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Forests are vital to ecosystems, supporting biodiversity and essential services, but are rapidly changing due to land use and climate change.
Forests and Climate Change: Forcings, Feedbacks, and the Climate Benefits of Forests
Bonan, G. B. 2008 · 2008
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An image is worth 16x16 words: Transformers for image recognition at scale
Dosovitskiy, A.; Beyer, L.; Kolesnikov, A.; et al. 2021 · 2010
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MODIS Collection 5 global land cover: Algorithm refinements and characterization of new datasets
Friedl, M.; Sulla-Menashe, D.; Tan, B.; et al. 2010 · 2010
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Irvin, J.; Sheng, H.; Ramachandran, N.; et al. 2020 · 2011
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U-net: Convolutional networks for biomedical image segmentation
Ronneberger, O.; Fischer, P.; and Brox, T. 2015 · 2015
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Review of studies on tree species classification from remotely sensed data
Fassnacht, F.; Latifi, H.; Stereńczak, K.; et al. 2016 · 2016
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Deep residual learning for image recognition
He, K.; Zhang, X.; Ren, S.; and Sun, J. 2016 · 2016
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USAT: A unified score-based association test for multiple phenotype-genotype analysis
Ray, D.; Pankow, J. S.; and Basu, S. 2016 · 2016
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Faster R-CNN: Towards real-time object detection with region proposal networks
Ren, S.; He, K.; Girshick, R.; and Sun, J. 2016 · 2016
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Rethinking atrous convolution for semantic image segmentation
Chen, L.-C.; Papandreou, G.; Schroff, F.; and Adam, H. 2017 · 2017
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Annual continuous fields of woody vegetation structure in the Lower Mekong region from 2000-2017 Landsat time-series
Potapov, P.; Tyukavina, A.; Turubanova, S.; et al. 2019 · 2017
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Functional map of the world
Christie, G.; Fendley, N.; Wilson, J.; and Mukherjee, R. 2018 · 2018
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Classifying drivers of global forest loss
Curtis, P. G.; Slay, C. M.; Harris, N. L.; Tyukavina, A.; and Hansen, M. C. 2018 · 2018
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CORINE Land Cover 2018 (vector), Europe, 6-yearly
EEA. 2020 · 2018
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Unified perceptual parsing for scene understanding
Xiao, T.; Liu, Y.; Zhou, B.; Jiang, Y.; and Sun, J. 2018 · 2018
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Advances in the Derivation of Northeast Siberian Forest Metrics Using High-Resolution UAV-Based Photogrammetric Point Clouds
Brieger, F.; Herzschuh, U.; Pestryakova, L.; et al. 2019 · 2019
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UAV data as alternative to field sampling to map woody invasive species based on combined Sentinel-1 and Sentinel-2 data
Kattenborn, T.; Lopatin, J.; Förster, M.; et al. 2019 · 2019
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SEN12MS–A curated dataset of georeferenced multi-spectral sentinel-1/2 imagery for deep learning and data fusion
Schmitt, M.; Hughes, L.; Qiu, C.; and Zhu, X. 2019 · 2019
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Global Forest Resources Assessment 2020
FAO. 2020 · 2020
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Forestry remote sensing from unmanned aerial vehicles: A review focusing on the data, processing and potentialities
Guimarães, N.; Pádua, L.; Marques, P.; et al. 2020 · 2020
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Convolutional Neural Networks accurately predict cover fractions of plant species and communities in Unmanned Aerial Vehicle imagery
Kattenborn, T.; Eichel, J.; Wiser, S.; et al. 2020 · 2020
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Focal Loss for Dense Object Detection
Lin, T.-Y.; Goyal, P.; Girshick, R.; et al. 2020 · 2020
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MMSegmentation: OpenMMLab Semantic Segmentation Toolbox and Benchmark
MMSegmentation Contributors. 2020 · 2020
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You Only Look at One Sequence: Rethinking Transformer in Vision through Object Detection
Fang, Y.; Liao, B.; Wang, X.; et al. 2021 · 2021
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Perceiver: General Perception with Iterative Attention
Jaegle, A.; Gimeno, F.; Brock, 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.; and Hinz, S. 2021 · 2021
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Toward foundation models for earth monitoring: Proposal for a climate change benchmark
Lacoste, A.; Sherwin, E.; Kerner, H.; et al. 2021 · 2021
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A review of tree species classification based on airborne LiDAR data and applied classifiers
Michałowska, M.; and Rapiński, J. 2021 · 2021
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BigEarthNet-MM: A large-scale, multimodal, multilabel benchmark archive for remote sensing image classification and retrieval [software and data sets]
