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Vegetation structure mapping is critical for understanding the global carbon cycle and monitoring nature-based approaches to climate adaptation and mitigation.
The contribution of trees outside of forests to landscape carbon and climate change mitigation in west Africa
Skole, D.L., Samek, J.H., Dieng, M., Mbow, C., 2021 · 1999
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Monitoring and Evaluating Forest Restoration Success. Springer New York, New York, NY
Vallauri, D., Aronson, J., Dudley, N., Vallejo, R., 2005 · 2005
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Old-growth forests as global carbon sinks
Luyssaert, S., Schulze, E.D., Börner, A., Knohl, A., Hessenmöller, D., Law, B.E., Ciais, P., Grace, J., 2008 · 2008
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An image is worth 16x16 words: Transformers for image recognition at scale, in: 9th International Conference on Learning Representations, ICLR 2021, Virtual Event, Austria, May 3-7, 2021, OpenReview.net
Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., Uszkoreit, J., Houlsby, N., 2021a · 2010
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An image is worth 16x16 words: Transformers for image recognition at scale
Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., Uszkoreit, J., Houlsby, N., 2021b · 2010
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The Brazilian Atlantic Forest: A Shrinking Biodiversity Hotspot. Springer Berlin Heidelberg, Berlin, Heidelberg
Ribeiro, M.C., Martensen, A.C., Metzger, J.P., Tabarelli, M., Scarano, F., Fortin, M.J., 2011 · 2011
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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., Kommareddy, A., Egorov, A., Chini, L., Justice, C.O., Townshend, J.R.G., 2013 · 2013
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Depth map prediction from a single image using a multi-scale deep network
Eigen, D., Puhrsch, C., Fergus, R., 2014 · 2014
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Generating pit-free canopy height models from airborne lidar
Khosravipour, A., Skidmore, A.K., Isenburg, M., Wang, T., Hussin, Y.A., 2014 · 2014
Earlier work this paper cites.
Good practices for estimating area and assessing accuracy of land change
Olofsson, P., Foody, G.M., Herold, M., Stehman, S.V., Woodcock, C.E., Wulder, M.A., 2014 · 2014
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Estimating area and map accuracy for stratified random sampling when the strata are different from the map classes
Stehman, S.V., 2014 · 2014
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Rate of tree carbon accumulation increases continuously with tree size
Stephenson, N.L., Das, A.J., Condit, R., Russo, S.E., Baker, P.J., Beckman, N.G., Coomes, D.A., Lines, E.R., Morris, W.K., Rüger, N., Álvarez, E., Blundo, C., Bunyavejchewin, S., Chuyong, G., Davies, S.J., Duque, Á., Ewango, C.N., Flores, O., Franklin, J.F., Grau, H.R., Hao, Z., Harmon, M.E., Hubbell, S.P., Kenfack, D., Lin, Y., Makana, J.R., Malizia, A., Malizia, L.R., Pabst, R.J., Pongpattananurak, N., Su, S.H., Sun, I.F., Tan, S., Thomas, D., van Mantgem, P.J., Wang, X., Wiser, S.K., Zavala, M.A., 2014 · 2014
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The hunt for the world’s missing carbon
Popkin, G., 2015 · 2015
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U-net: Convolutional networks for biomedical image segmentation, in: Medical Image Computing and Computer-Assisted Intervention–MICCAI 2015: 18th International Conference, Munich, Germany, October 5-9, 2015, Proceedings, Part III 18, Springer. pp. 234–241
Ronneberger, O., Fischer, P., Brox, T., 2015 · 2015
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Humid tropical forest disturbance alerts using landsat data
Hansen, M.C., Krylov, A., Tyukavina, A., Potapov, P.V., Turubanova, S., Zutta, B., Ifo, S., Margono, B., Stolle, F., Moore, R., 2016 · 2016
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Understanding the effective receptive field in deep convolutional neural networks
Luo, W., Li, Y., Urtasun, R., Zemel, R., 2016 · 2016
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Mapbiomas initiative: Mapping annual land cover and land use changes in brazil from 1985 to 2017
