11 DAILY WACV Saturday MarsLS-Net in RGB colors and grayscale, thermal information, elevation information, and slope statistics,” he tells us. “Our work was to collate all of that into this benchmark dataset.” However, he faced a challenge as these images did not all have the same resolution. “The Viking Mission images have a resolution of 232 m per pixel, but the most current CTX data has a resolution of 5 m per pixel, so it’s a really high resolution,” Abel continues. “We upsampled some of the modalities. We aligned all of those modalities and have this big image with all the landslides visually annotated by an expert. Some visual characteristics are length, width, relative relief, slope statistics, and the slope of the scarp.” The size of the image also proved challenging. He addressed this by breaking it into small batches of 128 x 128 pixels, which can be easily used as input for a deep learning model. In addition to the dataset, Abel has developed the Martian landslide segmentation network (MarsLSNet), a segmentation model specifically designed to be computationally efficient. “Last month, I attended the NeurIPS conference and was talking with someone from NASA JPL,” he recalls. “He told me they’re using traditional computer vision algorithms in their devices because deep learning models are computationally expensive. We wanted to create a segmentation
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