Xinyu Zou

Remote sensing time series (RSTS) are essential for monitoring land surface dynamics, yet existing pixel- or object-based methods often treat changes as isolated snapshots, failing to capture continuous spatiotemporal interactions. To address this, this study proposes the geographical process object-based spatiotemporal graph (GPO-STG) framework, which models land-cover changes as continuous geographical process objects (GPOs) connected by spatiotemporal topological relationships (STTRs). We applied this framework to the China Land Cover Dataset (CLCD) for the central arid and semi-arid region of the Ningxia Hui Autonomous Region (1991–2020) and conducted a systematic multilevel analysis. At the node level, degree centrality analysis identified key processes, revealing that grassland growing acts as the high centrality backbone of the regional landscape structure. At the edge level, interaction pattern analysis quantified the relationships between land-cover types and evolutionary states, uncovering a dominant coupling between grassland growing and cropland fluctuating. At the subgraph level, chain pattern extraction traced sequential evolutionary trajectories, confirming a “utilization–recovery” dynamic characterized by a transition from agricultural expansion to ecological restoration. The results demonstrate that the GPO-STG framework effectively characterizes complex land-cover changes that are often missed by pixel- or object-based methods.

Multi-label remote sensing scene classification (MLRSSC) requires autonomous discovery of all relevant land-cover categories without human guidance. Conventional expert classifiers return only label vectors without spatial evidence, while foundation segmenters (e.g., SAM, RemoteSAM) remain passively dependent on external prompts—misaligned with autonomous interpretation. We introduce SAFI-XRS, a parameter-efficient self-prompted framework that transforms passive prompting into active scene parsing. By training only <2% of a 332M-parameter segmenter (∼2.4M parameters), SAFI-XRS generates class-aligned queries from images via a Semantic Query Generator (SQR), replacing external prompts with self-generated conditioning. A Mask-Guided Classifier (MGC) aggregates spatial evidence into label confidences, enabling mask-based explainability. Experiments on UCM-ML, DFC15-ML, and AID-ML show SAFI-XRS surpasses text-prompted foundation segmenters (+3.9/+3.8 mAP on balanced datasets) while achieving 6.8× parameter efficiency compared to expert models, validating a practical path toward autonomous, explainable RS scene understanding.

Earth observation technology has improved the detection of land cover changes. However, current pixel-based change detection methods cannot adequately describe the evolutionary process and spatiotemporal association of geographic entities. Therefore, we developed a method for analyzing the processes and patterns of land cover evolution based on spatiotemporal graphs. First, a spatiotemporal graph was generated from a time series of land cover maps according to the spatial and temporal relationships between land cover objects, as defined by spatial adjacency and temporal transition, respectively. Subsequently, structural characteristics, such as the spatial roles, adjacency type, temporal transitions and evolution trajectories, were derived from the spatiotemporal graph to describe and analyze the evolution of land cover. Finally, this method was applied to analyze land cover evolution in Fujian Province, China, from 2001 to 2019. The proposed method not only completely preserves the spatial adjacency and temporal transition details among land cover objects in a spatiotemporally unified graph framework but also extracts evolution-related spatiotemporal structural characteristics. This study provides a reliable scientific basis for analyzing the consistency of long-term land cover dynamics and has practical value for other geographic applications.