Linda See

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Linda See is an author.


Only those publications related to wikis are shown here.
Title Keyword(s) Published in Language DateThis property is a special property in this wiki. Abstract R C
Comparing expert and non-expert conceptualisations of the land: An analysis of crowdsourced land cover data Geo-Wiki
Geographically Weighted Kernel
Land Cover
Volunteered Geographical Information (VGI)
Lecture Notes in Computer Science English 2013 This research compares expert and non-expert conceptualisations of land cover data collected through a Google Earth web-based interface. In so doing it seeks to determine the impacts of varying landscape conceptualisations held by different groups of VGI contributors on decisions that may be made using crowdsourced data, in this case to select the best global land cover dataset in each location. Whilst much other work has considered the quality of VGI, as yet little research has considered the impact of varying semantics and conceptualisations on the use of VGI in formal scientific analyses. This study found that conceptualisation of cropland varies between experts and non-experts. A number of areas for further research are outlined. 0 0
Harmonizing and combining existing land cover/land use datasets for cropland area monitoring at the African continental scale Africa
Crop mask
Food security
Remote Sensing English 2013 Mapping cropland areas is of great interest in diverse fields, from crop monitoring to climate change and food security. Recognizing the value of a reliable and harmonized crop mask that entirely covers the African continent, the objectives of this study were to (i) consolidate the best existing land cover/land use datasets, (ii) adapt the Land Cover Classification System (LCCS) for harmonization, (iii) assess the final product, and (iv) compare the final product with two existing datasets. Ten datasets were compared and combined through an expert-based approach in order to create the derived map of cropland areas at 250 m covering the whole of Africa. The resulting cropland mask was compared with two recent cropland extent maps at 1 km: one derived from MODIS and one derived from five existing products. The accuracy of the three products was assessed against a validation sample of 3,591 pixels of 1km regularly distributed over Africa and interpreted using high resolution images, which were collected using the Geo-Wiki tool. The comparison of the resulting crop mask with existing products shows that it has a greater agreement with the expert validation dataset, in particular for places where the cropland represents more than 30% of the area of the validation pixel. 0 0
Geo-Wiki: An online platform for improving global land cover English 2011 Land cover derived from remotely sensed products is an important input to a number of different global, regional and national scale applications including resource assessments and economic land use models. During the last decade three global land cover datasets have been created, i.e. the GLC-2000, MODIS and GlobCover, but comparison studies have shown that there are large spatial discrepancies between these three products. One of the reasons for these discrepancies is the lack of sufficient in-situ data for the development of these products. To address this issue, a crowdsourcing tool called Geo-Wiki has been developed. Geo-Wiki has two main aims: to increase the amount of in-situ land cover data available for training, calibration and validation, and to create a hybrid global land cover map that provides more accurate land cover information than any current individual product. This paper outlines the components that comprise Geo-Wiki and how they are integrated in the architectural design. An overview of the main functionality of Geo-Wiki is then provided along with the current usage statistics and the lessons learned to date, in particular the need to add a mechanism for feedback and interaction as part of community building, and the need to address issues of data quality. The tool is located at 0 0