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 $\textrm{m}^2$.

翻译：在难以进入区域的发展数据不足或难以获取的情况下，遥感数据成为利益相关者收集经济增长信息的重要工具。目前尚无研究利用深度学习在单个工业场地层面估算工业增长。本研究利用高分辨率全色影像，采用多层计算机视觉框架，估算了中华人民共和国419个工业场地随时间的发展变化。我们提出了两种近似发展程度的方法：（1）通过Mask R-CNN分割算法估算的结构面积覆盖率；（2）直接利用可见光红外成像辐射计套件（VIIRS）的可见光与红外辐射亮度推算发展程度。对这些方法生成的标签进行了比较评估与测试。在涵盖19年时段的2078张50厘米分辨率图像数据集上，结果表明可通过高分辨率日间影像估算工业发展的两个维度：（a）工业发展总面积（平均误差为0.021 $\textrm{km}^2$），（b）灯光辐射亮度（平均误差为9.8 $\mathrm{\frac{nW}{cm^{2}sr}}$）。技术趋势分析表明，基于Mask R-CNN标签训练的CNN-LSTM模型估算结果与地面真实测量最为吻合。从最早影像到最新影像，Mask R-CNN估算结果显示每个场地均呈现正增长，平均变化值为4084 $\textrm{m}^2$。