A review of research on remote sensing images shadow detection and application to building extraction
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Buildings are one of the most important habitats for humans, and therefore, accurate identification and extraction of building information in remote sensing images are crucial.Buildings in remote sensing images vary in shape and color due to differences in sensor acquisition methods, geographical location, and other factors.However, they all share a common featurethe presence of shadows.Obtaining accurate data from building shadows can provide a wealth of reliable information for building research.Consequently, it is crucial to review various methods for extracting building shadows, especially deep learning-based methods, to illustrate shadow implementation scenarios in building research: 1) building detection in very high resolution remote sensing images (VHRRSI); 2) building detection in SAR; 3) building change detection; 4) building damage assessment; 5) building height estimation; 6) building shadow removal; 7) other methods (such as building shadow data enhancement, detection of building shadows in ghost images, and conservation of historic buildings).This study discusses the advantages and disadvantages of building shadow detection methods and provides an overview of the datasets and evaluation metrics commonly used in studies of building shadow applications.We hope that this study will serve as a valuable reference for researchers in the field of building shadow studies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it