Satellite Imagery and AI in Land Use Mapping and Monitoring in Nigeria
Bibliographic record
Abstract
Satellite imagery and artificial intelligence (AI) are increasingly being utilised for land use mapping and monitoring in Nigeria to address challenges related to urban sprawl, deforestation, and agricultural productivity. A combination of high-resolution satellite images from the Landsat programme and machine learning models was employed. Specifically, a Convolutional Neural Network (CNN) model was trained on a dataset comprising satellite imagery spanning multiple years to classify different land cover types with an accuracy rate above 90%. The analysis revealed significant variations in land use patterns across the northeastern region of Nigeria, with urban expansion accounting for approximately 35% of total land change from to . This pattern is consistent with economic development trends and infrastructure investments. This study demonstrates the efficacy of satellite imagery and AI in monitoring rapid changes in land use, providing valuable insights for policymakers and resource managers aiming to manage natural resources sustainably. Further research should focus on integrating remote sensing data from other sources such as ground surveys and socioeconomic indicators to enhance the model's predictive accuracy. Implementation strategies should be developed to leverage these findings for effective land management policies. AI, Convolutional Neural Network (CNN), satellite imagery, land use monitoring, Nigeria Model estimation used $\hat{\theta}=argmin_{\theta}\sum_i\ell(y_i,f_\theta(x_i))+\lambda\lVert\theta\rVert_2^2$, with performance evaluated using out-of-sample error.
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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 | 0.001 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".