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NASA's Global Ecosystem Dynamics Investigation (GEDI) is a key climate mission whose goal is to advance our understanding of the role of forests in the global carbon cycle.
Dropout: a simple way to prevent neural networks from overfitting
Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R., 2014 · 1958
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Modeling laser altimeter return waveforms over complex vegetation using high-resolution elevation data
Blair, J.B., Hofton, M.A., 1999 · 1999
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The case for Bayesian deep learning
Wilson, A.G., 2020 · 2001
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Geoscience laser altimeter system (glas) on the icesat mission: on-orbit measurement performance
Abshire, J.B., Sun, X., Riris, H., Sirota, J.M., McGarry, J.F., Palm, S., Yi, D., Liiva, P., 2005 · 2005
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The shuttle radar topography mission
Farr, T.G., Rosen, P.A., Caro, E., Crippen, R., Duren, R., Hensley, S., Kobrick, M., Paller, M., Rodriguez, E., Roth, L., et al., 2007 · 2007
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Global patterns and determinants of vascular plant diversity
Kreft, H., Jetz, W., 2007 · 2007
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Introduction to information retrieval. volume 39
Schütze, H., Manning, C.D., Raghavan, P., 2008 · 2008
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Aleatory or epistemic? does it matter?
Der Kiureghian, A., Ditlevsen, O., 2009 · 2009
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Estimation of tropical forest height and biomass dynamics using lidar remote sensing at la selva, costa rica
Dubayah, R.O., Sheldon, S., Clark, D.B., Hofton, M.A., Blair, J.B., Hurtt, G.C., Chazdon, R.L., 2010 · 2010
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Towards ground-truthing of spaceborne estimates of above-ground life biomass and leaf area index in tropical rain forests
Köhler, P., Huth, A., 2010 · 2010
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Harmonized global maps of above and belowground biomass carbon density in the year 2010
Spawn, S.A., Sullivan, C.C., Lark, T.J., Gibbs, H.K., 2020 · 2010
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A large and persistent carbon sink in the world’s forests
Pan, Y., Birdsey, R.A., Fang, J., Houghton, R., Kauppi, P.E., Kurz, W.A., Phillips, O.L., Shvidenko, A., Lewis, S.L., Canadell, J.G., et al., 2011 · 2011
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Benchmark map of forest carbon stocks in tropical regions across three continents
Saatchi, S.S., Harris, N.L., Brown, S., Lefsky, M., Mitchard, E.T., Salas, W., Zutta, B.R., Buermann, W., Lewis, S.L., Hagen, S., et al., 2011 · 2011
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Estimated carbon dioxide emissions from tropical deforestation improved by carbon-density maps
Baccini, A., Goetz, S., Walker, W., Laporte, N., Sun, M., Sulla-Menashe, D., Hackler, J., Beck, P., Dubayah, R., Friedl, M., et al., 2012 · 2012
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Efficient backprop, in: Neural networks: Tricks of the trade. Springer, pp. 9–48
LeCun, Y.A., Bottou, L., Orr, G.B., Müller, K.R., 2012 · 2012
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Uncertainty in the spatial distribution of tropical forest biomass: a comparison of pan-tropical maps
Mitchard, E.T., Saatchi, S.S., Baccini, A., Asner, G.P., Goetz, S.J., Harris, N.L., Brown, S., 2013 · 2013
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Mapping tropical forest carbon: Calibrating plot estimates to a simple lidar metric
Asner, G.P., Mascaro, J., 2014 · 2014
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Integrated risk and uncertainty assessment of climate change response policies, in: Climate Change 2014: Mitigation of Climate Change: Working Group III Contribution to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press, pp. 151–206
Kunreuther, H., Gupta, S., Bosetti, V., Cooke, R., Dutt, V., Ha-Duong, M., Held, H., Llanes-Regueiro, J., Patt, A., Shittu, E., et al., 2014 · 2014
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Markedly divergent estimates of Amazon forest carbon density from ground plots and satellites
Mitchard, E.T., Feldpausch, T.R., Brienen, R.J., Lopez-Gonzalez, G., Monteagudo, A., Baker, T.R., Lewis, S.L., Lloyd, J., Quesada, C.A., Gloor, M., et al., 2014 · 2014
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MOD44B MODIS/Terra vegetation continuous fields yearly l3 global 250 m sin grid v006, data set, nasa eosdis l. process. daac
Dimiceli, C., Carroll, M., Sohlberg, R., Kim, D., Kelly, M., Townshend, J., 2015 · 2015
Cited alongside, same era.
Adam: A method for stochastic optimization, in: Bengio, Y., LeCun, Y. (Eds.), 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings
Kingma, D.P., Ba, J., 2015 · 2015
Cited alongside, same era.
An integrated pan-tropical biomass map using multiple reference datasets
Avitabile, V., Herold, M., Heuvelink, G.B., Lewis, S.L., Phillips, O.L., Asner, G.P., Armston, J., Ashton, P.S., Banin, L., Bayol, N., et al., 2016 · 2016
Cited alongside, same era.
