Salvato in:
| Autori principali: | , , , |
|---|---|
| Natura: | Dataset Open Access |
| Lingua: | en |
| Pubblicazione: |
PANGAEA
2024
|
| Soggetti: | |
| Accesso online: | https://doi.org/10.1594/PANGAEA.972753 |
| Tags: |
Aggiungi Tag
Nessun Tag, puoi essere il primo ad aggiungerne!!
|
| _version_ | 1867171045323046912 |
|---|---|
| author | Chen, Dong Farrell, Sinead L Duncan, Kyle Eun, Jaemin |
| author_facet | Chen, Dong Farrell, Sinead L Duncan, Kyle Eun, Jaemin |
| collection | Datos científicos de ciencias marinas y ambientales |
| contents | This data collection encompasses 1,387 classified LVIS georeferenced images, which include four classes: Ice, Melt Pond, Open Water, and Shadow. The original LVIS images were acquired in July 2022 using a PhaseOne medium-format camera during the ICESat-2 2022 Arctic Summer calibration campaign, with spatial resolution ranging between 0.39 m and 0.5 m. An image screening was conducted prior to the image classification to remove cloudy images from the collection. The image classification was based on the Random Forest algorithm. An accuracy assessment using 20 randomly selected classified images indicated that the classified imagery has an overall accuracy of more than 85%. The classified images can be used for tracking sea ice dynamics over time and for providing reference for the interpretation of altimetry data. |
| format | Dataset Open Access |
| id | pangaea_https___doi_org_10_1594_PANGAEA_972753 |
| institution | PANGAEA |
| language | en |
| publishDate | 2024 |
| publisher | PANGAEA |
| record_format | pangaea |
| spellingShingle | University of Maryland classified LVIS georeferenced imagery of Arctic summer sea ice, version 1 Chen, Dong Farrell, Sinead L Duncan, Kyle Eun, Jaemin Arctic; DATE/TIME; ice; image classication; machine learning; NASA_ICESat-2_Summer_Sea_Ice_Calibraiton_Campaign; Raster graphic, GeoTIFF format; Raster graphic, GeoTIFF format (File Size); remote sensing; Satellite imagery; SATI; Sea ice This data collection encompasses 1,387 classified LVIS georeferenced images, which include four classes: Ice, Melt Pond, Open Water, and Shadow. The original LVIS images were acquired in July 2022 using a PhaseOne medium-format camera during the ICESat-2 2022 Arctic Summer calibration campaign, with spatial resolution ranging between 0.39 m and 0.5 m. An image screening was conducted prior to the image classification to remove cloudy images from the collection. The image classification was based on the Random Forest algorithm. An accuracy assessment using 20 randomly selected classified images indicated that the classified imagery has an overall accuracy of more than 85%. The classified images can be used for tracking sea ice dynamics over time and for providing reference for the interpretation of altimetry data. |
| title | University of Maryland classified LVIS georeferenced imagery of Arctic summer sea ice, version 1 |
| topic | Arctic; DATE/TIME; ice; image classication; machine learning; NASA_ICESat-2_Summer_Sea_Ice_Calibraiton_Campaign; Raster graphic, GeoTIFF format; Raster graphic, GeoTIFF format (File Size); remote sensing; Satellite imagery; SATI; Sea ice |
| url | https://doi.org/10.1594/PANGAEA.972753 |