What is she best known for?
The fields of study she is best known for:
- Statistics
- Artificial intelligence
- Agriculture
Linda See spends much of her time researching Land cover, Land use, Cartography, Data science and Data mining.
Her work carried out in the field of Land cover brings together such families of science as Crowdsourcing, Remote sensing, Urban heat island and Thematic map.
Linda See has included themes like Forest management, Agroforestry, Baseline, Ecosystem services and Deforestation in her Land use study.
The study incorporates disciplines such as Africover, Agricultural land and Data set in addition to Cartography.
Her research in the fields of Volunteered geographic information overlaps with other disciplines such as Resource.
Her studies in Data mining integrate themes in fields like Artificial neural network, Field and Fuzzy logic.
Her most cited work include:
- HydroTest: A web-based toolbox of evaluation metrics for the standardised assessment of hydrological forecasts (307 citations)
- Mapping global cropland and field size (276 citations)
- Mapping local climate zones for a worldwide database of the form and function of cities (242 citations)
What are the main themes of her work throughout her whole career to date?
Her primary scientific interests are in Land cover, Crowdsourcing, Citizen science, Environmental resource management and Data science.
Her Land cover research integrates issues from Cartography, Sample, Data mining and Remote sensing.
Her Data mining study frequently draws connections between adjacent fields such as Artificial neural network.
Her research integrates issues of Rainfall runoff and Hydrological modelling in her study of Artificial neural network.
The concepts of her Crowdsourcing study are interwoven with issues in Quality and Identification.
Her study in Data science focuses on Volunteered geographic information in particular.
She most often published in these fields:
- Land cover (26.95%)
- Crowdsourcing (17.79%)
- Citizen science (16.44%)
What were the highlights of her more recent work (between 2017-2021)?
- Citizen science (16.44%)
- Land cover (26.95%)
- Crowdsourcing (17.79%)
In recent papers she was focusing on the following fields of study:
Her primary areas of investigation include Citizen science, Land cover, Crowdsourcing, Land use and Sustainable development.
Her studies deal with areas such as Change detection, Database, Environmental resource management, Class and Geographic information system as well as Land cover.
Linda See interconnects Urban heat island, Quality, Urban climate and Ecosystem services in the investigation of issues within Environmental resource management.
She has researched Crowdsourcing in several fields, including Identification, Data quality, Remote sensing, Data science and Sample.
She combines subjects such as Environmental monitoring and Volunteered geographic information with her study of Land use.
Her research investigates the link between Sustainable development and topics such as Sustainability that cross with problems in Agricultural extension, Decision support system and Environmental economics.
Between 2017 and 2021, her most popular works were:
- WUDAPT: An Urban Weather, Climate, and Environmental Modeling Infrastructure for the Anthropocene (107 citations)
- Citizen science and the United Nations Sustainable Development Goals (99 citations)
- A comparison of global agricultural monitoring systems and current gaps (73 citations)
In her most recent research, the most cited papers focused on:
- Statistics
- Artificial intelligence
- Agriculture
Linda See mainly investigates Crowdsourcing, Environmental resource management, Land cover, Citizen science and Satellite imagery.
Her Crowdsourcing research is multidisciplinary, incorporating perspectives in Data collection, Identification, Data quality, Remote sensing and Data science.
Her Environmental resource management study integrates concerns from other disciplines, such as Urban heat island, Land use and Urban climate.
With her scientific publications, her incorporates both Land cover and Weighted voting.
Her Satellite imagery research incorporates elements of Cartography, Agriculture, Forest management and Risk analysis.
Her studies examine the connections between Data mining and genetics, as well as such issues in Volunteered geographic information, with regards to Geographic information system.
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