Search Results for author:
Found 5 papers, 4 papers with code
Deep symbolic regression for physics guided by units constraints: toward the automated discovery of physical laws
Here we present $\Phi$-SO, a Physical Symbolic Optimization framework for recovering analytical symbolic expressions from physics data using deep reinforcement learning techniques by learning units constraints.
Detect, consolidate, delineate: scalable mapping of field boundaries using satellite images
Thus, knowing the exact location of fields and their boundaries is a prerequisite.
Looking for change? Roll the Dice and demand Attention
Further, we introduce a new encoder/decoder scheme, a network macro-topology, that is tailored for the task of change detection.
Building change detection for remote sensing images
Change Detection
+1
Deep learning on edge: extracting field boundaries from satellite images with a convolutional neural network
By minimising image preprocessing requirements and replacing local arbitrary decisions by data-driven ones, our approach is expected to facilitate the extraction of individual crop fields at scale.
ResUNet-a: a deep learning framework for semantic segmentation of remotely sensed data
Scene understanding of high resolution aerial images is of great importance for the task of automated monitoring in various remote sensing applications.
