OGSOD-2.0: a challenging multimodal benchmark for optical-SAR object detection
Bibliographic record
Abstract
With the development of the rapid development of satellite technology, multimodal object detection between optical and SAR (Synthetic Aperture Radar) images has attracted growing interest in the field of remote sensing image interpretation. However, this area lacks a dataset that considers novel and diverse challenges related to practical applications, crucial for both the training and evaluation of recent detectors. In this paper, we introduce a new challenging multi-modal dataset for optical-SAR object detection, OGSOD-2.0, aiming to enhance object detection in tiny-scale and crowded objects under complex backgrounds. Building on OGSOD-1.0, the proposed dataset supplements more 5,130 optical-SAR image pairs from the Sentinel satellite series with 24,421 instance annotations, containing four significant types of static objects: bridges, harbors, oil tanks, and playgrounds. These objects exhibit a wide variety of scales, aspect ratios, and orientations under complex aerial scenarios. Specially, most objects are characterized by relatively low resolution, even smaller than 12 pixels, and clustered together at high densities, further increasing the challenges for existing detection methods. To evaluate the challenging aspects of OGSOD-2.0, we compare our proposed dataset with existing optical-SAR datasets over several state-of-the-art methods including single-modal, cross-modal and multimodal detectors. Comprehensive experiments show that proposed OGSOD-2.0 are quite challenging and related to practical applications. This multi-modal benchmark will be publicly available.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.005 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.005 |
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".