Enterprise AI Analysis
SLUM-i: Semi-supervised Learning for Urban Mapping of Informal Settlements and Data Quality Benchmarking
This research introduces SLUM-i, a novel semi-supervised learning framework for robust urban mapping of informal settlements. It addresses challenges like data scarcity and spectral ambiguity by integrating Class-Aware Adaptive Thresholding and a Prototype Bank System. Validated on a new benchmark dataset for South Asian megacities (Lahore, Karachi, Mumbai) and five established benchmarks, SLUM-i outperforms state-of-the-art baselines and demonstrates superior zero-shot transferability, achieving a 0.461 mIoU on unseen geographies with only 10% labeled data.
Key Impact Metrics
SLUM-i delivers groundbreaking performance in challenging urban mapping scenarios, driving efficiency and accuracy.
0.461
Mean IoU (mIoU) on Unseen Geographies
10%
Training Data Used for Max Transfer
Superior
Domain Transfer Capability
Deep Analysis & Enterprise Applications
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Methodology Overview
Performance Insights
SLUM-i Framework Workflow
Labeled & Unlabeled Data Input
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Weak Augmentation (x^w)
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Encoder (g) & Decoder (h)
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Pseudo-Label Generation (p^w)
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Class-Aware Adaptive Thresholding (CAAT)
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Prototype Bank System (Semantic Anchoring)
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Strong Augmentation (x^s1, x^s2) for Consistency
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Total Objective Function (L_total) for Optimization
Critical Thresholding Adjustment
0.95 Original Static Confidence ThresholdThe baseline UniMatch relies on a fixed global threshold (τ = 0.95), which disproportionately suppresses minority classes in imbalanced datasets. SLUM-i's CAAT dynamically adjusts this for improved performance.
| Method | 10% Label | 20% Label | 30% Label | 100% Labeled (Ref) |
|---|---|---|---|---|
| Supervised | 0.324 | 0.326 | 0.368 | 0.437 |
| FixMatch [24] | 0.446 | 0.440 | 0.405 | |
| UniMatch [22] | 0.441 | 0.435 | 0.445 | |
| SLUM-i (Ours) | 0.447 | 0.431 | 0.457 | |
| Supervised † (DINOv2) | 0.388 | 0.387 | 0.419 | 0.456 |
| UniMatch-v2 [27] † | 0.443 | 0.443 | 0.443 | |
| SLUM-i (Ours) † | 0.461 | 0.452 | 0.461 |
Lahore, Karachi, and Mumbai Benchmarks
SLUM-i was rigorously evaluated on newly introduced, verified high-resolution semantic segmentation datasets for Lahore, Karachi, and Mumbai, validated against official records. These datasets address the lack of high-quality annotations and inherent data quality challenges, such as high spectral ambiguity and annotation noise, prevalent in South Asian megacities.
- Lahore (171 km²): Low SNR (5.7) reflecting subtle visual distinctions characteristic of mixed formal-informal neighborhoods.
- Karachi (1,182 km²): High SNR (12.9) with strong visual cues, focused on regions where slums are most densely clustered.
- Mumbai (516 km²): High SNR (14.8) with strong visual cues, derived from high-resolution satellite imagery.
Computational Efficiency
0.65% Negligible Overhead (ResNet-101)Our ResNet-101 based configuration incurs a negligible overhead of 0.65% compared to the standard UniMatch pipeline, maintaining high computational efficiency while delivering superior results.
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