Knowledge of detailed urban land-use patterns is essential in urban management, economic analysis, and policy-making aimed at sustainable urban development. To extract this information, previous studies relied on either the physical features extracted from remote sensing images or human activity patterns analyzed from social sensing data, but seldom on both of them. In this study, we proposed a framework to map the land-use patterns of New York City by combining multiple-source social sensing data and remote sensing images. We started by generating urban land use parcels using the transportation network from the Open street map and grouping them into built-up and non-built-up categories. Then, the random forest method was applied to classify built-up parcels and the National Land Cover Data was used to determine the land use type for non-built-up parcels. Results indicate that a satisfying overall testing accuracy with 77.31% was achieved for the level I classification (residential, commercial, and institutional regions) and 66.53% for level II classification (house, apartment, public service, transportation, office building, health service, education, and retails). Among the Level II classes, the residential land use has achieved the highest accuracy in built-up parcels with the user’s accuracy at 74.19% and producer’s accuracy at 80.99%. In addition, the classified map indicates that most commercial areas are concentrated in the Manhattan, residential land uses are distributed in the boroughs of Staten Island, Bronx, Queens, and Brooklyn, and institutional areas are evenly distributed in Manhattan, Brooklyn, Queens, Bronx, and Staten Island. The classified land use and functional information could further be used in other studies, such as urban planning and urban building energy use modeling.

了解详细的城市土地使用方式对于城市管理，经济分析和旨在可持续城市发展的政策制定至关重要。为了提取这些信息，以前的研究要么依赖于从遥感影像中提取的物理特征，要么依赖于根据社会感知数据分析出的人类活动模式，但很少涉及这两者。在这项研究中，我们提出了一个框架，通过结合多源社会感知数据和遥感图像来绘制纽约市的土地利用模式。我们首先使用开放街道地图中的交通网络生成城市土地使用地块，然后将其分组为已建成和未建成类别。然后，应用随机森林法对建成地块进行分类，并使用国家土地覆盖数据确定非建成地块的土地利用类型。结果表明，一级测试（住宅，商业和机构区域）的总体测试准确率达到了令人满意的77.31％，二级测试（房屋，公寓，公共服务，交通，办公大楼，卫生服务，教育和零售）。在II级级别中，住宅用地在已建成宗地中的精度最高，用户的精度为74.19％，生产商的精度为80.99％。此外，机密地图显示大多数商业区都集中在曼哈顿，住宅用地分布在史坦顿岛，布朗克斯，皇后区，和布鲁克林，机构区平均分布在曼哈顿，布鲁克林，皇后区，布朗克斯和史泰登岛。分类的土地利用和功能信息可进一步用于其他研究，例如城市规划和城市建筑能源利用模型。