Nighttime Light Missing Data Retrieval Using Modis Version 6 Satellite Data and Mask Dilated Partial Convolutional Neural Network

Xuanchen Liu, Shuxin Qiao, Kyle Gao, Hongjie He, Lingfei Ma, Jonathan Li

Abstract: Nighttime Lights (NTLs) remote sensing imagery contains tremendous information and has been shown to accurately predict a region’s human dynamics, economic health and energy consumption. Despite its usefulness, NTLs imagery is less widely available than other remote sensing data modalities. Several challenges appear when attempting to reconstruct NTLs data, either from other data modalities or existing NTLs data. These include complex non-linear relationships between NTLs and multispectral bands, non-matching spatial and temporal coverage, and different atmospheric and cloud conditions. This study attempts to create an out-of-the-box model that compensates for missing NTLs data using widely available daytime data in a broadly generalizable manner. The proposed project has two objectives: the construction of an image-to-image dataset mapping daytime multispectral images (MODIS V6 Land Surface Reflectance, MODIS V6 Land Cover, MODIS V6 Vegetation Indices) to NTLs images, and the reconstruction of NTLs data using deep learning techniques by researching, creating, and employing the state-of-the-art architecture of the Mask Partial Convolutional Neural Network in conjunction with dilated convolutions. The project will facilitate the training of new models for predicting missing NTLs and make NTLs data more accessible for future remote sensing research.