Optimizing Satellite Imagery Object Detection in Challenging Weather Conditions using IoT-Driven Fusion Strategies
Bibliographic record
Abstract
IoT devices collect data from remote areas for applications like environmental monitoring and disaster management. Satellite imagery complements this data by providing largescale coverage and context, enhancing decision-making in various domains. Aligning sensor data with satellite imagery resolution to integrate IoT and satellite data for advanced analytics poses a challenge, requiring a balance between local and global detection capabilities in the context of diverse spatiotemporal characteristics. Diverse weather conditions further complicate detection accuracy within IoT frameworks, emphasizing the need for comprehensive data capture and accurate analytics. To address this, we employ weighted fusion techniques that combine multiple detection models like You Only Look Once version (YOLOv7) and Detection TRansformer (DETR) algorithms to achieve objected detection with enhanced accuracy and robustness across different weather conditions and spatial scales. Specifically, this method incorporates the Weighted Fusion with Intersection over Union Matching (WF-IoU), Machine Learning (ML)-based fusion using Random Forest Regression (RFR), AdaBoost Regression (ABR), and XGBoost Regression (XGBR), as well as Deep Learning (DL)based fusion. These techniques integrate various algorithms to optimize object detection performance within IoT environments. Nevertheless, determining optimal Fusion Weights (FWs) and ensuring robustness across scenarios remains a challenge. Furthermore, addressing computational complexities in training and optimizing fusion models is imperative for seamless integration into IoT system. To validate our proposals, we conducted detection tasks across eight weather conditions, simulating them with a Satellite Cloud Generator (SCG). From that, we found that fusion method and its sub-approaches outperform the individual algorithms, enhancing object detection in integration with IoT applications, and thereby improving decision-making and resource management in complex scenarios.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".