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Accuracy Assessment and Reference data Collection

Accuracy Assessment and Reference data Collection. Kamini Yadav Dr. Russ Congalton 20 August 2015. Reference data collection , Organization & Web Hosting. Had Telecon on reference data protocols on July 28, 2015. Different data source we have: Ground data Crowd sourced data Geo-wiki

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Accuracy Assessment and Reference data Collection

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  1. Accuracy Assessment and Reference data Collection Kamini Yadav Dr. Russ Congalton 20 August 2015

  2. Reference data collection , Organization & Web Hosting Had Telecon on reference data protocols on July 28, 2015 Different data source we have: • Ground data • Crowd sourced data • Geo-wiki • Cropland layers • High resolution imagery • Literature Own different quality and standards Bring to GFSAD30 Objectives & Requirements using a common platform https://croplands.org/

  3. Need Standard Protocols…. • To be shared, visualized and made available both for the mapping and accuracy team members • The collection and sharing of reference data will be designed under 5 main steps: • All Reference Data Will be Provided to Justin • Cross Walk • Stratification based on Crop type • 60/40 Split into Training and Validation Data • Spatial Autocorrelation

  4. Actions Collected reference data will reside with Justin and will be distributed by him through https://croplands.org/ All other reference data such as Europe’s Corine data base will be ingested and analyzed based on this protocol The accuracy assessment team will test the validity of the sourced referenced data in comparison to the ground All reference data will be tagged with a flag as Ground “G” or Derived “D” The Accuracy Assessment Team will generate more samples to fill in the data gap areas using this protocol. Standard protocol for reference data collection in Flowchart Prepared by Justin

  5. More Samples • Generated 100 Random samples in 250m homogeneous No Crop region • Can generate more just to balance crop/No crop samples • Accuracy will remain almost same; but might have statistical significance in other regions

  6. High Resolution Imagery • Success in using RHSeg segmentation output in eCognition. • Segmentation is forced to maintain the field boundaries as per the RHSeg output • Now, small area region objects can be handled easily while labeling them (Also done by Jim in RHSeg itself) Working on useful vegetation indices to be used as variables in training and classification

  7. Random Forest in ‘R’ Variable Importance values Out of the bag error is the error rate of the out-of-bag classifier on the training set It gives empirical evidence to show that the out-of-bag estimate is as accurate as using a test set of the same size as the training set OOB estimate of error rate: 11.33%

  8. Future Work • Generate Crop/No-Crop samples in Africa • Generate pure samples in Canada • Come up with the classified output in eCognition/R • Continue to collect reference data from other sources from literature and high resolution imagery

  9. Thanks

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