Spatial-Gated Multilayer Perceptron for Land Use and Land Cover Mapping
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
Due to its capacity to recognize detailed spectral differences, hyperspectral data have been extensively used for precise Land Use Land Cover (LULC) mapping. However, recent multi-modal methods have shown their superior classification performance over the algorithms that use single data sets. On the other hand, Convolutional Neural Networks (CNNs) are models extensively utilized for the hierarchical extraction of features. Vision transformers (ViTs), through a self-attention mechanism, have recently achieved superior modeling of global contextual information compared to CNNs. However, to harness their image classification strength, ViTs require substantial training datasets. In cases where the available training data is limited, current advanced multi-layer perceptrons (MLPs) can provide viable alternatives to both deep CNNs and ViTs. In this paper, we developed the SGU-MLP, a deep learning algorithm that effectively combines MLPs and spatial gating units (SGUs) for precise Land Use Land Cover (LULC) mapping using multi-modal data from multi-spectral, LiDAR, and hyperspectral data. Results illustrated the superiority of the developed SGU-MLP classification algorithm over several CNN and CNN-ViT-based models, including HybridSN, ResNet, iFormer, EfficientFormer, and CoAtNet. The SGU-MLP classification model consistently outperformed the benchmark CNN and CNN-ViT-based algorithms. The code will be made publicly available at https: //github.com/aj1365/SGUMLP.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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