The status of Earth Observation (EO) & Geo-Information Sciences in Africa – trends and challenges
Bibliographic record
Abstract
Over the last 20 years, Africa has witnessed a slow but steady advancement in space-based technologies as they are increasingly recognized as an essential tool for decision-making that can leapfrog African development. A critical review on the outcome of a survey questionnaire focused on African private sector industries and universities, services and education/training in EO and Geo-Information Sciences, combined with literature review, and personal contacts reveal optimism for success in four sectors. These include the public sector (Government ministries and departments); Academic institutions (universities/colleges/national or regional centers); and space agencies and private sector companies. These sectors are intertwined and fundamental for creating an enabling environment for solutions to a broad spectrum of pressing priorities: job creation, poverty alleviation, and sustainable resource management. The result shows that there is an uptake in the number of institutions and market segments created. To date, there are more than 90 academic institutions and over 53 national space agencies in 28 countries. Within the 53 national space agencies, 11 African countries have already launched a total of 36 satellites into orbit, and additional five are expected by the first quarter of 2021; another five by 2025; thus, amounting to 46 satellites not foreseen ten years ago. In addition, there are now ten receiving and tracking stations in six African countries and 17 scientific National Associations or Societies with specialized expertise in Geo-Information technologies. The updated survey on the private sector in 2019 ascertained that around 4110 people are working in 130 of the 229 EO and Geo-Information Science companies identified in Africa. Ongoing investigations reiterate that companies dealing with space-based datasets and Geo-Information Sciences together with the private spin-off companies today absorb more than 15,000 people and the assumption is that this number is going to exceed 100,000 by the year 2025.
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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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.007 | 0.013 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.006 | 0.010 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".