2015 Urban Extents from VIIRS and MODIS for the Continental U.S. Using Machine Learning Methods

The 2015 Urban Extents from VIIRS and MODIS for the Continental U.S. Using Machine Learning Methods data set models urban settlements in the Continental United States (CONUS) as of 2015. When applied to the combination of daytime spectral and nighttime lights satellite data, the machine learning methods achieved high accuracy at an intermediate-resolution of 500 meters at large spatial scales. The input data for these models were two types of satellite imagery: Visible Infrared Imaging Radiometer Suite (VIIRS) Nighttime Light (NTL) data from the Day/Night Band (DNB), and Moderate Resolution Imaging Spectroradiometer (MODIS) corrected daytime Normalized Difference Vegetation Index (NDVI). Although several machine learning methods were evaluated, including Random Forest (RF), Gradient Boosting Machine (GBM), Neural Network (NN), and the Ensemble of RF, GBM, and NN (ESB), the highest accuracy results were achieved with NN, and those results were used to delineate the urban extents in this data set.

本数据集名为《2015年美国大陆城市范围VIIRS与MODIS数据》，运用机器学习方法对美国大陆（CONUS）2015年的城市聚落进行建模。当应用于日间光谱与夜间灯光卫星数据的组合时，机器学习方法在500米的中等分辨率下，在大尺度空间范围内达到了高精度。这些模型的输入数据包括两种卫星图像：来自日夜波段（DNB）的可见红外成像辐射计套装（VIIRS）夜间灯光（NTL）数据和校正后的中分辨率成像光谱辐射计（MODIS）日间归一化植被指数（NDVI）。尽管评估了包括随机森林（RF）、梯度提升机（GBM）、神经网络（NN）以及随机森林、梯度提升机和神经网络的集成（ESB）在内的多种机器学习方法，但最高精度结果由神经网络（NN）实现，并据此在该数据集中绘制了城市范围。