In this paper, we introduce a new large-scale change detection dataset called LIM-CD, designed for training and evaluating change detection algorithms on high resolution remote sensing images. The dataset currently consists of 9,259 images with labels covering six construction land use change types (i.e., residential land, industrial land, commercial land, public facilities, transportation land, and special land). The image annotations contain not only newly added regions of construction land as change annotations but also auxiliary information about construction land present in pre-change image (image T1), which serves as secondary annotations. These annotations offer crucial information for incremental monitoring applications. The remote sensing images are carefully selected to cover a broad range of imaging variations, including different image sources, years, backgrounds, and terrain. Additionally, we have provided comprehensive metadata labels, which can serve as additional features to aid model training and optimization. To establish a baseline for future algorithm development, we applied seven widely used and state-of-the-art change detection algorithms to the LIM-CD dataset. We are confident that our dataset can serve as a valuable resource for the research community, enabling the development of more accurate and robust change detection models. More information about the project can be found at <code>https://github.com/xiaoxiangAQ/LIM-CD-dataset</code>.