CLOUD-BASED GEOSPATIAL PLATFORM IN SUPPORT OF SUSTAINABLE DEVELOPMENT GOALS 2030: HOW TO BE PREPARED FOR EARTHQUAKE DISASTERS?
Bibliographic record
Abstract
Abstract. In July 2, 2018, the United Nations Economic and Social Council (ECOSOC) adopted a resolution of the strategic framework of disaster risk reduction. Many seismic countries have experienced challenges with natural hazards, such as earthquakes every year. Seismic safety monitoring and infrastructures, including building vulnerability assessment of earthquake are significant means to protect the safety of people and reduce the loss of property. We present cloud-based Geospatial Information Technologies in this study to support the Sustainable Development Goals (SDGs) 2030 in earthquake disaster loss reduction, mitigation, and resilience. The authors investigated and programmed the instruction building codes of the Federal Emergency Management Agency. We developed sophisticated algorithms to construct a geospatial cloud-based system to support the implementation of disaster risk reduction for strengthening infrastructures and resiliency of pre and post-earthquakes. However, the content is entirely based on the understanding of geospatial knowledge, engineering, and services to the people for a better world for future generations. The objectives of this study are to (1) participate in global sharing of experiences on utilizing geospatial information technologies to address disasters resilience and challenging issues of determining the vulnerability of buildings and estimation of risk as well as recommendation for retrofitting; and (2) developing Geospatial Infrastructure Management Ecosystem (GeoIME) including, Geospatial Rapid Visual Screening (GeoRVS) cloud-based platform. They enable the determination of the vulnerability of infrastructures, such as buildings and the estimation of risk for disaster reduction and management. This study shows that we reduced the cost and time for inspecting a building by 75% and %80, respectively. The application of this study can be used for retrofitting and rehabilitation of infrastructures like buildings and bridges for before and after earthquakes. Finally, we propose recommendations that might be helpful to countries having similar issues, and it has great potential for scalability and customization in other disasters such as floods.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".