The acquisition of land surface change information is a fundamental pathway for understanding the evolution of the natural environment and the impacts of human activities. Land surface changes reflect the joint influence of natural processes and anthropogenic drivers on landscape pattern evolution. Such changes are not only governed by natural factors but are also closely intertwined with social and economic development. Timely and accurate monitoring and identification of these changes are crucial for gaining deeper insights into regional ecological succession and urban expansion processes，and they provide essential scientific support for territorial spatial planning，ecological and environmental protection，and social governance. Remote sensing change detection is an important research direction in the fields of remote sensing and geographic information science，and it has attracted increasing attention in recent years. The core objective of remote sensing change detection is to identify considerable changes in land-cover elements within the same geographical area across different time points by analyzing bitemporal or multitemporal remote sensing imagery. Most existing studies focus on bitemporal change detection. Although substantial progress has been achieved in terms of detection accuracy and computational efficiency，bitemporal approaches remain inherently limited in their ability to model temporal dynamics. In real-world scenarios，land surface changes often exhibit continuity，stage-wise progression，and multiscale temporal characteristics. Typical examples include the gradual expansion of urban areas，seasonal degradation and recovery of vegetation，and nonlinear postdisaster land surface restoration processes. Relying solely on two temporal snapshots makes it difficult to characterize such long-term evolutionary trends and complex dynamic patterns，and it also limits the ability to uncover latent trends associated with gradual or periodic changes. Moreover，given that only two temporal images are involved，bitemporal approaches are more susceptible to pseudo changes and misclassification when confronted with illumination variations，observation noise，and other acquisition-related inconsistencies. Consequently，exploring multitemporal change detection to better capture the dynamic evolution of land surfaces has become an important research direction. However，from a data perspective，high-resolution，high-quality multitemporal remote sensing change detection datasets that cover diverse land surface change types remain scarce. Most widely used change detection datasets remain limited to bitemporal imagery with corresponding annotations，primarily describing changes between two discrete time points. These datasets are often constrained in terms of the number of temporal phases，spatial coverage，image scale，and temporal span. As a result，they are insufficient for modeling and analyzing long-term，stage-wise land surface evolution processes，particularly in complex application scenarios such as urban expansion and ecological transformation.To address these limitations，this study constructs a high-resolution，multiregional，and multitemporal remote sensing change detection dataset，termed HR-MTCD （high-resolution multitemporal change detection），with the aim of providing fundamental data support for dynamic land surface evolution modeling.

The dataset covers five representative regions in China，including the Yangtze River Delta，the Pearl River Delta，the Beijing-Tianjin-Hebei region，the Chengdu-Chongqing region，and Northwest China. It integrates multiple types of land surface changes related to urban development and ecological evolution. The dataset spans a five-year period，with changes annotated at one-year intervals，and consists of 1 601 change groups and 8 005 remote sensing images with a spatial resolution of approximately 0.5 meters. The change categories primarily focus on prominent land surface changes，such as building construction and demolition，and large-scale vegetation changes while covering typical scenarios related to urban growth and ecological dynamics. This design ensures the dataset’s practical applicability and relevance，providing robust support for research on diverse land surface changes and for the development of multitemporal change detection algorithms.

Systematic experiments conducted using several mainstream change detection models reveal performance differences among models and validate the usability and the inherent challenges of the proposed high-resolution multitemporal dataset in real-world change detection tasks. Within a unified experimental framework，we further design progressive cross-temporal extension experiments. By comparing detection performance as training data from different temporal spans are incrementally incorporated，we evaluate the influence of multitemporal data on a model’s ability to recognize long-term，nonlinear，or gradual land surface changes. Experimental results demonstrate that the temporal dimension information contained in multitemporal remote sensing imagery plays a fundamental role in change detection tasks，offering new perspectives for models to understand dynamic land-cover evolution.

Overall，the construction of the HR-MTCD dataset provides an important data foundation for advancing remote sensing change detection from discrete bitemporal change identification toward comprehensive modeling of multitemporal land surface evolution processes.In future work，the dataset will be continuously expanded to cover a broader range of geographical regions and longer temporal spans to better capture diverse land surface dynamics under different environmental and socioeconomic conditions. In addition，we plan to incorporate multisource remote sensing modalities progressively，including optical imagery，synthetic aperture radar，and LiDAR data，thereby enabling complementary representations of land surface structure，texture，and physical properties. Beyond geometric and radiometric changes，semantic-level change information will also be introduced to describe higher-level land-cover transitions and functional transformations. The dataset is publicly available at https://doi.org/10.57760/sciencedb.j00240.00110.