Harrison Luft's research while affiliated with University College London and other places

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Publication (1)

Figure 1. Graphical depiction of a CNN.
Figure 2. Example of multiclass image segmentation on a complex land...
Figure 5. Graphical depiction of the methodology for SAR change...
Figure 7. Derived ground truth reference image for the CZU Lightning...
+6 Figure 8. U-Net architecture with ResNet50 encoder.
Deep Learning Based Burnt Area Mapping Using Sentinel 1 for the Santa Cruz Mountains Lightning Complex (CZU) and Creek Fires 2020
Article
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  • Oct 2022
The study presented here builds on previous synthetic aperture radar (SAR) burnt area estimation models and presents the first U-Net (a convolutional network architecture for fast and precise segmentation of images) combined with ResNet50 (Residual Networks used as a backbone for many computer vision tasks) encoder architecture used with SAR, Digit...
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Citations

... A visual illustration is given in Figure 1. predictions (Luft et al., 2022). However, in the CNN-LSTM layer, the CNN layer, as the initial layer, reorganizes the data and extracts only some features (Lu et al., 2022). ...
Reference: Climate and environmental data contribute to the prediction of grain commodity prices using deep learning
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