Preface: 3D GeoInfo 2021
Bibliographic record
Abstract
Abstract. 3D GeoInfo 2021, the 16th GeoInfo conference, is an annual ISPRS workshop offering a forum for leading international decision makers and prominent voices in the field of 3D Geoinformation across the academic, commercial, and public sectors. The 2021 workshop is organized in conjunction with the 7th International FIG workshop on 3D Cadastres. The 2021 event was held virtually. Topics included: 3D data creation and acquisition 3D data processing and analysis 3D data management - data quality, metadata, provenance and trust Data integration, information fusion, multi-modal data analysis 3D visualization, including gamification, virtual reality, augmented reality 3D and Artificial Intelligence/Machine Learning 3D and Big Data, parallel computing, cloud computing 3D city modeling, underground infrastructure modeling, topography, and bathymetry modeling Building Information Modeling, Digital Twins, Smart Cities, Smart Infrastructure Usability and Human-Computer interaction in 3D GIS 3D GIS, spatial analysis and other applications (such as 3D cadastral systems, land administration, utilities, asset management, infrastructure, navigation, urban planning, geology, archaeology, marine systems, simulations, autonomous vehicles, facilities management, energy modeling, disaster and risk management, pandemic monitoring) The 3D GeoInfo 2021 tracks received 73 manuscripts including 30 full papers and 43 extended abstracts. The manuscripts were reviewed with a double-blind review process by members of the organizing and scientific committee and external reviewers. Ultimately 24 papers were accepted for the ISPRS Annals and 24 papers for the ISPRS Archives. We thank all of the authors and reviewers for their contributions.We look forward to the 3D GeoInfo 2021 virtual oral presentations and the opportunity to exchange ideas within our workshop and with the presenters and visitors of the 7th International FIG workshop on 3D Cadastres.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.011 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.337 | 0.238 |
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".