SPASS: Scientific Prominence Active Search System with Deep Image Captioning Network

TL;DR

This paper introduces SPASS, a system enabling planetary rovers to automatically search for scientifically significant features in images using deep captioning and natural language queries, enhancing automation and data retrieval efficiency.

Contribution

The paper presents a novel active search system combining deep image captioning with natural language queries for planetary exploration, improving automation in image prioritization.

Findings

Effective captioning of Mars images demonstrated

Natural language search improves image selection

System can be deployed on ground-based data systems

Abstract

Planetary exploration missions with Mars rovers are complicated, which generally require elaborated task planning by human experts, from the path to take to the images to capture. NASA has been using this process to acquire over 22 million images from the planet Mars. In order to improve the degree of automation and thus efficiency in this process, we propose a system for planetary rovers to actively search for prominence of prespecified scientific features in captured images. Scientists can prespecify such search tasks in natural language and upload them to a rover, on which the deployed system constantly captions captured images with a deep image captioning network and compare the auto-generated captions to the prespecified search tasks by certain metrics so as to prioritize those images for transmission. As a beneficial side effect, the proposed system can also be deployed to ground-based planetary data systems as a content-based search engine.