, T., Mahecha, M.D., Nelson, J.A., Knauer, J., Baldocchi, D.D., Perez-Priego, O., Christiansen, R., Peters, J., Anderson, K., et al., 2021 · 2021
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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., et al., 2021 · 2021
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Skilful precipitation nowcasting using deep generative models of radar
Ravuri, S., Lenc, K., Willson, M., Kangin, D., Lam, R., Mirowski, P., Fitzsimons, M., Athanassiadou, M., Kashem, S., Madge, S., et al., 2021 · 2021
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Mapping oil palm density at country scale: An active learning approach
Rodríguez, A.C., D’Aronco, S., Schindler, K., Wegner, J.D., 2021 · 2021
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The impact of geolocation uncertainty on GEDI tropical forest canopy height estimation and change monitoring
Roy, D.P., Kashongwe, H.B., Armston, J., 2021 · 2021
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Priority list of biodiversity metrics to observe from space
Skidmore, A.K., Coops, N.C., Neinavaz, E., Ali, A., Schaepman, M.E., Paganini, M., Kissling, W.D., Vihervaara, P., Darvishzadeh, R., Feilhauer, H., et al., 2021 · 2021
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Protected planet: The world database on protected areas (WDPA)
UNEP-WCMC, IUCN, 2021 · 2021
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Aboveground biomass density models for NASA’s Global Ecosystem Dynamics Investigation (GEDI) lidar mission
Duncanson, L., Kellner, J.R., Armston, J., Dubayah, R., Minor, D.M., Hancock, S., Healey, S.P., Patterson, P.L., Saarela, S., Marselis, S., et al., 2022 · 2022
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Global canopy height regression and uncertainty estimation from GEDI LIDAR waveforms with deep ensembles
Lang, N., Kalischek, N., Armston, J., Schindler, K., Dubayah, R., Wegner, J.D., 2022 · 2022
Closest in time.
Decarbonization strategies for Switzerland considering embedded greenhouse gas emissions in electricity imports
Rüdisüli, M., Romano, E., Eggimann, S., Patel, M.K., 2022 · 2022
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Perspectives in machine learning for wildlife conservation
Tuia, D., Kellenberger, B., Beery, S., Costelloe, B.R., Zuffi, S., Risse, B., Mathis, A., Mathis, M.W., van Langevelde, F., Burghardt, T., et al., 2022 · 2022
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United Nations strategic plan for forests 2017–2030
United Nations, · 2022
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