Geographic national condition monitoring can comprehensively and dynamically grasp the changes of national information. They can also provide the data for economic and social development. Geographic national condition census generates the vector data according to unified standards, visual interpretation and field verification. The extraction of change information and updating of land cover maps based on geographic national condition census and multi-temporal remote sensing images is the key to geographic national condition monitoring. According to the characteristics of the geographic national condition census and monitoring demand, a change detection framework for geographic national condition monitoring based on multi-temporal remote sensing images is constructed. Multi-temporal image change detection methods and statistical analysis of object entities are proposed. The proposed method realizes the change detection and updating of geographic national condition census with the combination of the previous census outcomes and bi-temporal remote sensing images. The change detection method based on pixel-object combination first extracts the pixel-based change according to the traditional change vector analysis. Taking the vectors of the geographic national census as the statistical unit, this method then calculates the proportion of the changing pixel within the objects to determine whether they have changed and their change intensities. While the change detection method based on statistical analysis of object entities directly considers the geographic national census vectors as the objects for feature extraction and difference image construction. The resulting difference image is then segmented by an automated threshold to achieve the geographic national condition object-based change detection map. According to the change detection results, the pixels in changed areas are segmented by object-based segmentation, and the training samples are selected from the unchanged areas in the previous temporal image and census map to train the classifier model. Finally, the updated geographical condition vectors are achieved based on the combination of the original unchanged pixels and supervised classification results of the changed areas. The geographic national condition census of Jiangyin County and two high-resolution remote sensing images are used in the experiments. The results demonstrate the effectiveness of the proposed method with accurate results and low cost for change detection and geographic national condition information updating, which provides potential means for geographic national condition monitoring.