science_cases:gmap_science_cases:mounds
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===== Short description of the science case ===== | ===== Short description of the science case ===== | ||
- | On Mars, mounds | + | The GMAP Mounds identification science case aims to develop a generalised machine learning pipeline for the localisation |
- | ===== Aim of the science case ===== | + | ===== Details about the data ===== |
- | With the ML tools developed for this sciene case, we want to identify | + | The data is obtained from the Mars Reconaissance Orbiter (MRO) mission. The MRO spacecraft is designed to study the geology and climate of Mars, provide reconnaissance of future landing sites, and relay data from surface missions back to Earth. The data was collected by the High Resolution Imaging Science Experiment, also known as HIRISE. HiRISE is the most powerful camera ever sent to another planet, one of six instruments |
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+ | ===== Description of the machine learning problem and our approach ===== | ||
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+ | The training set consists of two DTMs, one used for training and the other for testing. In the first step, the training DTM is tiled into several smaller fixed sized images. The label masks are created based on the available ground-truth shape files. The images are then scaled to be in range [-1,1]. The training set is then split further into train and validation sets with an 80/20 ratio. The **train set is augmented** in the next step with image manipulations such as flipping, rotation, rescaling | ||
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+ | For the initial **image segmentation task**, a **standard UNet** (Ronneberger et al., 2015) is trained using the training set. A mean IoU (Intersection over Union) | ||
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+ | Due to the limited number | ||
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+ | A simulator of such likes can be used for controlled generation. Another advantage of **latent space learning** is that it can offer benefits in downstream tasks, which is an added advantage for storage and efficient searching. We have developed this simulator and we plan to disseminate the method as a publication in the coming months. | ||
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+ | Results of this science case were presented at the {{: | ||
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+ | **References: | ||
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+ | * Pozzobon, R., et al. (2019), Fluids mobilization in Arabia Terra, Mars: Depth of pressurized reservoir from mounds self-similar clustering, Icarus 321, 938, doi: | ||
+ | * De Toffoli, B., et al. (2019), Surface Expressions of Subsurface Sediment Mobilization Rooted into a Gas Hydrate-Rich Cryosphere on Mars, Scientific Reports 9, 8603, doi: | ||
+ | * Ronneberger, | ||
+ | * Goodfellow, I., et al. (2020), Generative adversarial networks, Communications of the ACM 63, doi: | ||
science_cases/gmap_science_cases/mounds.1606484476.txt.gz · Last modified: 2020/11/27 14:41 by admin