Azevedo, T., Souza, C., Zanin Shimbo, J., Alencar, A., 2018 · 2017
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Amazon. Terrain Tiles on AWS
Mapzen, 2017 · 2017
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The ice, cloud, and land elevation satellite-2 (icesat-2): Science requirements, concept, and implementation
Markus, T., Neumann, T., Martino, A., Abdalati, W., Brunt, K., Csatho, B., Farrell, S., Fricker, H., Gardner, A., Harding, D., Jasinski, M., Kwok, R., Magruder, L., Lubin, D., Luthcke, S., Morison, J., Nelson, R., Neuenschwander, A., Palm, S., Popescu, S., Shum, C., Schutz, B.E., Smith, B., Yang, Y., Zwally, J., 2017 · 2017
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Road extraction by deep residual U-Net
Zhang, Z., Liu, Q., Wang, Y., 2017 · 2017
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Lidar surveys over selected forest research sites, Brazilian Amazon, 2008-2018. ORNL DAAC, Oak Ridge, Tennessee, USA
Dos-Santos, M., Keller, M., Morton, D., 2019 · 2018
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A compromise principle in deep monocular depth estimation
Fu, H., Gong, M., Wang, C., Tao, D., 2018 · 2018
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Monocular depth estimation with hierarchical fusion of dilated cnns and soft-weighted-sum inference
Li, B., Dai, Y., He, M., 2018 · 2018
Earlier work this paper cites.
Inventário florestal nacional do brasil - uma abordagem em escala de paisagem para monitorar e avaliar paisagens florestais
da Luz, N.B., Garrastazu, M.C., Rosot, M.A.D., Maran, J.C., de Oliveira, Y.M.M., Franciscon, L., Cardoso, D.J., de Freitas, J.V., 2018 · 2018
Cited alongside, same era.
Monitoring young tropical forest restoration sites: How much to measure?
Viani, R.A.G., Barreto, T.E., Farah, F.T., Rodrigues, R.R., Brancalion, P.H.S., 2018 · 2018
Cited alongside, same era.
Monitoring tropical forest carbon stocks and emissions using planet satellite data
Csillik, O., Kumar, P., Mascaro, J., O’Shea, T., Asner, G.P., 2019 · 2019
Cited alongside, same era.
Global carbon budget 2019
Friedlingstein, P., Jones, M.W., O’Sullivan, M., Andrew, R.M., Hauck, J., Peters, G.P., Peters, W., Pongratz, J., Sitch, S., Le Quéré, C., Bakker, D.C.E., Canadell, J.G., Ciais, P., Jackson, R.B., Anthoni, P., Barbero, L., Bastos, A., Bastrikov, V., Becker, M., Bopp, L., Buitenhuis, E., Chandra, N., Chevallier, F., Chini, L.P., Currie, K.I., Feely, R.A., Gehlen, M., Gilfillan, D., Gkritzalis, T., Goll, D.S., Gruber, N., Gutekunst, S., Harris, I., Haverd, V., Houghton, R.A., Hurtt, G., Ilyina, T., Jain, A.K., Joetzjer, E., Kaplan, J.O., Kato, E., Klein Goldewijk, K., Korsbakken, J.I., Landschützer, P., Lauvset, S.K., Lefèvre, N., Lenton, A., Lienert, S., Lombardozzi, D., Marland, G., McGuire, P.C., Melton, J.R., Metzl, N., Munro, D.R., Nabel, J.E.M.S., Nakaoka, S.I., Neill, C., Omar, A.M., Ono, T., Peregon, A., Pierrot, D., Poulter, B., Rehder, G., Resplandy, L., Robertson, E., Rödenbeck, C., Séférian, R., Schwinger, J., Smith, N., Tans, P.P., Tian, H., Tilbrook, B., Tubiello, F.N., van der Werf, G.R., Wiltshire, A.J., Zaehle, S., 2019 · 2019
Continental-scale building detection from high resolution satellite imagery
Sirko, W., Kashubin, S., Ritter, M., Annkah, A., Bouchareb, Y.S.E., Dauphin, Y.N., Keysers, D., Neumann, M., Cissé, M., Quinn, J., 2021 · 2021
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A benchmark dataset for canopy crown detection and delineation in co-registered airborne RGB, LiDAR and hyperspectral imagery from the National Ecological Observation Network
Weinstein, B.G., Graves, S.J., Marconi, S., Singh, A., Zare, A., Stewart, D., Bohlman, S.A., White, E.P., 2021 · 2021
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Masked autoencoders are scalable vision learners, in: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 16000–16009
He, K., Chen, X., Xie, S., Li, Y., Dollár, P., Girshick, R., 2022 · 2022
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Nation-wide mapping of tree-level aboveground carbon stocks in rwanda
Mugabowindekwe, M., Brandt, M., Chave, J., Reiner, F., Skole, D.L., Kariryaa, A., Igel, C., Hiernaux, P., Ciais, P., Mertz, O., et al., 2022 · 2022
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Cited alongside, same era.
