Super resolution enhancement of Landsat imagery and detections of high-latitude lakes
Bibliographic record
Abstract
This archive contains native resolution and super resolution (SR) Landsat imagery, derivative lake shorelines, and previously-published lake shorelines derived airborne remote sensing, used here for comparison. Landsat images are from 1985 (Landsat 5) and 2017 (Landsat 8) and are cropped to study areas used in the corresponding paper and converted to 8-bit format. SR images were created using the model of Lezine et al (2021a, 2021b), which outputs imagery at 10x-finer resolution, and they have the same extent and bit depth as the native resolution scenes included. Reference shoreline datasets are from Kyzivat et al. (2019a and 2019b) for the year 2017 and Walter Anthony et al. (2021a, 2021b) for Fairbanks, AK, USA in 1985. All derived and comparison shoreline datasets are cropped to the same extent, filtered to a common minimum lake size (40 m<sup>2</sup> for 2017; 13 m<sup>2</sup> for 1985), and smoothed via 10 m morphological closing. The SR-derived lakes were determined to have F-1 scores of 0.75 (2017 data) and 0.60 (1985 data) as compared to reference lakes for lakes larger than 500 m2, and accuracy is worse for smaller lakes. More details are in the forthcoming accompanying publication. All raster images are in cloud-optimized geotiff (COG) format (.tif) with file naming shown in <strong>Table 1</strong>. Vector shoreline datasets are in ESRI shapefile format (.shp, .dbf, etc.), and file names use the abbreviations LR for low resolution, SR for high resolution, and GT for “ground truth” comparison airborne-derived datasets. Landsat-5 and Landsat-8 images courtesy of the U.S. Geological Survey For an interactive map demo of these datasets via Google Earth Engine Apps, visit: https://ekyzivat.users.earthengine.app/view/super-resolution-demo <strong>Table 1</strong>: File naming scheme based on region, with some regions requiring two-scene mosaics. <strong>Region</strong> <strong>Landsat ID</strong> <strong>Mosaic name</strong> <strong>Yukon Flats Basin</strong> LC08_L2SP_068014_20170708_20200903_02_T1 LC08_20170708_yflats_cog.tif <strong>“</strong> LC08_L2SP_068013_20170708_20201015_02_T1 “ <strong>Old Crow Flats</strong> LC08_L2SP_067012_20170903_20200903_02_T1 - <strong>Mackenzie River Delta</strong> LC08_L2SP_064011_20170728_20200903_02_T1 LC08_20170728_inuvik_cog.tif <strong>“</strong> LC08_L2SP_064012_20170728_20200903_02_T1 “ <strong>Canadian Shield Margin</strong> LC08_L2SP_050015_20170811_20200903_02_T1 LC08_20170811_cshield-margin_cog.tif <strong>“</strong> LC08_L2SP_048016_20170829_20200903_02_T1 “ <strong>Canadian Shield near Baker Creek</strong> LC08_L2SP_046016_20170831_20200903_02_T1 - <strong>Canadian Shield near Daring Lake</strong> LC08_L2SP_045015_20170723_20201015_02_T1 - <strong>Peace-Athabasca Delta</strong> LC08_L2SP_043019_20170810_20200903_02_T1 - <strong>Prairie Potholes North 1</strong> LC08_L2SP_041021_20170812_20200903_02_T1 LC08_20170812_potholes-north1_cog.tif <strong>“</strong> LC08_L2SP_041022_20170812_20200903_02_T1 “ <strong>Prairie Potholes North 2</strong> LC08_L2SP_038023_20170823_20200903_02_T1 - <strong>Prairie Potholes South</strong> LC08_L2SP_031027_20170907_20200903_02_T1 - <strong>Fairbanks </strong> LT05_L2SP_070014_19850831_20200918_02_T1 - <strong>References:</strong> Kyzivat, E. D., Smith, L. C., Pitcher, L. H., Fayne, J. V., Cooley, S. W., Cooper, M. G., Topp, S. N., Langhorst, T., Harlan, M. E., Horvat, C., Gleason, C. J., & Pavelsky, T. M. (2019b). A high-resolution airborne color-infrared camera water mask for the NASA ABoVE campaign. <em>Remote Sensing</em>, <em>11</em>(18), 2163. https://doi.org/10.3390/rs11182163 Kyzivat, E.D., L.C. Smith, L.H. Pitcher, J.V. Fayne, S.W. Cooley, M.G. Cooper, S. Topp, T. Langhorst, M.E. Harlan, C.J. Gleason, and T.M. Pavelsky. 2019a. ABoVE: AirSWOT Water Masks from Color-Infrared Imagery over Alaska and Canada, 2017. ORNL DAAC, Oak Ridge, Tennessee, USA. https://doi.org/10.3334/ORNLDAAC/1707 Ekaterina M. D. Lezine, Kyzivat, E. D., & Smith, L. C. (2021a). Super-resolution surface water mapping on the Canadian shield using planet CubeSat images and a generative adversarial network. <em>Canadian Journal of Remote Sensing</em>, <em>47</em>(2), 261–275. https://doi.org/10.1080/07038992.2021.1924646 Ekaterina M. D. Lezine, Kyzivat, E. D., & Smith, L. C. (2021b). Super-resolution surface water mapping on the canadian shield using planet CubeSat images and a generative adversarial network. <em>Canadian Journal of Remote Sensing</em>, <em>47</em>(2), 261–275. https://doi.org/10.1080/07038992.2021.1924646 Walter Anthony, K.., Lindgren, P., Hanke, P., Engram, M., Anthony, P., Daanen, R. P., Bondurant, A., Liljedahl, A. K., Lenz, J., Grosse, G., Jones, B. M., Brosius, L., James, S. R., Minsley, B. J., Pastick, N. J., Munk, J., Chanton, J. P., Miller, C. E., & Meyer, F. J. (2021a). Decadal-scale hotspot methane ebullition within lakes following abrupt permafrost thaw. <em>Environ. Res. Lett</em>, <em>16</em>, 35010. https://doi.org/10.1088/1748-9326/abc848 Walter Anthony, K., and P. Lindgren. 2021b. ABoVE: Historical Lake Shorelines and Areas near Fairbanks, Alaska, 1949-2009. ORNL DAAC, Oak Ridge, Tennessee, USA. https://doi.org/10.3334/ORNLDAAC/1859
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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 | 0.001 | 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) | 0.078 | 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".