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Global vegetation structure mapping is critical for understanding the global carbon cycle and maximizing the efficacy of nature-based carbon sequestration initiatives.
Olson, D.M., Dinerstein, E., Wikramanayake, E.D., Burgess, N.D., Powell, G.V., Underwood, E.C., D’amico, J.A., Itoua, I., Strand, H.E., Morrison, J.C., et al
2001
Earlier work this paper cites.
Shi, W., Caballero, J., Huszár, F., Totz, J., Aitken, A.P., Bishop, R., Rueckert, D., Wang, Z.: Real-time single image and video super-resolution using an efficient sub-pixel convolutional neural network. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1874–1883 (2016)
2016
Earlier work this paper cites.
Jucker, T., Caspersen, J., Chave, J., Antin, C., Barbier, N., Bongers, F., Dalponte, M., Ewijk, K.Y., Forrester, D.I., Haeni, M., et al
2017
Earlier work this paper cites.
Xia, G.-S., Bai, X., Ding, J., Zhu, Z., Belongie, S., Luo, J., Datcu, M., Pelillo, M., Zhang, L.: Dota: A large-scale dataset for object detection in aerial images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3974–3983 (2018)
2018
Earlier work this paper cites.
Demir, I., Koperski, K., Lindenbaum, D., Pang, G., Huang, J., Basu, S., Hughes, F., Tuia, D., Raskar, R.: Deepglobe 2018: A challenge to parse the earth through satellite images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, pp. 172–181 (2018)
2018
Earlier work this paper cites.
Xiao, T., Liu, Y., Zhou, B., Jiang, Y., Sun, J.: Unified perceptual parsing for scene understanding. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 418–434 (2018)
2018
Earlier work this paper cites.
Ganz, S., Käber, Y., Adler, P.: Measuring tree height with remote sensing—a comparison of photogrammetric and lidar data with different field measurements. Forests 10
2019
Earlier work this paper cites.
Neuenschwander, A., Pitts, K.: The atl08 land and vegetation product for the icesat-2 mission. Remote sensing of environment 221
2019
Earlier work this paper cites.
Waqas Zamir, S., Arora, A., Gupta, A., Khan, S., Sun, G., Shahbaz Khan, F., Zhu, F., Shao, L., Xia, G.-S., Bai, X.: isaid: A large-scale dataset for instance segmentation in aerial images. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, pp. 28–37 (2019)
2019
Earlier work this paper cites.
Li, Y., Li, M., Li, C., Liu, Z.: Forest aboveground biomass estimation using landsat 8 and sentinel-1a data with machine learning algorithms. Scientific reports 10
2020
Earlier work this paper cites.
Santi, E., Paloscia, S., Pettinato, S., Cuozzo, G., Padovano, A., Notarnicola, C., Albinet, C.: Machine-learning applications for the retrieval of forest biomass from airborne p-band sar data. Remote Sensing 12
2020
Cited alongside, same era.
2020
Cited alongside, same era.
Contributors, M.: MMSegmentation: OpenMMLab Semantic Segmentation Toolbox and Benchmark. https://github.com/open-mmlab/mmsegmentation (2020)
2020
Cited alongside, same era.
Radford, A., Kim, J.W., Hallacy, C., Ramesh, A., Goh, G., Agarwal, S., Sastry, G., Askell, A., Mishkin, P., Clark, J., et al
2021
Cited alongside, same era.
He, K., Chen, X., Xie, S., Li, Y., Dollár, P., Girshick, R.: Masked autoencoders are scalable vision learners. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 16000–16009 (2022)
2022
Later among the works it cites.
2022
Later among the works it cites.
Cheng, B., Misra, I., Schwing, A.G., Kirillov, A., Girdhar, R.: Masked-attention mask transformer for universal image segmentation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 1290–1299 (2022)
2022
Later among the works it cites.
Xie, Z., Zhang, Z., Cao, Y., Lin, Y., Bao, J., Yao, Z., Dai, Q., Hu, H.: Simmim: A simple framework for masked image modeling. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9653–9663 (2022)
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Liu, Z., Lin, Y., Cao, Y., Hu, H., Wei, Y., Zhang, Z., Lin, S., Guo, B.: Swin transformer: Hierarchical vision transformer using shifted windows. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 10012–10022 (2021)
2021
Cited alongside, same era.
Manas, O., Lacoste, A., Giró-i-Nieto, X., Vazquez, D., Rodriguez, P.: Seasonal contrast: Unsupervised pre-training from uncurated remote sensing data. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9414–9423 (2021)
2021
Cited alongside, same era.
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
Cited alongside, same era.
Lang, N., Kalischek, N., Armston, J., Schindler, K., Dubayah, R., Wegner, J.D.: Global canopy height regression and uncertainty estimation from gedi lidar waveforms with deep ensembles. Remote sensing of environment 268
2022
Cited alongside, same era.
Ehlers, D., Wang, C., Coulston, J., Zhang, Y., Pavelsky, T., Frankenberg, E., Woodcock, C., Song, C.: Mapping forest aboveground biomass using multisource remotely sensed data. Remote Sensing 14
2022
Cited alongside, same era.
Nathaniel, J., Klein, L.J., Watson, C.D., Nyirjesy, G., Albrecht, C.M.: Aboveground carbon biomass estimate with physics-informed deep network (2022) https://doi.org/10.48550/arXiv.2210.13752
2022
Cited alongside, same era.
2022
Later among the works it cites.
Tian, L., Wu, X., Tao, Y., Li, M., Qian, C., Liao, L., Fu, W.: Review of remote sensing-based methods for forest aboveground biomass estimation: Progress, challenges, and prospects. Forests 14
2023
Later among the works it cites.
Ahmad, N., Ullah, S., Zhao, N., Mumtaz, F., Ali, A., Ali, A., Tariq, A., Kareem, M., Imran, A.B., Khan, I.A., et al
2023
Later among the works it cites.
Pascarella, A.E., Giacco, G., Rigiroli, M., Marrone, S., Sansone, C.: Reuse: Regressive unet for carbon storage and above-ground biomass estimation. Journal of Imaging 9
2023
Later among the works it cites.
2023
Later among the works it cites.
Zhang, X., Han, L.: A generic self-supervised learning (ssl) framework for representation learning from spectral–spatial features of unlabeled remote sensing imagery. Remote Sensing 15
2023
Later among the works it cites.
Prexl, J., Schmitt, M.: Multi-modal multi-objective contrastive learning for sentinel-1/2 imagery. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, pp. 2136–2144 (2023)
2023
Later among the works it cites.