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About Deep Learning On Edge Extracting Field Boundaries
This document proposes using a convolutional neural network to extract field boundaries from satellite images. Specifically, it uses ResUNet-a, a deep learning model, to assign probabilities to each pixel for belonging to a field, being part of a boundary, and its distance to the closest boundary. When combined, these labels can delineate closed field boundaries. The model was able to accurately map fields, boundaries, and individual fields from a single composite satellite image with only marginal decreases in accuracy when using a single-date image. Additionally, the model generalized well across different resolutions, sensors, locations, and times without recalibration, though averaging predictions from multiple images across a season helped cope with temporal variations. The goal is to facilitate automated extraction of
Detailed Information
| Author: | ['Said Ousguine'] |
|---|---|
| Publication Year: | 2021 |
| Pages: | 15 |
| Language: | English |
| Format: | |
| Price: | FREE |
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