Landscape patterns and building functions are successfully used to provide the social sensing information of urban areas. However, previous studies treated ground objects equally, ignoring their size differences. Considering the different contributions of various types of ground objects in land-use classification, this paper measured nine area-weighted mean landscape-level metrics to describe landscape patterns based on the land-cover map, derived from remote sensing images. Additionally, the same idea was applied for identifying building functions. Impervious surfaces, which occupy the majority of urban areas, have a decisive impact on land-use classes. In terms of this, this paper proposed the impervious surface area-weighted building-based indexes from the building outline data. To better represent the physical structure of urban areas, the entire study was based on the analysis units delineated by the OpenStreetMap road network. Finally, a random forest model combining the landscape-level metrics and building-based indexes was adopted in Wuchang District of Wuhan city, China. The results showed that the proposed method was effective at describing landscape patterns and identifying building functions for accurate urban land-use classification, increasing the precision by 10.67%. In general, the contribution of landscape-level metrics to the urban land-use classification is slightly greater than that of building-based indexes. Moreover, different land-use types of analysis units express different landscape patterns. It is of great significance for improving urban form and guiding future urban design. The paper demonstrates that area-weighted landscape metrics and building-based indexes offer a better understanding of urban land use, which plays a vital role in urban planning, construction, and management.

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