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Tree stress estimation with deep learning (assigned)
A healthy canopy prevents soil erosion, slows rainwater runoff, and is key to clean and ample water supply. However, the amount of trees, their stress level (e.g., defoliation), species, biomass, and age are often unknown because no up-to-date database exists due to the high cost of in-situ surveys.
Keywords: Satellite images, deep machine learning
A healthy canopy prevents soil erosion, slows rainwater runoff, and is key to clean and ample water supply. However, the amount of trees, their stress level (e.g., defoliation), species, biomass, and age are often unknown because no up-to-date database exists due to the high cost of in-situ surveys.
With this project, a collaboration between ETH and WSL, we aim at developing an automated, image-based system to track changes of trees' states (e.g., stress level, pests, re-planting events) over time at country-scale. The long-term objective is to measure the impact of climate change on Swiss trees.
The system centers on state-of-the-art supervised deep convolutional neural networks where sparse very high-resolution ground-level images from in-situ surveys are combined with lower resolution aerial and satellite images that cover entire Switzerland. The general idea is to use sparse, but very accurate tree measurements acquired by WSL for training deep machine learning approaches that can then predict tree stress at any other location in Switzerland using satellite images of the Sentinel 2 satellites.
A healthy canopy prevents soil erosion, slows rainwater runoff, and is key to clean and ample water supply. However, the amount of trees, their stress level (e.g., defoliation), species, biomass, and age are often unknown because no up-to-date database exists due to the high cost of in-situ surveys. With this project, a collaboration between ETH and WSL, we aim at developing an automated, image-based system to track changes of trees' states (e.g., stress level, pests, re-planting events) over time at country-scale. The long-term objective is to measure the impact of climate change on Swiss trees. The system centers on state-of-the-art supervised deep convolutional neural networks where sparse very high-resolution ground-level images from in-situ surveys are combined with lower resolution aerial and satellite images that cover entire Switzerland. The general idea is to use sparse, but very accurate tree measurements acquired by WSL for training deep machine learning approaches that can then predict tree stress at any other location in Switzerland using satellite images of the Sentinel 2 satellites.
With this project, a collaboration between ETH and WSL, we aim at developing an automated, image-based system to track changes of trees' states (e.g., stress level, pests, re-planting events) over time at country-scale. The long-term objective is to measure the impact of climate change on Swiss trees.
With this project, a collaboration between ETH and WSL, we aim at developing an automated, image-based system to track changes of trees' states (e.g., stress level, pests, re-planting events) over time at country-scale. The long-term objective is to measure the impact of climate change on Swiss trees.
Dr. Jan Dirk Wegner (jan.wegner@geod.baug.ethz.ch)
Dr. Jan Dirk Wegner (jan.wegner@geod.baug.ethz.ch)