  Nighttime light (NTL) remote sensing observation serves as a unique proxy for
quantitatively assessing progress toward meeting a series of Sustainable
Development Goals (SDGs), such as poverty estimation, urban sustainable
development, and carbon emission. However, existing NTL observations often
suffer from pervasive degradation and inconsistency, limiting their utility for
computing the indicators defined by the SDGs. In this study, we propose a novel
approach to reconstruct high-resolution NTL images using multi-modal remote
sensing data. To support this research endeavor, we introduce DeepLightMD, a
comprehensive dataset comprising data from five heterogeneous sensors, offering
fine spatial resolution and rich spectral information at a national scale.
Additionally, we present DeepLightSR, a calibration-aware method for building
bridges between spatially heterogeneous modality data in the multi-modality
super-resolution. DeepLightSR integrates calibration-aware alignment, an
auxiliary-to-main multi-modality fusion, and an auxiliary-embedded refinement
to effectively address spatial heterogeneity, fuse diversely representative
features, and enhance performance in $8\times$ super-resolution (SR) tasks.
Extensive experiments demonstrate the superiority of DeepLightSR over 8
competing methods, as evidenced by improvements in PSNR (2.01 dB $ \sim $ 13.25
dB) and PIQE (0.49 $ \sim $ 9.32). Our findings underscore the practical
significance of our proposed dataset and model in reconstructing
high-resolution NTL data, supporting efficiently and quantitatively assessing
the SDG progress.
