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Linda See

Researcher at International Institute for Applied Systems Analysis

Publications -  338
Citations -  13633

Linda See is an academic researcher from International Institute for Applied Systems Analysis. The author has contributed to research in topics: Land cover & Crowdsourcing. The author has an hindex of 55, co-authored 312 publications receiving 10755 citations. Previous affiliations of Linda See include International Institute of Minnesota & University College London.

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LACO-WIKI: an open access online portal for land cover validation

TL;DR: A conceptual overview of the LACO-Wiki system is presented and the main validation workflow is described, in which the user uploads the map for validation, creates a validation sample, carries out the sample interpretation and generates a report detailing the accuracy assessment.
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Local Knowledge and Professional Background Have a Minimal Impact on Volunteer Citizen Science Performance in a Land-Cover Classification Task

TL;DR: It is suggested that, at least for simple tasks, the geographical origin of VGI volunteers has little impact on their ability to complete image classifications.
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Global forest management data for 2015 at a 100 m resolution

TL;DR: In this article , a reference data set and a prototype of a globally consistent forest management map with high spatial detail on the most prevalent forest management classes such as intact forests, managed forests with natural regeneration, planted forests, plantation forest (rotation up to 15 years), oil palm plantations, and agroforestry are presented.
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The Value of Citizen Science for Flood Risk Reduction: Cost-benefit Analysis of a Citizen Observatory in the Brenta-Bacchiglione Catchment

TL;DR: In this article, a citizen observatory for flood risk management has been proposed and is currently being implemented in the Brenta-Bacchiglione catchment, where citizens are involved through monitoring water levels and obstructions and providing other relevant information through mobile apps, where data are assimilated with other sensor data in a hydrological-hydraulic model used in early warning.
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Highlighting Current Trends in Volunteered Geographic Information

TL;DR: This Special Issue aims to capture the main trends in VGI research based on 16 original papers, and distinguishes between two main areas, i.e., those that deal with the characteristics of VGI and those focused on applications of V GI.