Remote sensing imagery is a fundamental data source in spatial information science and is widely used in earth observation and geospatial applications. The explosive growth of such data poses significant challenges for online management and service, particularly in terms of storage scalability, processing efficiency, and real-time accessibility. To overcome these limitations, we propose DDMS, a distributed data management and service framework for heterogeneous remote sensing data that structures its functionality around three core components: storage, computing, and service. In this framework, a distributed integrated storage model is constructed by integrating file systems with database technologies to support heterogeneous data management, and a parallel computing model is designed to optimize large-scale image processing. To verify the effectiveness of the proposed framework, a prototype system was implemented and evaluated with experiments on representative datasets, covering both optical and InSAR images. Results show that DDMS can flexibly adapt to heterogeneous remote sensing data and storage backends while maintaining efficient data management and stable service performance. Stress tests further confirm its scalability and consistent responsiveness under varying workloads. DDMS provides a practical and extensible solution for large-scale online management and real-time service of remote sensing images. By enhancing modularity, scalability, and service responsiveness, the framework supports both research and practical applications that depend on massive earth observation data.

Amidst burgeoning socioeconomic development, anthropogenic activities have exacerbated soil erosion. This erosion, characterized by its brief duration, high frequency, and considerable environmental degradation, presents a major challenge to ecological systems. Therefore, it is imperative to regulate and remediate erosion–prone, anthropogenically disturbed parcels, with dynamic change detection (CD) playing a crucial role in enhancing management efficiency. Currently, traditional methods for change detection, such as field surveys and visual interpretation, suffer from time inefficiencies, complexity, and high resource consumption. Meanwhile, despite advancements in remote sensing technology that have improved the temporal and spatial resolution of images, the complexity and heterogeneity of terrestrial cover types continue to limit large–scale dynamic monitoring of anthropogenically disturbed soil erosion parcels (ADPSE) using remote sensing techniques. To address this, we propose a novel ISegFormer model, which integrates the SegFormer network with a pseudo–residual multilayer perceptron (PR–MLP), cross–scale boundary constraint module (CSBC), and multiscale feature fusion module (MSFF). The PR–MLP module improves feature extraction by capturing spatial contextual information, while the CSBC module enhances boundary prediction through high– and low–level semantic guidance. The MSFF module fuses multiscale features with attention mechanisms, boosting segmentation precision for diverse change types. Model performance is evaluated using metrics, such as precision, recall, F1–score, intersection over union (IOU), and mean intersection over union (mIOU). The results demonstrate that our improved model performs exceptionally well in dynamic monitoring tasks for ADPSE. Compared to five other models, our model achieved an mIOU of 72.34% and a Macro–F1 score of 83.55% across twelve types of ADPSE changes, surpassing the other models by 1.52–2.48% in mIOU and 2.25–3.64% in Macro–F1 score. This work provides a theoretical and methodological foundation for policy–making in soil and water conservation departments.