About: Rapid Response Crop Maps in Data Sparse Regions

Facets (new session)
Description
Metadata
Settings
- owl:sameAs
- Inference Rule:

About: Rapid Response Crop Maps in Data Sparse Regions Goto Sponge NotDistinct Permalink

An Entity of Type : schema:ScholarlyArticle, within Data Space : wasabi.inria.fr associated with source document(s)

Attributes	Values
type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
has title	Rapid Response Crop Maps in Data Sparse Regions
Creator	Barker, Brian Becker-Reshef, Inbal Hosseini, Mehdi Kerner, Hannah Munshell, Blake Nakalembe, Catherine Paliyam, Madhava Tseng, Gabriel
Source	ArXiv
abstract	Spatial information on cropland distribution, often called cropland or crop maps, are critical inputs for a wide range of agriculture and food security analyses and decisions. However, high-resolution cropland maps are not readily available for most countries, especially in regions dominated by smallholder farming (e.g., sub-Saharan Africa). These maps are especially critical in times of crisis when decision makers need to rapidly design and enact agriculture-related policies and mitigation strategies, including providing humanitarian assistance, dispersing targeted aid, or boosting productivity for farmers. A major challenge for developing crop maps is that many regions do not have readily accessible ground truth data on croplands necessary for training and validating predictive models, and field campaigns are not feasible for collecting labels for rapid response. We present a method for rapid mapping of croplands in regions where little to no ground data is available. We present results for this method in Togo, where we delivered a high-resolution (10 m) cropland map in under 10 days to facilitate rapid response to the COVID-19 pandemic by the Togolese government. This demonstrated a successful transition of machine learning applications research to operational rapid response in a real humanitarian crisis. All maps, data, and code are publicly available to enable future research and operational systems in data-sparse regions.
has issue date	2020-06-23(xsd:dateTime)
has license	arxiv
sha1sum (hex)	be95d43ffd5677d528595ed90decbd40704019f6
resource representing a document's title	Rapid Response Crop Maps in Data Sparse Regions
resource representing a document's body	covid:be95d43ffd5677d528595ed90decbd40704019f6#body_text
is schema:about of	named entity 'However' named entity 'All' named entity 'sub-Saharan Africa' named entity 'food security' named entity 'ground truth' named entity 'successful' named entity 'PRODUCTIVITY' named entity 'DELIVERED' named entity 'SPARSE' named entity 'FUTURE' named entity 'validating' named entity 'COVID-19' named entity 'accessible' named entity 'aid' named entity 'training' named entity 'regions' named entity 'wide range' named entity 'A major' named entity 'Togo' named entity 'food security' named entity 'humanitarian assistance' named entity 'land cover' named entity 'Togo' named entity 'regularizer' named entity 'food security' named entity 'Geo-Wiki' named entity 'satellite imagery' named entity 'overfitting' named entity 'hyperparameters' named entity 'validation set' named entity 'LSTM' named entity 'Togo' named entity 'Buchhorn' named entity 'Paliyam' named entity 'cloud services' named entity 'Analysis Data' named entity 'pixel' named entity 'pixel' named entity 'Togo' named entity 'pixel' named entity 'Togo' named entity 'Togo' named entity 'pixel' named entity 'pixel' named entity 'LSTM' named entity 'Buchhorn' named entity 'training data' named entity 'food insecurity' named entity 'Togo' named entity 'LSTM' named entity 'decision-making' named entity 'COVID-19' named entity 'posterior probability' named entity 'grid search' named entity 'cloud platforms' named entity 'pixel' named entity 'land cover' named entity 'Google Earth' named entity 'Image Segmentation' named entity 'GEE' named entity 'decision-making' named entity 'Sentinel-2' named entity 'Sentinel-2' named entity 'COVID-19 pandemic' named entity 'pixel' named entity 'aerosol' named entity 'early stopping' named entity 'Togolese government' named entity 'LSTM' named entity 'high-resolution' named entity 'USGS' named entity 'labeled data' named entity 'classifiers' named entity 'normalized difference vegetation index' named entity 'West African' named entity 'Land Cover' named entity 'high-resolution' named entity 'inference' named entity 'software engineering' named entity 'drought'

Faceted Search & Find service v1.13.91 as of Mar 24 2020

Alternative Linked Data Documents: Sponger | ODE Content Formats:

RDF

ODATA

Microdata

About

OpenLink Virtuoso version 07.20.3229 as of Jul 10 2020, on Linux (x86_64-pc-linux-gnu), Single-Server Edition (94 GB total memory)
Data on this page belongs to its respective rights holders.
Virtuoso Faceted Browser Copyright © 2009-2024 OpenLink Software