Using Remote Sensing to Address Green Heritage of the City of Marrakech, Morocco
Bibliographic record
Abstract
Climate change and rapid urbanization have significant impact on green spaces and natural resources in African countries.To investigate this impact in the city of Marrakech, this study develops remote-sensing data to monitor changes in land cover and land use from 1990 to 2020.Results show almost 35% diminution of vegetation cover over the investigation period.In 1990, the city of Marrakech had a vegetation cover of 4.2 km 2 , which fell to 2.7 km 2 in 2020.The main change occurred between 1990 and 2000 with a decrease of 13.7%, which is essentially due to the increase in build-up areas, related to the rapid growth of the city's population.This evolution in land cover affects the urban environment negatively including air quality and temperature regulation.This research provides a better understanding of changing trends, confirms the importance of using satellite imagery to monitor vegetation cover in urban settings, helps determine efficient environmental management, and affects successful green infrastructure policy and planning, thereby allowing for improved adaptation and mitigation to climate change. HIGHLIGHTS� Remote sensing and GIS confirm the potential benefits of their use to develop urban planning policies that address green heritage.� Land classification of Marrakech confirms that part of vegetation cover and fallow land were converted to built-up areas from 1990 to 2020.� Green resources are under significant pressure due to the expansion of the city.� To guarantee a decent ratio per capita, a plan for environmental improvements assess specific green spaces needs by implementing or preserving existing green heritage.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".