Sumbul, G.; De Wall, A.; Kreuziger, T.; et al. 2021 · 2021
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TreeSatAI Benchmark Archive
Ahlswede, S.; Schulz, C.; Gava, C.; et al. 2023 · 2023
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CROMA: Remote Sensing Representations with Contrastive Radar-Optical Masked Autoencoders
Fuller, A.; Millard, K.; and Green, J. R. 2023 · 2023
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Garioud, A.; De Wit, A.; Poupée, M.; et al. 2023 · 2023
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Segment anything
Kirillov, A.; Mintun, E.; Ravi, N.; et al. 2023 · 2023
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AI Foundation Models for Weather and Climate: Applications, Design, and Implementation
Mukkavilli, S.; Civitarese, D.; Schmude, J.; et al. 2023 · 2023
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Training data-efficient image transformers & distillation through attention
Touvron, H.; Cord, M.; Douze, M.; et al. 2021 · 2021
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Cropharvest: A global dataset for crop-type classification
Tseng, G.; Zvonkov, I.; Nakalembe, C.; and Kerner, H. 2021 · 2021
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SiDroForest: A comprehensive forest inventory of Siberian boreal forest investigations including drone-based point clouds, individually labelled trees, synthetically generated tree crowns and Sentinel-2 labelled image patches
van Geffen, F.; Heim, B.; Brieger, F.; et al. 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.; Graves, S.; Marconi, S.; et al. 2021 · 2021
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Sustainbench: Benchmarks for monitoring the sustainable development goals with machine learning
Yeh, C.; Meng, C.; Wang, S.; et al. 2021 · 2021
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Point Transformer
Zhao, H.; Jiang, L.; Jia, J.; et al. 2021 · 2021
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Rapidai4Eo: Mono-and Multi-Temporal Deep Learning Models for Updating the Corine land Cover Product
Bhugra, P.; Bischke, B.; Werner, C.; et al. 2022 · 2022
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Papoutsis, I.; Bountos, N.; Zavras, A.; et al. 2023 · 2023
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Puliti, S.; Pearse, G.; Surový, P.; et al. 2023 · 2023
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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.; Keutzer, K.; Candido, S.; Uyttendaele, M.; and Darrell, T. 2023 · 2023
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Cross-Scale MAE: A Tale of Multiscale Exploitation in Remote Sensing
Tang, M.; Cozma, A. L.; Georgiou, K.; and Qi, H. 2023 · 2023
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Enabling country-scale land cover mapping with meter-resolution satellite imagery
Tong, X.-Y.; Xia, G.-S.; and Zhu, X.-X. 2023 · 2023
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Lightweight, Pre-trained Transformers for Remote Sensing Timeseries
Tseng, G.; Zvonkov, I.; Purohit, M.; Rolnick, D.; and Kerner, H. 2023 · 2023
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Spectralmae: Spectral masked autoencoder for hyperspectral remote sensing image reconstruction
Zhu, L.; Wu, J.; Biao, W.; Liao, Y.; and Gu, D. 2023 · 2023
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OmniSat: Self-supervised Modality Fusion for Earth Observation
Astruc, G.; Gonthier, N.; Mallet, C.; et al. 2025 · 2024
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Bountos, N. I.; Sdraka, M.; Zavras, A.; Karasante, I.; Karavias, A.; Herekakis, T.; Thanasou, A.; Michail, D.; and Papoutsis, I. 2024 · 2024
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FLAIR: a Country-Scale Land Cover Semantic Segmentation Dataset From Multi-Source Optical Imagery
Garioud, A.; Gonthier, N.; Landrieu, L.; et al. 2024 · 2024
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Skysense: A multi-modal remote sensing foundation model towards universal interpretation for earth observation imagery
Guo, X.; Lao, J.; Dang, B.; et al. 2024 · 2024
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SpectralGPT: Spectral remote sensing foundation model
Hong, D.; Zhang, B.; Li, X.; et al. 2024 · 2024
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GEO-Bench: Toward Foundation Models for Earth Monitoring
Lacoste, A.; Lehmann, N.; Rodriguez, P.; et al. 2024 · 2024
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DINOv2: Learning Robust Visual Features without Supervision
Oquab, m.; Darcet, T.; Moutakanni, T.; et al. 2024 · 2024
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OpenForest: A data catalogue for machine learning in forest monitoring
Ouaknine, A.; Kattenborn, T.; Laliberté, E.; and Rolnick, D. 2024 · 2024
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Mission Critical – Satellite Data is a Distinct Modality in Machine Learning
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Ssl4eo-l: Datasets and foundation models for landsat imagery
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Neural Plasticity-Inspired Multimodal Foundation Model for Earth Observation
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The ERA5 global reanalysis
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