Image Classification of Land Use Land Cover of Bengaluru City Using Convolutional Neural Network
Why this work is in the frame
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Bibliographic record
Abstract
Developing countries like India is witnessing an increasing economic growth, rapid population in addition to industrialization leading to an increased rate of land use and cover. In order to better utilize the land and natural resource is essential to classify and analyse the land use and cover. Machine Learning and Deep Learning techniques are considered to be one of the effective and efficient ways for analysing and classifying the land use & cover. Here, in this paper, methodology for land use & cover classification – analysis of rural and urban regions of Bengaluru is been proposed. The proposed system’s main objective is to monitor the land cover changes of Bengaluru district including its rural and urban region for classifying the land cover into its exact classes. Classification algorithms such as SVM (Support Vector Machine), RF (Random Forest), KNN (K – Nearest Neighbor) and DT (Decision Tree) are used in the preprocessing of images and model created is tested using CNN. The Landsat datasets from usgs earth explorer is used. Performance evaluation of these algorithms are done based on their accuracy rates and efficiency. The proposed system shows that CNN classifies the land cover classes efficiently because of its highest accuracy and efficiency rates when compared with other algorithms.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it