AI-Powered Satellite Imagery in Land Use Mapping and Monitoring in Uganda
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Satellite imagery has been widely used for land use mapping and monitoring due to its ability to provide comprehensive coverage of large geographical areas over time. A hybrid machine learning approach combining deep convolutional neural networks (CNNs) with transfer learning was employed. The dataset consisted of Landsat satellite images from multiple years, segmented into training and validation sets for model development and evaluation. The AI models achieved an overall accuracy of 92% in classifying land use types compared to manual interpretation, demonstrating the potential of automated systems in large-scale applications. This study highlights the efficacy of AI in satellite imagery analysis for sustainable land management practices and underscores its utility for monitoring environmental changes over time. Further research should focus on integrating AI into existing governmental and non-governmental initiatives to enhance spatial data governance and policy implementation. AI, Satellite Imagery, Land Use Mapping, Machine Learning, Uganda 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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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 | 0.001 |
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 it