Complex settlement pattern extraction with multi-instance learning
Bibliographic record
Abstract
Per-pixel (or single instance) based classification schemes which have proven to be very useful in thematic classification have shown to be inadequate when it comes to analyzing very high resolution remote sensing imagery. The main problem being that the pixel size (less than a meter) is too small as compared to the typical object size (100s of meters) and contains too little contextual information to accurately distinguish complex settlement types. One way to alleviate this problem is to consider a bigger window or patch/segment consisting a group of adjacent pixels which offers better spatial context than a single pixel. Unfortunately, this makes per-pixel based classification schemes ineffective. In this work, we look at a new class of machine learning approaches, called multi-instance learning, where instead of assigning class labels to individual instances (pixels), a label is assigned to the bag (all pixels in a window or segment). We applied this multi-instance learning approach for identifying two important urban patterns, namely formal and informal settlements. Experimental evaluation shows the better performance of multi-instance learning over several well-known single-instance classification schemes.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".