  Timely and accurate land use mapping is a long-standing problem, which is
critical for effective land and space planning and management. Due to complex
and mixed use, it is challenging for accurate land use mapping from widely-used
remote sensing images (RSI) directly, especially for high-density cities. To
address this issue, in this paper, we propose a coarse-to-fine machine
learning-based approach for parcel-level urban land use mapping, integrating
multisource geospatial data, including RSI, points-of-interest (POI), and
area-of-interest (AOI) data. Specifically, we first divide the city into
built-up and non-built-up regions based on parcels generated from road
networks. Then, we adopt different classification strategies for parcels in
different regions, and finally combine the classified results into an
integrated land use map. The results show that the proposed approach can
significantly outperform baseline method that mixes built-up and non-built-up
regions, with accuracy increase of 25% and 30% for level-1 and level-2
classification, respectively. In addition, we examine the rarely explored AOI
data, which can further boost the level-1 and level-2 classification accuracy
by 13% and 14%. These results demonstrate the effectiveness of the proposed
approach and also indicate the usefulness of AOIs for land use mapping, which
are valuable for further studies.
