{"type":"ScholarlyArticle","title":"DeepLight: Reconstructing High-Resolution Observations of Nighttime Light With Multi-Modal Remote Sensing Data","authors":[{"name":"Lixian Zhang","slug":"lixian-zhang","given":"Lixian","family":"Zhang","affiliation":"National supercomputing center in Shenzhen; Tsinghua University -Xi'an Institute of Surveying and Mapping Joint Research Center"},{"name":"Runmin Dong","slug":"runmin-dong","given":"Runmin","family":"Dong","affiliation":"Tsinghua University; Tsinghua University -Xi'an Institute of Surveying and Mapping Joint Research Center"},{"name":"Shuai Yuan","slug":"shuai-yuan","given":"Shuai","family":"Yuan","affiliation":"The University of Hong Kong 4 Sun-Yat San University"},{"name":"Jinxiao Zhang","slug":"jinxiao-zhang","given":"Jinxiao","family":"Zhang","affiliation":"Tsinghua University"},{"name":"Mengxuan Chen","slug":"mengxuan-chen","given":"Mengxuan","family":"Chen","affiliation":"Tsinghua University"},{"name":"Juepeng Zheng","slug":"juepeng-zheng","given":"Juepeng","family":"Zheng","affiliation":""},{"name":"Haohuan Fu","slug":"haohuan-fu","given":"Haohuan","family":"Fu","affiliation":"Tsinghua University; Tsinghua University -Xi'an Institute of Surveying and Mapping Joint Research Center"}],"abstract":"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 multimodal 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 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 super-resolution tasks. Extensive experiments demonstrate the superiority of DeepLightSR over 8 competing methods, as evidenced by improvements in PSNR (2.01 dB ~ 13.25 dB) and PIQE (0.49 ~ 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. The code and data will be available at https://github.com/xian1234/DeepLight.","doi":"10.24963/ijcai.2024/837","url":"https://ijcai.org/proceedings/2024/837","pdf":"https://ijcai.org/proceedings/2024/0837.pdf","markdown":"https://ijcai.org/proceedings/2024/837.md","bibtex":"https://ijcai.org/proceedings/2024/bibtex/837","volume":{"slug":"2024","title":"Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2024","url":"https://ijcai.org/proceedings/2024/","year":2024,"location":"Jeju, Korea","dates":"3-9 August 2024","editors":"Kate Larson","isbn":"978-1-956792-04-1","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2024"},"section":"AI for Good","track":"AI for Good","number":837,"pages":{"first":7563,"last":7571},"keywords":["Computer Vision: General","Humans and AI: General"],"videos":[],"references":[{"position":1,"raw":"[Abrahams et al., 2018] Alexei Abrahams, Christopher Oram, and Nancy Lozano-Gracia. 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