Data and <strong>coding</strong> used in paper entitled <strong>"MIU: Deep Embedded Building Cluster Model of Urban Functional Zoning"</strong>

Data and coding used in paper entitled "MIU: Deep Embedded Building Cluster Model of Urban Functional Zoning".The compressed package contains 6 folders. Building Footprint: Building vector data were used to extract geometric and compactness featrues. Google Earth Image: VHR images were applied to extract spectral and textural features. Luojia 1-01 Nighttime Light Image: Nighttime data were used to extract brightness features. OSM Street:OSM road networks were used to extract location features. POI of Study Area:POI data were used to generate labels for training the Word2Vec model. Python Code:DEC code was used to process the cluster for generating the MIU; Word2Vec code was used train the Word2Vec model.