Nighttime Light Missing Data Retrieval Using Modis Version 6 Satellite Data and Mask Dilated Partial Convolutional Neural Network
Bibliographic record
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".