Aggregation and Visualization of Spatial Data with Application to Classification of Land Use and Land Cover
Bibliographic record
Abstract
Aggregation and visualization of geographical data are an important part of environmental data mining, environmental modelling, and agricultural management. However, it is difficult to aggregate geospatial data of the various formats such as maps, census and surveys. This paper presents a framework named PlaniSphere,which can aggregate the various geospatial datasets and synthesizes raw data. We developed an algorithm in PlaniSphere to aggregate remote sensing images with census data for classification and visualization of land use and land cover (LULC). The results show that the framework is able to classify geospatial data sets ofLULC from multiple formats. National census data sets can be used for calibration of remote sensing LULC classifications. This provides a new approach for the classification of remote sensing data. The approach proposed in this paper is useful for LULC classification in environmental spatial analysis.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 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 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".