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Monitoring and managing Earth's forests in an informed manner is an important requirement for addressing challenges like biodiversity loss and climate change.
Applications of advances in nonlinear sensitivity analysis,
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Predicting forest stand characteristics with airborne scanning laser using a practical two-stage procedure and field data,
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N. R. Council, Completing the Forecast: Characterizing and Communicating Uncertainty for Better Decisions Using Weather and Climate Forecasts, The National Academies Press, Washington, DC, 2006
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Airborne laser scanning as a method in operational forest inventory: Status of accuracy assessments accomplished in scandinavia,
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Remote sensing of vegetation 3-d structure for biodiversity and habitat: Review and implications for lidar and radar spaceborne missions,
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T. Strutz, Data Fitting and Uncertainty: A Practical Introduction to Weighted Least Squares and Beyond, Vieweg and Teubner, Wiesbaden, DEU, 2010
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ImageNet classification with deep convolutional neural networks,
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Estimation of forest resources from a country wide laser scanning survey and national forest inventory data,
T. Nord-Larsen, J. Schumacher, · 2012
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High-resolution global maps of 21st-century forest cover change,
M. C. Hansen, P. V. Potapov, R. Moore, M. Hancher, S. A. Turubanova, A. Tyukavina, D. Thau, S. V. Stehman, S. J. Goetz, T. R. Loveland, A. Kommareddy, A. Egorov, L. Chini, C. O. Justice, J. R. G. Townshend, · 2013
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Decision making under uncertainty in energy systems: State of the art,
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Deep neural networks for object detection,
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Stochastic gradient hamiltonian monte carlo,
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Rich feature hierarchies for accurate object detection and semantic segmentation,
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Black box variational inference,
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Weight uncertainty in neural network,
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Batch normalization: Accelerating deep network training by reducing internal covariate shift,
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Adam: A method for stochastic optimization,
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Estimating crop yields with deep learning and remotely sensed data,
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Fully convolutional networks for semantic segmentation,
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Very deep convolutional networks for large-scale image recognition,
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Aboveground carbon loss in natural and managed tropical forests from 2000 to 2012,
A. Tyukavina, A. Baccini, M. Hansen, P. Potapov, S. Stehman, R. Houghton, A. Krylov, S. Turubanova, S. Goetz, · 2015
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L.-C. Chen, G. Papandreou, I. Kokkinos, K. Murphy, A. L. Yuille, · 2016
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A forest structure habitat index based on airborne laser scanning data,
N. C. Coops, P. Tompaski, W. Nijland, G. J. Rickbeil, S. E. Nielsen, C. W. Bater, J. J. Stadt, · 2016
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Dropout as a bayesian approximation: Representing model uncertainty in deep learning,
Y. Gal, Z. Ghahramani, · 2016
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I. Goodfellow, Y. Bengio, A. Courville, Deep Learning, MIT Press, 2016. http://www.deeplearningbook.org
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Mapping tree height distributions in Sub-Saharan Africa using Landsat 7 and 8 data,
M. C. Hansen, P. V. Potapov, S. J. Goetz, S. Turubanova, A. Tyukavina, A. Krylov, A. Kommareddy, A. Egorov, · 2016
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Deep residual learning for image recognition,
K. He, X. Zhang, S. Ren, J. Sun, · 2016
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You only look once: Unified, real-time object detection,
Can you trust your model's uncertainty? evaluating predictive uncertainty under dataset shift,
Y. Ovadia, E. Fertig, J. Ren, Z. Nado, D. Sculley, S. Nowozin, J. Dillon, B. Lakshminarayanan, J. Snoek, · 2019
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Annual continuous fields of woody vegetation structure in the Lower Mekong region from 2000-2017 Landsat time-series,
P. Potapov, A. Tyukavina, S. Turubanova, Y. Talero, A. Hernandez-Serna, M. Hansen, D. Saah, K. Tenneson, A. Poortinga, A. Aekakkararungroj, et al., · 2019
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Semantic segmentation of crop type in Africa: A novel dataset and analysis of deep learning methods,
R. Rustowicz, R. Cheong, L. Wang, S. Ermon, M. Burke, D. Lobell, · 2019
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Uncertainty in decision-making: A review of the international business literature,
S. Sniazhko, · 2019
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Age-independent site index mapping with repeated single-tree airborne laser scanning,
H. K. Svein Solberg, S. Puliti, · 2019
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J. Redmon, S. Divvala, R. Girshick, A. Farhadi, · 2016
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Sea ice concentration estimation during melt from dual-pol SAR scenes using deep convolutional neural networks: A case study,
L. Wang, K. A. Scott, L. Xu, D. A. Clausi, · 2016
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On calibration of modern neural networks,
C. Guo, G. Pleiss, Y. Sun, K. Q. Weinberger, · 2017
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Learning Aerial Image Segmentation from Online Maps,
P. Kaiser, J. Wegner, A. Lucchi, M. Jaggi, T. Hofmann, K. Schindler, · 2017
