HLWATER V1.0 - Trained Mask R-CNN for surface water mapping in boreal forest-tundra ...
The HLWATER V1.0 is a Mask R-CNN supervised model trained over PlanetScope (including Dove and Dove-R) satellite imagery (Analytic MS – Surface Reflectance) with focus on the automated detection and delineation of small water bodies in the circumpolar North. The model was trained over a dataset of v...
Main Authors: | , , , , , |
---|---|
Format: | Article in Journal/Newspaper |
Language: | unknown |
Published: |
Zenodo
2023
|
Subjects: | |
Online Access: | https://dx.doi.org/10.5281/zenodo.10203553 https://zenodo.org/doi/10.5281/zenodo.10203553 |
id |
ftdatacite:10.5281/zenodo.10203553 |
---|---|
record_format |
openpolar |
spelling |
ftdatacite:10.5281/zenodo.10203553 2024-04-28T08:23:17+00:00 HLWATER V1.0 - Trained Mask R-CNN for surface water mapping in boreal forest-tundra ... Freitas, Pedro Vieira, Gonçalo Canário, João Vincent, Warwick F. Pina, Pedro Mora, Carla 2023 https://dx.doi.org/10.5281/zenodo.10203553 https://zenodo.org/doi/10.5281/zenodo.10203553 unknown Zenodo https://dx.doi.org/10.5281/zenodo.10203552 Creative Commons Attribution 4.0 International https://creativecommons.org/licenses/by/4.0/legalcode cc-by-4.0 Mask R-CNN Deep Learning PlanetScope Water mapping Small water bodies article Model CreativeWork 2023 ftdatacite https://doi.org/10.5281/zenodo.1020355310.5281/zenodo.10203552 2024-04-02T11:52:12Z The HLWATER V1.0 is a Mask R-CNN supervised model trained over PlanetScope (including Dove and Dove-R) satellite imagery (Analytic MS – Surface Reflectance) with focus on the automated detection and delineation of small water bodies in the circumpolar North. The model was trained over a dataset of very-high resolution 23,432 manually delineated lakes, ponds, rivers, streams, creeks and coastal sectors, located in diverse landscapes from the sporadic to the continuous permafrost zones of Canada. The training dataset is freely available upon request to pedro-freitas@edu.uslisboa.pt. Most training features (97%) were water bodies smaller than 1 hectare located in diverse environmental and hydrological settings. HLWATER V1.0 accuracy was tested for Eastern Hudson Bay (Nunavik, Subarctic Canada), a region that comprises a variety of water bodies, environments and permafrost in different stages of degradation. For the evaluation of model performance, a two-scale testing approach was used by comparing with manually ... Article in Journal/Newspaper Hudson Bay permafrost Subarctic Tundra Nunavik DataCite Metadata Store (German National Library of Science and Technology) |
institution |
Open Polar |
collection |
DataCite Metadata Store (German National Library of Science and Technology) |
op_collection_id |
ftdatacite |
language |
unknown |
topic |
Mask R-CNN Deep Learning PlanetScope Water mapping Small water bodies |
spellingShingle |
Mask R-CNN Deep Learning PlanetScope Water mapping Small water bodies Freitas, Pedro Vieira, Gonçalo Canário, João Vincent, Warwick F. Pina, Pedro Mora, Carla HLWATER V1.0 - Trained Mask R-CNN for surface water mapping in boreal forest-tundra ... |
topic_facet |
Mask R-CNN Deep Learning PlanetScope Water mapping Small water bodies |
description |
The HLWATER V1.0 is a Mask R-CNN supervised model trained over PlanetScope (including Dove and Dove-R) satellite imagery (Analytic MS – Surface Reflectance) with focus on the automated detection and delineation of small water bodies in the circumpolar North. The model was trained over a dataset of very-high resolution 23,432 manually delineated lakes, ponds, rivers, streams, creeks and coastal sectors, located in diverse landscapes from the sporadic to the continuous permafrost zones of Canada. The training dataset is freely available upon request to pedro-freitas@edu.uslisboa.pt. Most training features (97%) were water bodies smaller than 1 hectare located in diverse environmental and hydrological settings. HLWATER V1.0 accuracy was tested for Eastern Hudson Bay (Nunavik, Subarctic Canada), a region that comprises a variety of water bodies, environments and permafrost in different stages of degradation. For the evaluation of model performance, a two-scale testing approach was used by comparing with manually ... |
format |
Article in Journal/Newspaper |
author |
Freitas, Pedro Vieira, Gonçalo Canário, João Vincent, Warwick F. Pina, Pedro Mora, Carla |
author_facet |
Freitas, Pedro Vieira, Gonçalo Canário, João Vincent, Warwick F. Pina, Pedro Mora, Carla |
author_sort |
Freitas, Pedro |
title |
HLWATER V1.0 - Trained Mask R-CNN for surface water mapping in boreal forest-tundra ... |
title_short |
HLWATER V1.0 - Trained Mask R-CNN for surface water mapping in boreal forest-tundra ... |
title_full |
HLWATER V1.0 - Trained Mask R-CNN for surface water mapping in boreal forest-tundra ... |
title_fullStr |
HLWATER V1.0 - Trained Mask R-CNN for surface water mapping in boreal forest-tundra ... |
title_full_unstemmed |
HLWATER V1.0 - Trained Mask R-CNN for surface water mapping in boreal forest-tundra ... |
title_sort |
hlwater v1.0 - trained mask r-cnn for surface water mapping in boreal forest-tundra ... |
publisher |
Zenodo |
publishDate |
2023 |
url |
https://dx.doi.org/10.5281/zenodo.10203553 https://zenodo.org/doi/10.5281/zenodo.10203553 |
genre |
Hudson Bay permafrost Subarctic Tundra Nunavik |
genre_facet |
Hudson Bay permafrost Subarctic Tundra Nunavik |
op_relation |
https://dx.doi.org/10.5281/zenodo.10203552 |
op_rights |
Creative Commons Attribution 4.0 International https://creativecommons.org/licenses/by/4.0/legalcode cc-by-4.0 |
op_doi |
https://doi.org/10.5281/zenodo.1020355310.5281/zenodo.10203552 |
_version_ |
1797584331975163904 |