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| Hauptverfasser: | , , , , , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2024
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| Schlagworte: | |
| Online-Zugang: | https://arxiv.org/abs/2410.22150 |
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| _version_ | 1866913567124488192 |
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| author | Thomas, Nancy Rahimi, Saba Vapsi, Annita Ansell, Cathy Christie, Elizabeth Borrajo, Daniel Balch, Tucker Veloso, Manuela |
| author_facet | Thomas, Nancy Rahimi, Saba Vapsi, Annita Ansell, Cathy Christie, Elizabeth Borrajo, Daniel Balch, Tucker Veloso, Manuela |
| contents | Amidst escalating climate change, hurricanes are inflicting severe socioeconomic impacts, marked by heightened economic losses and increased displacement. Previous research utilized nighttime light data to predict the impact of hurricanes on economic losses. However, prior work did not provide a thorough analysis of the impact of combining different techniques for pre-processing nighttime light (NTL) data. Addressing this gap, our research explores a variety of NTL pre-processing techniques, including value thresholding, built masking, and quality filtering and imputation, applied to two distinct datasets, VSC-NTL and VNP46A2, at the zip code level. Experiments evaluate the correlation of the denoised NTL data with economic damages of Category 4-5 hurricanes in Florida. They reveal that the quality masking and imputation technique applied to VNP46A2 show a substantial correlation with economic damage data. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_22150 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Shining a Light on Hurricane Damage Estimation via Nighttime Light Data: Pre-processing Matters Thomas, Nancy Rahimi, Saba Vapsi, Annita Ansell, Cathy Christie, Elizabeth Borrajo, Daniel Balch, Tucker Veloso, Manuela Computer Vision and Pattern Recognition Amidst escalating climate change, hurricanes are inflicting severe socioeconomic impacts, marked by heightened economic losses and increased displacement. Previous research utilized nighttime light data to predict the impact of hurricanes on economic losses. However, prior work did not provide a thorough analysis of the impact of combining different techniques for pre-processing nighttime light (NTL) data. Addressing this gap, our research explores a variety of NTL pre-processing techniques, including value thresholding, built masking, and quality filtering and imputation, applied to two distinct datasets, VSC-NTL and VNP46A2, at the zip code level. Experiments evaluate the correlation of the denoised NTL data with economic damages of Category 4-5 hurricanes in Florida. They reveal that the quality masking and imputation technique applied to VNP46A2 show a substantial correlation with economic damage data. |
| title | Shining a Light on Hurricane Damage Estimation via Nighttime Light Data: Pre-processing Matters |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2410.22150 |