论文标题
基于多源地理空间数据的城市土地使用映射的粗到精细方法
A Coarse-to-Fine Approach for Urban Land Use Mapping Based on Multisource Geospatial Data
论文作者
论文摘要
及时，准确的土地使用映射是一个长期存在的问题，这对于有效的土地和太空规划和管理至关重要。由于复杂和混合的使用，直接从广泛使用的遥感图像（RSI）的准确土地使用映射方面，尤其是对于高密度城市而言，这具有挑战性。为了解决这个问题，在本文中，我们提出了一种基于粗到的机器学习的方法，用于包裹级的城市土地使用映射，整合了包括RSI，利益点（POI）和利益领域（AOI）数据在内的多源地理空间数据。具体而言，我们首先根据从路网络产生的包裹中将城市分为构建和非建造区域。然后，我们采用不同地区的包裹的不同分类策略，最后将分类结果组合到集成的土地使用图中。结果表明，所提出的方法可以显着超过基线方法，该方法将构建和非建造区域混合在一起，对于1级和2级分类，精度分别增加了25％和30％。此外，我们研究了很少探索的AOI数据，这可以将Level-1和2级分类精度提高13％和14％。这些结果证明了拟议方法的有效性，还表明了AOIS对土地使用映射的有用性，这对于进一步的研究很有价值。
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.
