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Policy makers often make decisions based on parameters such as GDP,unemployment rate, industrial output, etc. The primary methods to obtain oreven estimate such information are resource intensive and time consuming. Inorder to make timely and well-informed decisions, it is imperative to be ableto come up with proxies for these parameters which can be sampled quickly andefficiently, especially during disruptive events, like the COVID-19 pandemic.Recently, there has been a lot of focus on using remote sensing data for thispurpose. The data has become cheaper to collect compared to surveys, and can beavailable in real time. In this work, we present Regional GDP NightLight(ReGNL), a neural network based model which is trained on a custom dataset ofhistorical nightlights and GDP data along with the geographical coordinates ofa place, and estimates the GDP of the place, given the other parameters. Takingthe case of 50 US states, we find that ReGNL is disruption-agnostic and is ableto predict the GDP for both normal years (2019) and for years with a disruptiveevent (2020). ReGNL outperforms timeseries ARIMA methods for prediction, evenduring the pandemic. Following from our findings, we make a case for buildinginfrastructures to collect and make available granular data, especially inresource-poor geographies, so that these can be leveraged for policy makingduring disruptive events.

ReGNL: Rapid Prediction of GDP during Disruptive Events using Nightlights