DrCyZ: Techniques for analyzing and extracting useful information from CyZ. Samples from NASA Perseverance and set of GAN generated synthetic images from Neural Mars. Repository: https://github.com/decurtoidiaz/drcyz Subset of samples from (includes tools to visualize and analyse the dataset): CyZ: MARS Space Exploration Dataset. [https://doi.org/10.5281/zenodo.5655473] Images from NASA missions of the celestial body. Repository: https://github.com/decurtoidiaz/cyz Authors: J. de Curtò c@decurto.be I. de Zarzà z@dezarza.be ------------------------------------------ File Information from DrCyZ-1.1 ------------------------------------------ • Subset of samples from Perseverance (drcyz/c). ∙ png (drcyz/c/png). PNG files (5025) selected from NASA Perseverance (CyZ-1.1) after t-SNE and K-means Clustering. ∙ csv (drcyz/c/csv). CSV file. • Resized samples from Perseverance (drcyz/c+). ∙ png 64x64; 128x128; 256x256; 512x512; 1024x1024 (drcyz/c+/drcyz_64-1024). PNG files resized at the corresponding size. ∙ TFRecords 64x64; 128x128; 256x256; 512x512; 1024x1024 (drcyz/c+/tfr_drcyz_64-1024). TFRecord resized at the corresponding size to import on Tensorflow. • Synthetic images from Neural Mars generated using Stylegan2-ada (drcyz/drcyz+). ∙ png 100; 1000; 10000 (drcyz/drcyz+/drcyz_256_100-10000) PNG files subset of 100, 1000 and 10000 at size 256x256. • Network Checkpoint from Stylegan2-ada trained at size 256x256 (drcyz/model_drcyz). ∙ network-snapshot-000798-drcyz.pkl • Notebooks in python to analyse the original dataset and reproduce the experiments; K-means Clustering, t-SNE, PCA, synthetic generation using Stylegan2-ada and instance segmentation using Deeplab (https://github.com/decurtoidiaz/drcyz/tree/main/dr_cyz+). ∙ clustering_curiosity_de_curto_and_de_zarza.ipynb K-means Clustering and PCA(2) with images from Curiosity. ∙ clustering_perseverance_de_curto_and_de_zarza.ipynb K-means Clustering and PCA(2) with images from Perseverance. ∙ tsne_curiosity_de_curto_and_de_zarza.ipynb t-SNE and PCA (components selected to explain 99% of variance) with images from Curiosity. ∙ tsne_perseverance_de_curto_and_de_zarza.ipynb t-SNE and PCA (components selected to explain 99% of variance) with images from Perseverance. ∙ Stylegan2-ada_de_curto_and_de_zarza.ipynb Stylegan2-ada trained on a subset of images from NASA Perseverance (DrCyZ). ∙ statistics_perseverance_de_curto_and_de_zarza.ipynb Compute statistics from synthetic samples generated by Stylegan2-ada (DrCyZ) and images from NASA Perseverance (CyZ). ∙ DeepLab_TFLite_ADE20k_de_curto_and_de_zarza.ipynb Example of instance segmentation using Deeplab with a sample from NASA Perseverance (DrCyZ).

{"references": ["de Curt\u00f2, J., & de Zarz\u00e0, I. (2021). CyZ: MARS Space Exploration Dataset. (1.1) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.5655473"]}