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Found 5 papers, 3 papers with code
Very high resolution canopy height maps from RGB imagery using self-supervised vision transformer and convolutional decoder trained on Aerial Lidar
The maps are generated by the extraction of features from a self-supervised model trained on Maxar imagery from 2017 to 2020, and the training of a dense prediction decoder against aerial lidar maps.
A global method to identify trees outside of closed-canopy forests with medium-resolution satellite imagery
Here we present a globally consistent method to identify trees with canopy diameters greater than three meters with medium-resolution optical and radar imagery.
Text mining policy: Classifying forest and landscape restoration policy agenda with neural information retrieval
Dozens of countries have committed to restoring the ecological functionality of 350 million hectares of land by 2030.
Spatio-temporal crop classification of low-resolution satellite imagery with capsule layers and distributed attention
Land use classification of low resolution spatial imagery is one of the most extensively researched fields in remote sensing.
Imbalanced multi-label classification using multi-task learning with extractive summarization
Extractive summarization and imbalanced multi-label classification often require vast amounts of training data to avoid overfitting.
