National geographic conditions refer to the sum of information on the natural and human elements related to the geography of a country, reflecting the status and development changes of the country’s nature, economy, and humanity in space and time sequence. Beijing has carried out the monitoring of single buildings and established a semi-annual dynamic updating mechanism, forming full-coverage information data of single buildings. However, owing to the effect of the single building data acquisition method, there are problems such as low acquisition accuracy, the inaccuracy of the area, the imprecision of calculating the scale of special-shaped buildings, and the incompleteness of the data acquisition, which limit the promotion and application of single building data in many fields. Therefore, in view of the shortcomings in the updating of the single building data, we propose a technical method that can optimize the accuracy and quickly update the single building in the monitoring of national geographic conditions. First, we constructed a graphical correction model, which can achieve the consistency of the structural expression of two maps. Secondly, we combined the spatial similarity and attribute association to construct a spatial change detection and rapid update model, which updates single building data on the basis of multi-source heterogeneous data space and attribute information fusion. Finally, the low-code technique was designed to check and automatically correct various attributes of updated data. This method was successfully applied to the datasets of six districts of Beijing, and the result was compared with the field investigation results to validate the performance of the algorithm.