Tracking the industrial growth of modern China with high-resolution panchromatic imagery: A sequential convolutional approach

Satellite imagery analysis using deep learning methods, specifically convolutional neural networks (CNNs), has grown in popularity since 2012, with uses extending into the estimation of population [1], wealth [2], poverty [3], conflict [4], migration [5], education [6], and infrastructure [7], among other applications [8, 9, 10, 11]. These techniques have broadly illustrated that harnessing satellites to remotely track development over time in otherwise data sparse regions is a potentially effective strategy [12]. One currently untested application of deep learning with satellite imagery is the identification and monitoring of industrial sites (e.g., factories, power plants, ports). The development of industrial sites is of broad interest, as it can serve as a proxy for everything from economic development [13] to the projection of soft power [14]. Because of its interrelationship with national security or proprietary corporate interests, information on such large-scale development is often undocumented or difficult to obtain openly by interested parties. This article focuses on testing our capability to automatically detect and monitor industrial sites within China using high-resolution panchromatic satellite imagery. Largely unrecorded in structured open source text information, the size and extent of industrial sites in China can be observed through routine or targeted satellite collection. From select sources, many locations appear, on average, at least yearly in cloud-free high-resolution imagery from satellite-based sensors over the past 15 years; some locations of interest have temporal granularity of as high as one day. To-date, no work has explored the use of machine learning methods trained on satellite imagery to estimate, and monitor over time, the development of particular economic industries at the scale of individual sites.