In order to solve the problem of complex extraction caused by large feature dimension of remote sensing data, this paper proposes a dimension compression extraction method of urban building land remote sensing data under BIM Technology. Firstly, the remote sensing data is imported into the BIM model for lightweight processing to obtain the element information required for urban construction land and then analyze the urban construction land data, extract the key elements of BIM Technology through semantic filtering, and use the triangulation method to transform the remote sensing data into the triangulation model that can be processed by GIS model. Finally, the random projection method is used to reduce the dimension and compress the remote sensing data, and the remote sensing data extraction of urban construction land is realized through dictionary learning, vocabulary coding, and feature extraction. The experimental results show that the accuracy of extracting different land use types by this method is more than 99%, while the accuracy of extracting different land use types by depth learning method and PLS method is less than 98.5%. In addition, the signal-to-noise ratio of the image extracted by this method is significantly higher than that by depth learning method and PLS method. Conclusion. This method can effectively compress and extract the urban construction land in the remote sensing data, and the extraction accuracy of remote sensing data is high. It provides a technical basis for the approval of urban construction planning. It has the advantages of simple feature extraction and effective differentiation of ground objects.