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What uncertainties do we need in bayesian deep learning for computer vision?,
A. Kendall, Y. Gal, · 2017
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Simple and scalable predictive uncertainty estimation using deep ensembles,
B. Lakshminarayanan, A. Pritzel, C. Blundell, · 2017
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Sen2Cor for Sentinel-2,
M. Main-Knorn, B. Pflug, J. Louis, V. Debaecker, U. Müller-Wilm, F. Gascon, · 2017
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Joint deep learning for land cover and land use classification,
C. Zhang, I. Sargent, X. Pan, H. Li, A. Gardiner, J. Hare, P. M. Atkinson, · 2019
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D. R. Alves de Almeida, S. C. Stark, C. A. Silva, C. Hamamura, R. Valbuena, 2020. URL: https://rdocumentation.org/packages/leafR/versions/0.3
2020
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Pitfalls of in-domain uncertainty estimation and ensembling in deep learning,
A. Ashukha, A. Lyzhov, D. Molchanov, D. Vetrov, · 2020
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Terrestrial laser scanning in forest ecology: Expanding the horizon,
K. Calders, J. Adams, J. Armston, H. Bartholomeus, S. Bauwens, L. P. Bentley, J. Chave, F. M. Danson, M. Demol, M. Disney, et al., · 2020
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The Global Ecosystem Dynamics Investigation: High-resolution laser ranging of the Earth’s forests and topography,
R. Dubayah, J. B. Blair, S. Goetz, L. Fatoyinbo, M. Hansen, S. Healey, M. Hofton, G. Hurtt, J. Kellner, S. Luthcke, et al., · 2020
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Evaluating scalable bayesian deep learning methods for robust computer vision,
F. K. Gustafsson, M. Danelljan, T. B. Schon, · 2020
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Modelling above-ground biomass stock over norway using national forest inventory data with ArcticDEM and Sentinel-2 data,
S. Puliti, M. Hauglin, J. Breidenbach, P. Montesano, C. Neigh, J. Rahlf, S. Solberg, T. F. Klingenberg, R. Astrup, · 2020
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Self-attention for raw optical satellite time series classification,
M. Rußwurm, M. Körner, · 2020
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The response of canopy height diversity to natural disturbances in two temperate forest landscapes,
C. Senf, A. Mori, R. Müller, J.and Seidl, · 2020
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Comparison of multi-temporal planetscope data with Landsat 8 and Sentinel-2 data for estimating airborne LiDAR derived canopy height in temperate forests,
K. Shimizu, T. Ota, N. Mizoue, H. Saito, · 2020
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Standardizing ecosystem morphological traits from 3d information sources,
R. Valbuena, B. O’Connor, F. Zellweger, W. Simonson, P. Vihervaara, M. Maltamo, C. Silva, D. Almeida, F. Danks, F. Morsdorf, G. Chirici, R. Lucas, D. Coomes, N. Coops, · 2020
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Bayesian deep learning and a probabilistic perspective of generalization,
A. G. Wilson, P. Izmailov, · 2020
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Determining maximum entropy in 3d remote sensing height distributions and using it to improve aboveground biomass modelling via stratification,
S. Adnan, M. Maltamo, L. Mehtätalo, R. N. L. Ammaturo, P. Packalen, R. Valbuena, · 2021
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Decision-making under uncertainty for the deployment of future hyperconnected networks: A survey,
N. Alzate-Mejía, G. Santos-Boada, J. R. de Almeida-Amazonas, · 2021
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Deep neural networks with transfer learning for forest variable estimation using Sentinel-2 imagery in boreal forest,
H. Astola, L. Seitsonen, E. Halme, M. Molinier, A. Lönnqvist, · 2021
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National mapping and estimation of forest area by dominant tree species using sentinel-2 data,
J. Breidenbach, L. T. Waser, M. Debella-Gilo, J. Schumacher, J. Rahlf, M. Hauglin, S. Puliti, R. Astrup, · 2021
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European Space Agency, Sentinel Application Platform (SNAP) v8.0.0, [Online]. Available from: https://step.esa.int/main/download/snap-download/ , 2020a. Accessed: 2021-08-31
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European Space Agency, SNAP Sentinel-1 Toolbox (S1TBX) v8.0, [Online]. Available from: https://sentinel.esa.int/web/sentinel/toolboxes/sentinel-1 , 2020b. Accessed: 2021-08-31
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European Space Agency, ESA Copernicus Earth observation programme, [Online]. Available from: https://www.copernicus.eu/en , 2021. Accessed: 2021-08-31
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European Space Agency, Sentinel-1 User Guide, [Online]. Available from: https://sentinels.copernicus.eu/web/sentinel/user-guides/sentinel-1-sar , 2021. Accessed: 2021-08-31
2021
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FU Berlin, Remote Sensing Data Analysis online course, [Online]. Available from: https://blogs.fu-berlin.de/reseda/ , 2019. Accessed: 2021-08-31
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High carbon stock mapping at large scale with optical satellite imagery and spaceborne LIDAR,
N. Lang, K. Schindler, J. D. Wegner, · 2021
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Mapping global forest canopy height through integration of GEDI and Landsat data,
P. Potapov, X. Li, A. Hernandez-Serna, A. Tyukavina, M. C. Hansen, A. Kommareddy, A. Pickens, S. Turubanova, H. Tang, C. E. Silva, et al., · 2021
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Above-ground biomass change estimation using national forest inventory data with Sentinel-2 and Landsat,
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Mapping oil palm density at country scale: An active learning approach,
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Mapping functional diversity using individual tree-based morphological and physiological traits in a subtropical forest,
Z. Zheng, Y. Zeng, F. D. Schneider, Y. Zhao, D. Zhao, B. Schmid, M. E. Schaepman, F. Morsdorf, · 2021
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