Future Prediction of Land Use Land Cover Change in the Salalah Region of Oman Using Landsat Satellite Data
Bibliographic record
Abstract
Analysing and predicting changes in land use and land cover (LULC) and urban growth are crucial for sustainable management and socio-economic development planning. This paper focuses on studying the urban land use expansion in Salalah City using high-resolution Landsat 8 satellite imagery from 2013 to 2021. Remote sensing techniques, such as maximum likelihood classification (MLC) and Geographic Information Systems (GIS), are employed to obtain Land Use Land Cover (LULC) change maps. Additionally, the future growth of urban land use from 2031 to 2041 is assessed using the Markov cellular automata (MCA) model in QGIS 2.26. The studies utilised four essential spatial variable maps—elevation, slope, aspect, and distance from the road in an ANN-Multi Layer Perception model to predict the impact on LULC changes between 2013 and 2017. The accuracy assessment of projected LULC maps for 2021 yielded promising results, with an overall Kappa value of 0.90 and a correctness percentage of 94.55 %. The results revealed a notable increase in built-up areas, rising from 79.82% in 2021 to a projected 96.96% in 2031 and 113.74% in 2041. Simultaneously, water bodies, bare ground, and range land experienced a decrease over time. These valuable findings have significant implications for urban planners and policymakers, offering insights for developing optimal land use plans and implementing better management techniques for the sustainable long-term development of natural resources.
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 | 0.001 |
| 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 | 0.001 | 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".