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China Plans to Complete AI-Driven Geologic Map of Mars by 2028

China plans to complete a new-generation geologic map of Mars by 2028, combining data from its Tianwen-1 mission, international space agencies, and AI-driven analysis to redefine the Red Planet’s geological timeline.

China’s plan to map Mars by 2028 represents a significant step in its space exploration ambitions, with the project aiming to create the first AI-driven geological atlas of the planet. The initiative, unveiled by Hou Zengqian, chief scientist of the upcoming Tianwen-3 mission, will integrate findings from the Zhurong rover, launched in 2021, and data from U.S. and European Space Agency (ESA) probes. The final deliverables include a 1:5 million-scale map, a 1:50,000-scale landing area chart, and a series of thematic atlases.

Scope of the Mapping Project

The project will focus on reconstructing Mars’ watery past, with particular attention to evidence of ancient oceans and flood events. Zhurong’s findings, including a 3.5-billion-year-old ocean and 1.6-billion-year-old flooding, will be central to the map. The effort will also create a Chinese framework for dividing Martian geologic time, challenging the current Noachian, Hesperian, and Amazonian periods, which rely on crater density rather than absolute dates. This new system, according to Hou, aims to become an international standard.

China Plans to Complete AI-Driven Geologic Map of Mars by 2028
Photo: CCTV

The map will leverage data from multiple sources, including orbital and ground-based observations from Tianwen-1, U.S. and ESA probes, and advanced technologies like radar, gravity, and magnetic readings. By fusing these datasets, the project will build a comprehensive database of Mars’ physical fields, enabling a more nuanced understanding of its geology. The integration of AI will allow geologists to embed expertise into large models, ensuring explainable results while identifying mineral patterns and tectonic units.

Technological Innovations

Artificial intelligence will play a pivotal role in interpreting Mars’ complex geology. Geologists’ knowledge will be encoded into AI systems via rule engines, ensuring transparency in the mapping process. Multi-scale attention techniques will enable feature extraction, from local mineral identification to global tectonic divisions. This approach not only enhances accuracy but also sets a precedent for future planetary mapping missions, including those targeting the Moon, Venus, and asteroids.

China to complete new global geological map of Mars by 2028

The project’s timeline is tightly structured, with key milestones including the release of the 1:5 million-scale map and the first intelligent geologic atlas of Mars by 2028. These outputs will serve as critical preparation for the Tianwen-3 sample-return mission, scheduled for around 2028. The mission’s top scientific objective is to search for potential signs of life on Mars, with the map providing essential context for selecting landing sites and analyzing geological samples.

International Collaboration and Scientific Impact

China’s efforts reflect a broader trend of international collaboration in planetary science. While the project primarily uses data from Tianwen-1, it also incorporates U.S. and ESA probe findings, highlighting the interconnected nature of modern space exploration. The initiative’s emphasis on AI and data fusion could influence future missions, offering a template for integrating diverse datasets into a cohesive scientific framework.

China Plans to Complete AI-Driven Geologic Map of Mars by 2028
Photo: global.chinadaily.com.cn

The Institute of Geology, Chinese Academy of Geological Sciences, has led geologic mapping for 70 years, expanding from regional charts to lunar and Martian studies. Director Yang Zhiming emphasized that the Mars map will provide the scientific foundation for China’s planetary program, guiding decisions on where to go, what to explore and why. This work underscores the growing role of AI and data science in reshaping our understanding of planetary geology.