Abstract
Due to insufficient or difficult to obtain data on development in inaccessible regions, remote sensing data is an important tool for interested stakeholders to collect information on economic growth. To date, no studies have utilized deep learning to estimate industrial growth at the level of individual sites. In this study, we harness high-resolution panchromatic imagery to estimate development over time at 419 industrial sites in the People's Republic of China using a multi-tier computer vision framework. We present two methods for approximating development: (1) structural area coverage estimated through a Mask R-CNN segmentation algorithm, and (2) imputing development directly with visible & infrared radiance from the Visible Infrared Imaging Radiometer Suite (VIIRS). Labels generated from these methods are comparatively evaluated and tested. On a dataset of 2,078 50 cm resolution images spanning 19 years, the results indicate that two dimensions of industrial development can be estimated using high-resolution daytime imagery, including (a) the total square meters of industrial development (average error of 0.021 $\textrm{km}^2$), and (b) the radiance of lights (average error of 9.8 $\mathrm{\frac{nW}{cm^{2}sr}}$). Trend analysis of the techniques reveal estimates from a Mask R-CNN-labeled CNN-LSTM track ground truth measurements most closely. The Mask R-CNN estimates positive growth at every site from the oldest image to the most recent, with an average change of 4,084 $\textrm{m}^2$.
Abstract (translated)
由于难以获取在难以到达的地区的发展数据,遥感数据是感兴趣的利益相关者收集经济增长信息的重要工具。到目前为止,没有研究使用深度学习估计单个站点的发展。在本研究中,我们利用高分辨率彩色图像,使用多级计算机视觉框架估计了在中国419个工业地点的未来发展,使用了VIIRS相机测量系统。我们提出了两种方法来近似发展:(1)通过Mask R-CNN分割算法估计结构面积覆盖,(2)通过VIIRS可见和红外辐射来计算发展直接。这些方法产生的标签进行比较评估和测试。在一个涵盖19年的50厘米分辨率图像数据集上,结果表明,可以使用高分辨率白天图像估计两个发展维度,包括(a)工业发展总面积(平均误差为0.021 $ extrm{km}^2),(b)灯光亮度(平均误差为9.8 $mathrm{frac{nW}{cm^{2}sr}}$)。技术趋势分析揭示了由Mask R-CNN标注的CNN-LSTM跟踪的地面真实测量数据最接近的指标估计。Mask R-CNN从最古老的图像到最近的图像估计了每个站点的积极增长,平均变化为4,084 $ extrm{m}^2。
URL
https://arxiv.org/abs/2301.09620
