Water Observations from Space: accurate maps of surface water through time for the continent of Africa
Bibliographic record
Abstract
Earth observation of waterbodies through time is a powerful tool in understanding both the location of waterbodies and their temporal dynamics. Water Observations from Space (WOfS), developed and well-tested in Australia, is a service providing historical surface water observations derived from Landsat satellite imagery from 1987 to present day. WOfS provides better understanding of where water is usually present; where it is seldom observed; and where inundation of the surface has been occasionally observed by satellite. We applied the WOfS algorithm to Africa and validated its accuracy through image interpretation of satellite and aerial imagery using an online tool created by the NASA Servir program, Collect Earth Online. The Digital Earth Africa Product Development Task Team, composed of four regional geospatial organisations RCMRD, AfriGIST, AGRHYMET and OSS, conducted the validation campaign and provided both the regional expertise and experience required for a continental-scale validation effort. In order to understand the accuracy and bias of the WOfS algorithm in Africa at both the continental-scale and regional zones, we generated 2900 sample points covering the continent including the main islands and distributed them into 7 Agro-ecological zones. We assessed whether the point was flooded, dry, or cloud covered, for 12 months in 2018, resulting in 34,800 assessed observations. As water information is available through WOfS in near real-time, it can be used for environmental monitoring, flood mapping, monitoring planned water releases, and management of water resources in highly regulated systems. WOfS is expected to be used by ministries and state departments of agriculture and water management in countries, international organizations, academia and the private sector.
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.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 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 | 0.001 |
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".