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Planetary exploration depends heavily on 3D image data to characterize thestatic and dynamic properties of the rock and environment. Analyzing 3D imagesrequires many computations, causing efficiency to suffer lengthy processingtime alongside large energy consumption. High-Performance Computing (HPC)provides apparent efficiency at the expense of energy consumption. However, forremote explorations, the conveyed surveillance and the robotized sensing needfaster data analysis with ultimate accuracy to make real-time decisions. Insuch environments, access to HPC and energy is limited. Therefore, we realizethat reducing the number of computations to optimal and maintaining the desiredaccuracy leads to higher efficiency. This paper demonstrates the semanticsegmentation capability of a probabilistic decision tree algorithm, 3D AdaptedRandom Forest Vision (3DARFV), exceeding deep learning algorithm efficiency atthe utmost accuracy.

3D Adapted Random Forest Vision (3DARFV) for Untangling  Heterogeneous-Fabric Exceeding Deep Learning Semantic Segmentation Efficiency  at the Utmost Accuracy