A comparative analysis of PlanetScope 4-band and 8-band imageries for land use land cover classification
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Earth-observing satellites have become essential in comprehending human impacts on the landscape. Satellite-based imagery is indispensable for mapping Earth's features, managing resources, and studying environmental changes. Readily available remote sensing data with improved radiometric, spectral, spatial, and temporal resolution presents opportunities for advanced data analysis. Precise and accurate land use land cover (LULC) information is essential for the surveillance of environmental conditions and the effective management of natural resources. This research assesses the performance of PlanetScope product SuperDove sensor (PSB.SD), having two different band combinations, including 4-band (Red, Blue, Green and Near-Infrared (NIR)) and 8-band (Blue, Green II, Red, NIR, Coastal Blue, Green, Yellow, Red-Edge) imagery in ArcGIS Pro for the month of July 2021. Four different supervised classifiers, including support vector machine (SVM), k-nearest neighbours (KNN), random forest (RF), and maximum likelihood (ML) classifiers. This study was carried out for the three major areas, i.e., City of Summerside, City of Charlottetown, and Town of Three Rivers in Prince Edward Island (PEI), Canada and LULC classification scheme consists of six major classes, which include Agriculture, Forest, Vegetation, Bare Land, Urban and Water bodies. For accuracy assessment, overall accuracy as well as kappa coefficient were estimated to identify the most accurate combination of LULC classifier and different band combination imagery from PlanetScope. Results show that the highest overall accuracy of 0.94 for Town of Three Rivers and 0.93 for City of Summerside and City of Charlottetown were observed using 8-band imagery with SVM classifier. The lowest overall accuracy of 0.78 for Town of Three Rivers, 0.83 for City of Charlottetown, and 0.82 for City of Summerside was observed using 4-band imagery using ML classifier. Further, the SVM classifier performs well in accuracy with 8-band imagery of PlanetScope, showcasing its potential in LULC classification compared to previous PlanetScope 4-band imagery. • Performance evaluation of PlanetScope’s SuperDove sensor. • Uses 4-band and 8-band imagery with four supervised classifiers (SVM, ML, RF, and KNN). • The highest accuracy was achieved with 8-band imagery using the SVM classifier, resulting in an overall accuracy of 0.94. • Spectral bands and resolution improve LULC classification and mapping for sustainable land management.
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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) | 0.000 | 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 it