TY - GEN
T1 - A Coarse-to-Fine Approach for Urban Land Use Mapping Based on Multisource Geospatial Data
AU - Zhou, Qiaohua
AU - Cao, Rui
N1 - Funding Information:
This work was supported in part by the National Natural Science Foundation of China under Grant 42101472 and the Hong Kong Polytechnic University Start-Up under Grant BD41.
Publisher Copyright:
© 2022 IEEE.
PY - 2022/12
Y1 - 2022/12
N2 - 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 areas-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 250% 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.
AB - 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 areas-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 250% 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.
KW - areas-of-interest (AOI)
KW - coarse-to-fine grained classification
KW - land use mapping
KW - machine learning
KW - multi-source data fusion
KW - points-of-interest (POI)
KW - remote sensing image (RSI)
UR - http://www.scopus.com/inward/record.url?scp=85143887184&partnerID=8YFLogxK
U2 - 10.1109/Geoinformatics57846.2022.9963851
DO - 10.1109/Geoinformatics57846.2022.9963851
M3 - Conference article published in proceeding or book
T3 - International Conference on Geoinformatics
BT - Proceedings - 2022 29th International Conference on Geoinformatics, Geoinformatics 2022
A2 - Hu, Shixiong
A2 - Ye, Xinyue
A2 - Lin, Hui
A2 - Gao, Song
A2 - Zheng, Xinqi
A2 - Zhang, Chunxiao
ER -
