AGRICULTURE LAND USE MAPPING USING MULTI-SENSOR AND MULTI- TEMPORAL EARTH OBSERVATION DATA
Bibliographic record
Abstract
Large area mapping of crop information is a crucial source of information on agricultural land use, and can be done most efficiently using Earth observation (EO) technology. Agriculture and Agri-Food Canada (AAFC) is developing a crop inventory system based on EO data. Detailed crop type identification relies on image data acquired during key crop phenological stages in order to capture the unique temporal signature of individual crop types. Optical data such as Landsat and SPOT can provide valuable information for crop classification, however, cloud cover is a reoccurring obstacle to the application of optical data, hindering mapping and monitoring at regional and national scales. Consequently, the integration of radar with optical EO data is essential for operational monitoring over many areas. Crop information mapping from EO data was conducted by AAFC for two consecutive years (2004 and 2005) over an Eastern Ontario pilot site. Landsat, SPOT, RADARSAT (standard mode), and Envisat ASAR (VV, VH) data were acquired during the growing seasons for both years. This site consists primarily of corn, soybean, cereal, forage production, and animal pasture. Three classification techniques (MaximumLikelihood Classifier, Decision-Tree Classifier, and Neural Network Classifier) were applied to various image combinations. Overall, these results show that data acquired later in the growing season provide a better classification accuracy for both optical and radar data. The integration of radar and optical data resulted in a synergistic effect, producing an increase in classification accuracies (individual class accuracy, overall accuracy, and Kappa coefficient).
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| 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) | 0.001 | 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".