UAV-Based Multi-Sensor Data Fusion for Urban Land Cover Mapping Using a Deep Convolutional Neural Network
Bibliographic record
Abstract
Accurate and up-to-date land cover classification information is essential for many applications, such as land-use change detection, global environmental change, and forest management, among others. Unoccupied aerial systems (UASs) provide the advantage of flexible and rapid data acquisition at low cost compared to conventional platforms, such as satellite and airborne systems. UASs are often equipped with high spatial resolution cameras and/or light detection and ranging (LiDAR). However, the high spatial resolution imagery has a high information content, which makes land cover classification quite challenging. Recently, deep convolutional neural networks (DCNNs) have been effectively applied to remote sensing applications, which overcome the drawback of traditional techniques. In this research, a low-cost UAV-based multi-sensor data fusion model was developed for land cover classification based on a DCNN. For the purpose of this research, two datasets were collected at two different urban locations using two different UASs. A DCNN model, based on U-net with Resnet101 as a backbone, was used to train and test the fused image/LiDAR data. The maximum likelihood and support vector machine techniques were used as a reference for classifier comparison. It was shown that the proposed DCNN approach improved the overall accuracy of land cover classification for the first dataset by 15% compared to the reference classifiers. In addition, the overall accuracy of land cover classification improved by 7%, and the precision, recall, and F-measure improved by 18% when the fused image/LiDAR data were used compared to the images only. The trained DCNN model was also tested on the second dataset, and the obtained results were largely similar to those of the first dataset.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.001 |
| 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".