Detection and impacts of tiling artifacts in MODIS burned area classification
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
Abstract Since 2000, observations from the Moderate Resolution Imaging Spectroradiometer (MODIS) instrument, aboard the Terra and Aqua satellites, have been used to monitor global burned area and its trends. The FireCCI and MCD64A1 products classify burned area using algorithms that detect change in surface reflectance and separately process each ∼10° × 10° MODIS tile. We find that artifacts arise in both products from this tiling procedure. In particular, we find severe tiling artifacts in FireCCI, version 5.1 (FireCCI51) in northwest India and Pakistan, where the classified burned area is disjointed at the latitudinal boundary of two tiles that largely separates the Indian states of Punjab and Haryana. In contrast, this tiling effect is less noticeable in MCD64A1, Collection 6 (C6). As a result, while the average 2003–2019 October-November burned area in Haryana is of similar magnitude across the two products, that for Punjab is 13,381 km 2 for MCD64A1 and just 1,486 km 2 for FireCCI. We find moderate tiling artifacts in Southeast Asia and Eastern Europe. Our results highlight that additional processing is needed to ensure the continuity of burned area classification in FireCCI and MCD64A1, as well as other products relying on tile-dependent algorithms.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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