Bibliographic record
Abstract
Abstract. The work of ISPRS Technical Commission III is devoted to remote sensing. For the XXIVth ISPRS CONGRESS – 2021 Edition (Digital), Technical Commission III received total 201 submissions, including 72 full papers and 138 abstracts. Among these submissions 38 are accepted as peer-reviewed contributions for publication in the ISPRS Annals, 118 were accepted for publication in the ISPRS Archives.These papers are dedicated mostly to topics of the 10 TC III working groups and 4 inter-commission working groups as follows – WG III/1: Thematic Information Extraction; WG III/2: Microwave Remote Sensing; WG III/3: SAR-based Surface Generation and Deformation Monitoring; WG III/4: Hyperspectral Image Processing; WG III/5: Information Extraction from LiDAR Intensity Data; WG III/6: Remote Sensing Data Fusion; WG III/7: Landuse and Landcover Change Detection; WG III/8: Remote Sensing of Atmospheric Environment; WG III/9: Cryosphere and Hydrosphere; WG III/10: Agriculture and Natural Ecosystems Modelling and Monitoring; ICWG III/II: Planetary Remote Sensing and Mapping; ICWG III/Iva: Disaster Assessment, Monitoring and Management; ICWG III/IVb: Remote Sensing Data Quality; ICWG III/IVc: Environment and Health. The papers and abstracts were evaluated by the experts in the field and Working Group Chairs according to content, significance, originality, relevance, and clearness of presentation.We would like to thank the authors for their contributions, the reviewers for their reviewing, the working group officers for their efforts on calling for papers, and the organizers of the Congress for publishing this volume.
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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 | 0.010 | 0.022 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.005 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.012 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.304 | 0.343 |
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".