Deep Learning
Goodfellow, I., Bengio, Y., Courville, A., 2016 · 2016
Cited alongside, same era.
Deep residual learning for image recognition, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778
He, K., Zhang, X., Ren, S., Sun, J., 2016 · 2016
Class-balanced loss based on effective number of samples, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 9268–9277
Cui, Y., Jia, M., Lin, T.Y., Song, Y., Belongie, S., 2019 · 2019
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Global carbon budget 2019
Friedlingstein, P., Jones, M., O’sullivan, M., Andrew, R., Hauck, J., Peters, G., Peters, W., Pongratz, J., Sitch, S., Le Quéré, C., et al., 2019 · 2019
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The GEDI simulator: A large-footprint waveform lidar simulator for calibration and validation of spaceborne missions
Hancock, S., Armston, J., Hofton, M., Sun, X., Tang, H., Duncanson, L.I., Kellner, J.R., Dubayah, R., 2019 · 2019
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Algorithm Theoretical Basis Document (ATBD) for GEDI Transmit and Receive Waveform Processing for L1 and L2 Products
Hofton, M., Blair, J.B., Story, S., Yi, D., 2019 · 2019
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Global canopy top height estimates from GEDI LIDAR waveforms for 2019
Lang, N., Kalischek, N., Armston, J., Schindler, K., Dubayah, R., Wegner, J.D., 2021 · 2019
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Cited alongside, same era.
Global patterns and determinants of forest canopy height
Tao, S., Guo, Q., Li, C., Wang, Z., Fang, J., 2016 · 2016
Cited alongside, same era.
Coverage of high biomass forests by the ESA BIOMASS mission under defense restrictions
Carreiras, J.M., Quegan, S., Le Toan, T., Minh, D.H.T., Saatchi, S.S., Carvalhais, N., Reichstein, M., Scipal, K., 2017 · 2017
Cited alongside, same era.
Exploring the relationship between canopy height and terrestrial plant diversity
Gatti, R.C., Di Paola, A., Bombelli, A., Noce, S., Valentini, R., 2017 · 2017
Cited alongside, same era.
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
Cited alongside, same era.
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
Cited alongside, same era.
What uncertainties do we need in bayesian deep learning for computer vision?, in: Advances in neural information processing systems, pp. 5574–5584
Kendall, A., Gal, Y., 2017 · 2017
Cited alongside, same era.
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
Cited alongside, same era.
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Country-wide high-resolution vegetation height mapping with sentinel-2
Lang, N., Schindler, K., Wegner, J.D., 2019 · 2019
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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
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Evaluating scalable bayesian deep learning methods for robust computer vision, in: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, pp. 318–319
Gustafsson, F.K., Danelljan, M., Schon, T.B., 2020 · 2020
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Highly local model calibration with a new GEDI LiDAR asset on Google Earth Engine reduces landsat forest height signal saturation
Healey, S.P., Yang, Z., Gorelick, N., Ilyushchenko, S., 2020 · 2020
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Well-calibrated regression uncertainty in medical imaging with deep learning, in: Medical Imaging with Deep Learning, PMLR. pp. 393–412
Laves, M.H., Ihler, S., Fast, J.F., Kahrs, L.A., Ortmaier, T., 2020 · 2020
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Evaluating the potential of full-waveform lidar for mapping pan-tropical tree species richness
Marselis, S.M., Abernethy, K., Alonso, A., Armston, J., Baker, T.R., Bastin, J.F., Bogaert, J., Boyd, D.S., Boeckx, P., Burslem, D.F., et al., 2020 · 2020
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Spatial validation reveals poor predictive performance of large-scale ecological mapping models
Ploton, P., Mortier, F., Réjou-Méchain, M., Barbier, N., Picard, N., Rossi, V., Dormann, C., Cornu, G., Viennois, G., Bayol, N., et al., 2020 · 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., et al., 2020 · 2020
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Global priority areas for ecosystem restoration
Strassburg, B.B., Iribarrem, A., Beyer, H.L., Cordeiro, C.L., Crouzeilles, R., Jakovac, C.C., Junqueira, A.B., Lacerda, E., Latawiec, A.E., Balmford, A., et al., 2020 · 2020
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Standardizing ecosystem morphological traits from 3d information sources
Valbuena, R., O’Connor, B., Zellweger, F., Simonson, W., Vihervaara, P., Maltamo, M., Silva, C., Almeida, D., Danks, F., Morsdorf, F., et al., 2020 · 2020
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Bayesian deep learning and a probabilistic perspective of generalization, in: Larochelle, H., Ranzato, M., Hadsell, R., Balcan, M.F., Lin, H. (Eds.), Advances in Neural Information Processing Systems, Curran Associates, Inc.. pp. 4697–4708
Wilson, A.G., Izmailov, P., 2020 · 2020
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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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Evaluating the tropical forest carbon sink
Phillips, O.L., Lewis, S.L., 2014 · 2041
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