Future deep space missions will require communication systems with high data rates that are not achievable with current radiofrequency-based systems. NASA’s Deep Space Optical Communications project is a technical demonstration for establishing optical communications for deep space missions at Mars distances. A new report presents an overview of the system and operational results of the ground laser transmitter from a two-year mission launched together with NASA’s Psyche spacecraft.

A new study published in Big Earth Data proposes a large language model (LLM)-based multi-agent framework for remote sensing analysis that integrates data resources, analytical tools, and AI-driven reasoning to assist non-expert users in conducting complex remote sensing tasks. Using the ExpertsRS prototype built on AutoGen and DeepSeek-V3, experiments demonstrate that the system can transform ambiguous queries into expert-level analytical workflows and outperform baseline LLM and single-agent approaches in planning efficiency and result accuracy. The Sentinel-2 dataset used in this study is openly available from the European Space Agency at https://www.esa.int/Applications/Observing_the_Earth/Copernicus/Sentinel-2. The code repository for this work is accessible at https://github.com/zhelunSun/URSA.

A new artificial intelligence (AI) framework offers a more reliable way to restore partially hidden objects in satellite imagery. 

New experimental evidence demonstrates that discrete space-time crystals can be realized in classical soft-matter systems, thereby moving beyond the traditional complexities of quantum mechanics.

A new study published in Big Earth Data describes the development of the National Earth Observation Data Cube of Cyprus as a modern software-based infrastructure for efficiently storing, managing, and analyzing the rapidly growing volume of Earth observation data. It further demonstrates its value through a water resource monitoring case under the Mediterranean climate crisis and highlights the potential of semantic classification for more generalized and efficient environmental monitoring. An online platform has been developed and welcomes users to access the CEODC ecosystem at https://cypruscube.com/, while all tools and services are also integrated within the ERATOSTHENES Regional Exploitation Platform: https://rep.eratosthenes.org.cy/.

A new study published in Big Earth Data proposes GeoJSON agents, a novel multi-agent large language model (LLM) framework for geospatial analysis that transforms natural language instructions into structured GeoJSON operations through function calling and code generation. Experiments on a hierarchical benchmark of 70 spatial tasks show that the code generation–based agent achieved 97.14% accuracy and the function calling–based agent achieved 85.71%, both significantly outperforming general-purpose models, while highlighting the trade-off between flexibility and execution stability in GeoAI applications. The data that support the findings of this study are openly available in figshare at https://doi.org/10.6084/m9.figshare.29921492.