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
Cited alongside, same era.
An unexpectedly large count of trees in the West African Sahara and Sahel
Brandt, M., Tucker, C.J., Kariryaa, A., Rasmussen, K., Abel, C., Small, J., Chave, J., Rasmussen, L.V., Hiernaux, P., Diouf, A.A., Kergoat, L., Mertz, O., Igel, C., Gieseke, F., Schöning, J., Li, S., Melocik, K., Meyer, J., Sinno, S., Romero, E., Glennie, E., Montagu, A., Dendoncker, M., Fensholt, R., 2020 · 2020
Cited alongside, same era.
Monitoring forest structure to guide adaptive management of forest restoration: a review of remote sensing approaches
Camarretta, N., Harrison, P.A., Bailey, T., Potts, B., Lucieer, A., Davidson, N., Hunt, M., 2020 · 2020
Cited alongside, same era.
Mapping carbon accumulation potential from global natural forest regrowth
Cook-Patton, S.C., Leavitt, S.M., Gibbs, D., Harris, N.L., Lister, K., Anderson-Teixeira, K.J., Briggs, R.D., Chazdon, R.L., Crowther, T.W., Ellis, P.W., Griscom, H.P., Herrmann, V., Holl, K.D., Houghton, R.A., Larrosa, C., Lomax, G., Lucas, R., Madsen, P., Malhi, Y., Paquette, A., Parker, J.D., Paul, K., Routh, D., Roxburgh, S., Saatchi, S., van den Hoogen, J., Walker, W.S., Wheeler, C.E., Wood, S.A., Xu, L., Griscom, B.W., 2020 · 2020
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., Armston, J., Tang, H., Duncanson, L., Hancock, S., Jantz, P., Marselis, S., Patterson, P.L., Qi, W., Silva, C., 2020 · 2020
Cited alongside, same era.
Biomass estimation from simulated GEDI, ICESat-2 and NISAR across environmental gradients in Sonoma County, California
Duncanson, L., Neuenschwander, A., Hancock, S., Thomas, N., Fatoyinbo, T., Simard, M., Silva, C.A., Armston, J., Luthcke, S.B., Hofton, M., Kellner, J.R., Dubayah, R., 2020 · 2020
Cited alongside, same era.
High-resolution mapping of forest canopy height using machine learning by coupling icesat-2 lidar with sentinel-1, sentinel-2 and landsat-8 data
Li, W., Niu, Z., Shang, R., Qin, Y., Wang, L., Chen, H., 2020 · 2020
Cited alongside, same era.
Timber exploitation in colonial brazil: A historical perspective of the atlantic forest
Maioli, V., Belharte, S., Stuker Kropf, M., Callado, C.H., 2020 · 2020
Cited alongside, same era.
