通过对地观测可在影像空间上全面反映地表地理现象、格局及演化过程，基于遥感数据提取的土地利用与覆盖变化（LUCC）产品为分析地理要素空间分布规律及其变化机制提供全覆盖、定量化和快速更新的本底信息。本文以高分辨率遥感视觉特征（图）与多源多模态观测机理特征（谱）相互耦合的图谱认知理论研究为基础，提出了精细地理图斑空间结构（精）上融合定量指标反演模型（准）的精准LUCC简称P-LUCC（Precision LUCC）的概念，并依据“五土合一”的地理学思想构建了分层感知、时空协同与多粒度决策于一体的P-LUCC遥感信息智能提取与综合地理应用方法，提出并解析了其中视觉感知的深度学习、外部知识逐步融入的迁移学习以及增量式的自组织强化学习等3类机器学习模型的协同计算机制。基于此，进一步设计了“分区控制—分层提取—分级迁移—功能重组”为途径的高分辨率遥感P-LUCC信息产品生产线系统，并以苏州市高新区为应用区域针对土地利用（LU）图斑的形态、类型以及土地覆盖变化（LCC）的指标等内容开展了P-LUCC产品精度、生产效率的综合分析，展望了基于P-LUCC信息产品开展专题应用研究的新思路。

Through the earth observation, the geographical phenomena, patterns, and evolutionary processes on the earth’s surface can be fully reflected by the earth observation of remotely sensed imageries. The Land Use/Cover Change (LUCC) products from the High-resolution Remote Sensing (HSRS) data can provide full coverage, quantitative, and fast-updating background information for analyzing the law of spatial distribution of geographic features and their changing mechanisms. The development of HSRS is deeply driving the accuracy of LUCC information. At present, from the implementation effect of major survey projects such as national geographic conditions survey and natural resources survey deployed at the national level, the industrial production of LUCC information can be realized through the combination of human-computer interaction, in-house interpretation, and field survey. The development of high-resolution remote sensing is deeply driving the accuracy of LUCC information. How to improve the intelligent productivity of LUCC products through the innovation of theory and technology is an important bottleneck and key challenge.In the previous research, we put forward the basic concept of geo-parcel/geo-object based on the experience and mode of LUCC production in the departments of land investigation, and determined that the high-precision-level LUCC production is a gradually deepening cognitive process from external visual understanding to internal mechanism analysis. Therefore, this paper takes the geo-parcel as the basic unit of the cognition of land information of earth's surface, and further clarifies the meaning and geographical characteristics of LUCC.Based on the above background, we proposed a new concept of Precise LUCC (P-LUCC) in this paper, which integrates (accurate) quantitative index inversion model on the spatial structure of (fine) geo-parcels. First, this concept is a derivative of our developed spatial-spectrum cognitive theory, which is achieved by coupling HSRS visual features (TU-spatial maps) with multi-source and multi-modal observation mechanism features (PU-spectrum). Moreover, based on the geographical idea of “the unity of five land features”, we further proposed a series of intelligent remote sensing information extraction methods for P-LUCC production. They are organized hierarchically in three kinds of models, i.e. stratified perception model, spatiotemporal synergistically inversion model, and multi-granular decision-making model. In this aspect, we analyzed the cooperative computing mechanism of three types of machine learning models, namely deep learning for visual perception, transfer learning of external knowledge integration, and reinforcement learning via incremental self-organizing. Multi-type learning algorithms based on these mechanism are organized and transformed organically by using the route of “zoning partition-stratified extraction-graded transfer-functional reconstruction”. Thus, we designed a P-LUCC product production line for an information system of HSRS intelligent interpretation.Experiments were performed in the Suzhou High-tech Zone, China, and the accuracy and production efficiency of P-LUCC products were analyzed comprehensively. Through this large regional experimental verification, we show that our proposed technology has obvious advantages in the production accuracy and efficiency of LUCC products. Finally, we also provide some new ideas on thematic application based on P-LUCC information products. In conclusion, this intelligent production mode is worthy of popularization and application in engineering natural resources survey.