A research team led by Prof. CAI Xinxia, Assoc. Prof. LUO Jinping and Assoc. Prof. WANG Mixia from the Aerospace Information Research Institute of the Chinese Academy of Sciences (AIRCAS) has demonstrated that in vitro biological neuronal networks can achieve both low-power consumption and high-speed communication through predictable electrical stimulation, providing a promising new route for next-generation brain-inspired computing systems. The study shows that regular stimulation increased neural communication speed by about 1.79 times without adding extra energy cost.

A UNESCO report released on 21 April 2026, People and Nature in UNESCO-designated Sites: Global and Local Contributions, delivers the first global assessment of over 2,260 World Heritage sites, Biosphere Reserves and Global Geoparks. The study finds that more than one in four sites could reach critical ecological tipping points by 2050 if pressures intensify, while also showing that monitored wildlife populations within these sites have remained stable on average, contrasting sharply with a 73% global decline since 1970.

Researchers from the Aerospace Information Research Institute under the Chinese Academy of Sciences (AIRCAS) have developed a knowledge-guided artificial intelligence algorithm that significantly improves the resolution of global soil moisture observation.

A research team from the Aerospace Information Research Institute of the Chinese Academy of Sciences (AIRCAS), in collaboration with Changsha University of Science and Technology, and Tsinghua University, has proposed a systematic and unified framework that integrates Light Detection and Ranging (LiDAR) remote sensing with Weakly Supervised Learning (WSL). The study provides a comprehensive review that bridges the traditional gap between LiDAR data interpretation and large-scale parameter inversion, offering scalable solutions to reduce the high costs of manual data annotation in Earth observation.

A recent study published in the ISPRS Journal of Photogrammetry and Remote Sensing​, describes a physics-based framework that maps global subsurface soil temperature profiles by harnessing the multi-overpass capabilities of China's Fengyun (FY) meteorological satellites. This work was led by Prof. ZHAO Tianjie from the Laboratory of Earth Observation for Water at the Aerospace Information Research Institute of the Chinese Academy of Sciences (AIRCAS).

A research team led by Prof. WANG Shudong at the State Key Laboratory of Remote Sensing and Digital Earth, Aerospace Information Research Institute, Chinese Academy of Sciences (AIRCAS), has made significant progress in reconstructing long-term global surface soil moisture variations and improving future projections through observation-constrained climate modeling. The study, Quantifying Historical and Future Surface Soil Moisture Drying Using Deep Learning and Remote Sensing, was recently published in Earth's Future .