Exploiting JECAM Database for Agriculture Land Cover Classification of Antsirabe Site Using Sentinel 2 Imagery with Deep Learning
Bibliographic record
Abstract
Zero hunger, the goal 2 of Sustainable Development Goals (SDGs), can only be achieved when food is available, affordable and accessible to the people.Food insecurity, a phenomenon where either or all of these ingredients for zero hunger are absent, remains a critical global issue that warrants coordinated strategies at regional scale; most especially for crop farming which serves as the major source of food for most humans.Therefore, efficient Land Use and Land Cover (LULC) classification is a pivotal tool in the development of apposite strategies for combating food insecurity.Open satellite missions like Sentinel 2 offer a cost effective way for acquiring regional imagery dataset for LULC classification; however, the relevance of such dataset is dependent on the quality of ground truth data from which the imagery dataset is created.Qualitative ground truth data are usually obtained through ground surveys which come at extra costs, warranting the need for elaborate community ground truth geo-database constructed from joint ground surveys.Such database is absent in the tropical belt that is mostly made up of developing countries where higher impacts of food insecurity are experienced.This remained the case, until recently when JECAM (Joint Experiment for Crop Assessment and Monitoring) database was developed for six countries in the tropical belt.JECAM database is an elaborate geodatabase that consists of 27,074 agricultural LULC polygons (20,257 crops and 6,817 non crops).In this study, we built three deep learning models for agricultural LULC classification using the entire 13 bands of the satellite imagery dataset.Class-based performance evaluation metrics were used to evaluate the performances of the deep learning models on test set.LSTM (Long Short-Term Memory) model exhibited the highest capability for LULC class discrimination, followed by 2D-CNN (2 Dimension Convolution Neural Network) Autoencoder model, then the 2D-CNN model.In the future, we intend to exploit spectral indices and transfer learning paradigm to address class imbalance problem, which is inherent in the imagery dataset, for improved LULC class discrimination.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| 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 teacher head, 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".