Visual Attention Model with Adaptive Weighting of Conspicuity Maps for Building Detection in Satellite Images
Bibliographic record
Abstract
Abstract The lack of automation and the limited performance of current image processing techniques pose critical challenges to the efficient and timely use of the large amount of data made available by aerial and space based assets. The imitation of fast adaptation and inference capability of human visual system appears to be a promising research direction for the development of computational algorithms able to deal with large variations in image content, characteristics and scale as those encountered in satellite imaging. The paper explores the potential use of an improved computational model of visual attention for the complex task of building identification in satellite images. It contributes to extend the envelope of application areas of such models and also to expand their current use from single object to multiple object detection. A set of original weighting schemes based on the contribution of different features to the identification of building and non-building areas is first proposed and evaluated against existing solutions in the literature. A novel adaptive algorithm then chooses the best weighting scheme based on a similarity error to ensure the best performance of the attention model in a given context. Finally, a neural network is trained to predict the set of weights provided by the best weighting scheme for the context of the image in which buildings are to be detected. The solution provides encouraging results on a set of 50 satellite images.
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.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".