Use of the Earth Observing One (EO-1) Satellite for the Namibia SensorWeb Flood Early Warning Pilot
Bibliographic record
Abstract
The Earth Observing One (EO-1) satellite was launched in November 2000 as a one year technology demonstration mission for a variety of space technologies. After the first year, it was used as a pathfinder for the creation of SensorWebs. A SensorWeb is the integration of a variety of space, airborne and ground sensors into a loosely coupled collaborative sensor system that automatically provides useful data products. Typically, a SensorWeb is comprised of heterogeneous sensors tied together with an open messaging architecture and web services. SensorWebs provide easier access to sensor data, automated data product production and rapid data product delivery. Disasters are the perfect arena to test SensorWeb functionality since emergency workers and managers need easy and rapid access to satellite, airborne and in-situ sensor data as decision support tools. The Namibia Early Flood Warning SensorWeb pilot project was established to experiment with various aspects of sensor interoperability and SensorWeb functionality. The SensorWeb system features EO-1 data along with other data sets from such satellites as Radarsat, Terra and Aqua. Finally, the SensorWeb team began to examine how to measure economic impact of SensorWeb technology infusion. This paper describes the architecture and software components that were developed along with performance improvements that were experienced. Also, problems and challenges that were encountered are described along with a vision for future enhancements to mitigate some of the problems.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".