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3 results on '"Magori, B."'

1. A Crowdsourced Global Data Set for Validating Built-up Surface Layers V.2

Author

See, L., Georgieva, I., Duerauer, M., Kemper, T., Corbane, C., Maffenini, L., Gallego, J., Pesaresi, M., Sirbu, F., Ahmed, R., Blyshchyk, K., Magori, B., Blyshchyk, V., Melnyk, O., Zadorozhniuk, R., Mandici, M.-T., Su, Y.-F., Rabia, A.H., Pérez-Hoyos, A., Vasylyshyn, R., Pawe, C.K., Bilous, S., Kovalevskyi, S.B., Kovalevskyi, S.S., Bordoloi, K., Bilous, A., Panging, K., Bilous, V., Prestele, R., Sahariah, D., Deka, A., Nath, N., Neves, R., Myroniuk, V., Karner, M., and Fritz, S.

Abstract

This collection contains data that were collected during a crowdsourcing campaign using Geo-Wiki (https://www.geo-wiki.org/). The campaign involved visual interpretation of a sample that is designed for validating any existing global built-up surface product. A zipped shapefile (ValidationGrids.zip) contains the random stratified sample of 50K locations, which consist of 80x80m grids further sub-divided into 10m cells so there are 64 cells per grid. These locations were provided to the crowd, who used very high-resolution satellite images to label the grids as built-up (i.e., containing a building), non-built-up or unsure. The file (Geo-WikiBuilt-upCentroidsAll.csv) contains the data collected in the campaign summarized by the centroid (or central point of each 80m grid location). It also contains fields for quality control, one that indicates if the change information matches the control points (see below) or the majority answer from the crowd, and another that indicates whether the presence/absence of built-up matches the control points (see below) or the majority answer from the crowd. The data collected for all 64 cells per grid can be found in Geo-WikiBuilt-upCellsAll.csv. The Geo-Wiki campaign uses visually interpreted grid locations called control points as part of the scoring mechanism of Geo-Wiki for quality control. These control points are provided by centroid (Geo-WikiBuilt-upCentroidsControls.csv) and for all cells in the 80m grid (Geo-WikiBuilt-upCellsControls.csv). In addition to the raw data, two additional quality-controlled files have been produced. The first file (Geo-WikiBuilt-upCentroidsChangeQualityControlled.csv) provides a single record for each location on change in built-up (if built-up is present) that lists either the control point answer or the majority answer from the crowd. The second file (Geo-WikiBuilt-upCellsQualityControlled.csv) contains a single record for each of the 64 cells in each grid, listing either the control point answer or the majority answer from the crowd. Finally, the file Strata.csv contains the mapping between the grid location and the sampling stratum used in the design of the sample.

Published
2021
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2. Drivers of tropical forest loss between 2008 and 2019.

Author

Laso Bayas JC, See L, Georgieva I, Schepaschenko D, Danylo O, Dürauer M, Bartl H, Hofhansl F, Zadorozhniuk R, Burianchuk M, Sirbu F, Magori B, Blyshchyk K, Blyshchyk V, Rabia AH, Pawe CK, Su YF, Ahmed M, Panging K, Melnyk O, Vasylyshyn O, Vasylyshyn R, Bilous A, Bilous S, Das K, Prestele R, Pérez-Hoyos A, Bungnamei K, Lashchenko A, Lakyda M, Lakyda I, Serediuk O, Domashovets G, Yurchuk Y, Koper M, and Fritz S

Abstract

During December 2020, a crowdsourcing campaign to understand what has been driving tropical forest loss during the past decade was undertaken. For 2 weeks, 58 participants from several countries reviewed almost 115 K unique locations in the tropics, identifying drivers of forest loss (derived from the Global Forest Watch map) between 2008 and 2019. Previous studies have produced global maps of drivers of forest loss, but the current campaign increased the resolution and the sample size across the tropics to provide a more accurate mapping of crucial factors leading to forest loss. The data were collected using the Geo-Wiki platform ( www.geo-wiki.org ) where the participants were asked to select the predominant and secondary forest loss drivers amongst a list of potential factors indicating evidence of visible human impact such as roads, trails, or buildings. The data described here are openly available and can be employed to produce updated maps of tropical drivers of forest loss, which in turn can be used to support policy makers in their decision-making and inform the public., (© 2022. The Author(s).)

Published
2022
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3. A crowdsourced global data set for validating built-up surface layers.

Author

See L, Georgieva I, Duerauer M, Kemper T, Corbane C, Maffenini L, Gallego J, Pesaresi M, Sirbu F, Ahmed R, Blyshchyk K, Magori B, Blyshchyk V, Melnyk O, Zadorozhniuk R, Mandici MT, Su YF, Rabia AH, Pérez-Hoyos A, Vasylyshyn R, Pawe CK, Bilous S, Kovalevskyi SB, Kovalevskyi SS, Bordoloi K, Bilous A, Panging K, Bilous V, Prestele R, Sahariah D, Deka A, Nath N, Neves R, Myroniuk V, Karner M, and Fritz S

Abstract

Several global high-resolution built-up surface products have emerged over the last five years, taking full advantage of open sources of satellite data such as Landsat and Sentinel. However, these data sets require validation that is independent of the producers of these products. To fill this gap, we designed a validation sample set of 50 K locations using a stratified sampling approach independent of any existing global built-up surface products. We launched a crowdsourcing campaign using Geo-Wiki ( https://www.geo-wiki.org/ ) to visually interpret this sample set for built-up surfaces using very high-resolution satellite images as a source of reference data for labelling the samples, with a minimum of five validations per sample location. Data were collected for 10 m sub-pixels in an 80 × 80 m grid to allow for geo-registration errors as well as the application of different validation modes including exact pixel matching to majority or percentage agreement. The data set presented in this paper is suitable for the validation and inter-comparison of multiple products of built-up areas., (© 2022. The Author(s).)

Published
2022
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