Ecosystem structure (dp3.30015.001)
National Ecological Observatory Network (NEON), 2022 · 2022
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Scale-MAE: A scale-aware masked autoencoder for multiscale geospatial representation learning
Reed, C.J., Gupta, R., Li, S., Brockman, S., Funk, C., Clipp, B., Candido, S., Uyttendaele, M., Darrell, T., 2022 · 2022
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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., Wigneron, J.P., 2022 · 2022
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Revisiting Weakly Supervised Pre-Training of Visual Perception Models, in: CVPR
Singh, M., Gustafson, L., Adcock, A., Reis, V.d.F., Gedik, B., Kosaraju, R.P., Mahajan, D., Girshick, R., Dollár, P., van der Maaten, L., 2022 · 2022
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Woody species composition, structure, and carbon stock of coffee-based agroforestry system along an elevation gradient in the moist mid-highlands of southern ethiopia
Tesfay, F., Moges, Y., Asfaw, Z., 2022 · 2022
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A ViT-based multiscale feature fusion approach for remote sensing image segmentation
Wang, W., Tang, C., Wang, X., Zheng, B., 2022 · 2022
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3d fuel structure in relation to prescribed fire, ca 2020. national center for airborne laser mapping (ncalm). distributed by opentopography
Brande, K., 2021 · 2023
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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., Bazzi, H., 2023 · 2023
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Large-scale date palm tree segmentation from multiscale uav-based and aerial images using deep vision transformers
Gibril, M.B.A., Shafri, H.Z.M., Al-Ruzouq, R., Shanableh, A., Nahas, F., Al Mansoori, S., 2023 · 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., Li, S., Mugabowindekwe, M., Saatchi, S., Yue, Y., Chen, Z., Fensholt, R., 2023 · 2023
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Dinov2: Learning robust visual features without supervision
Oquab, M., Darcet, T., Moutakanni, T., Vo, H., Szafraniec, M., Khalidov, V., Fernandez, P., Haziza, D., Massa, F., El-Nouby, A., Assran, M., Ballas, N., Galuba, W., Howes, R., Huang, P.Y., Li, S.W., Misra, I., Rabbat, M., Sharma, V., Synnaeve, G., Xu, H., Jegou, H., Mairal, J., Labatut, P., Joulin, A., Bojanowski, P., 2023 · 2023
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Sub-meter tree height mapping of california using aerial images and lidar-informed u-net model
Wagner, F.H., Roberts, S., Ritz, A.L., Carter, G., Dalagnol, R., Favrichon, S., Hirye, M.C., Brandt, M., Ciais, P., Saatchi, S., 2023 · 2023
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GEDI L4A footprint level aboveground biomass density, version 2.1
Dubayah, R., Armston, J., Kellner, J., Duncanson, L., Healey, S., Patterson, P., Hancock, S., Tang, H., Bruening, J., Hofton, M., Blair, J., Luthcke, S., 2022 · 2056
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Accuracy assessment of gedi terrain elevation and canopy height estimates in european temperate forests: Influence of environmental and acquisition parameters
Adam, M., Urbazaev, M., Dubois, C., Schmullius, C., 2020 · 2072
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Deep neural networks with transfer learning for forest variable estimation using sentinel-2 imagery in boreal forest
Astola, H., Seitsonen, L., Halme, E., Molinier, M., Lönnqvist, A., 2021 · 2072
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Accuracy assessment in convolutional neural network-based deep learning remote sensing studies—part 2: Recommendations and best practices
Maxwell, A.E., Warner, T.A., Guillén, L.A., 2021 · 2072
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Use-specific considerations for optimising data quality trade-offs in citizen science: Recommendations from a targeted literature review to improve the usability and utility for the calibration and validation of remotely sensed products
Schacher, A., Roger, E., Williams, K.J., Stenson, M.P., Sparrow, B., Lacey, J., 2023 · 2072
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Efficient transformer for remote sensing image segmentation
Xu, Z., Zhang, W., Zhang, T., Yang, Z., Li, J., 2021 · 2072
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