With the rapid development of the social economy, digitization, informatization, and intelligence have become important trends in promoting national construction. In the era of big data, the essence of intelligent applications in various industries is to correlate ubiquitous information and solve required parameters in their respective spaces. How to intelligently analyze large-scale spatial parameters on the complex land surface, which is dependent on natural resources, has become an important proposition for digital driven high-quality development in the new era. As a new trend in the development of Artificial Intelligence (AI), the revolutionary influence of Large Models (LMs) on scientific research paradigms, production methods, and industrial models cannot be underestimated. Investing in LM research is an inevitable choice. In the field of geographic AI, a significant gap remains between the scientific design and practical application of LMs. This article adheres to the principle of deconstructing complex land surface systems and solving precise land parameters. It proposes to conduct land spatial object-oriented modeling supported by multisource and multimodal observation data. The article proposes an object-oriented modeling approach for land surface space, integrating basic geographic data to build an object-oriented base, and transmitting remote sensing data to collaborators’ knowledge in a signal manner to systematically analyze complex land spaces. On this basis, a land spatial parameter system and a solution framework are outlined via the integration of five land parameters from land use, land cover change, land soil, land resource, and land type/application. Furthermore, an intelligent computing remote sensing LM is designed for large-scale parameter solving via integrating three core systems, namely, symbol, perception, and control systems. This model deploys heterogeneous deep learning algorithms to break through the bottlenecks in mapping, transforming, and transmitting relationships on key nodes. It is worth emphasizing that in deep learning algorithms, we introduce attention and external incremental information to solve the ordered decomposition and step-by-step simplification of complex problems, thereby achieving large-scale, accurate, and fast solution of land parameters. A preliminary experiment is conducted using the solution of land use parameters in agricultural production spaces as an application case. Results show that the proposed framework has great potential in improving the accuracy of large-scale parameter calculation in land space. The experiment has shown that the remote sensing model constructed in this article has good performance, revealing that the land spatial information generated by this research model has five advantages of measurability, detectability, verifiability, optimizability, and customizability, and has broad application potential in the comprehensive service of human, land, money, and matter. The proposed model helps serve the intelligent customization of refined land information products and deepen the understanding of land space. Finally, prospects for LM research on land spatial parameter calculation are presented from the perspectives of model adaptability/robustness and interpretability/credibility of results. This study is based on the existing research of the authors’ team and presents the spiral evolution from remote sensing regression to geography, from big data to big model research in recent years. It is another milestone in theoretical development and practical application. It should be noted that the LM framework established in this article is more of an intelligent computing strategy proposed for solving large-scale land parameter problems in complex geographic systems, and there is still room for optimization and adjustment in specific implementation stages.

Большие модели; геоинформационный интеллект; объектная модель земельного пространства; решение параметров земли; механизм внимания; глубокие сети обучения; пространство сельскохозяйственного производства