A crowdsourced global data set for validating built-up surface layers

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

Come valutare giudizi apparentemente simili

In analisi sensoriale dopo l’assaggio bisogna fare di conto. Proprio qui la statistica diventa una disciplina fondamentale per trovare gli indici di sintesi più adeguati. Tra questi lo Scostamento Medio della Media Bipolare

Osservazioni sullo scostamento medio dalla media bipolare

Nel 2005, Walter Maffenini e Michele Zenga hanno introdotto la media bipolare quale nuovo criterio di sintesi per caratteri ordinali. Per media bipolare s’intende una distribuzione che concentra tutta la numerosità dei casi su un’unica modalità (categoria) del carattere o, al massimo, su due modalità contigue ed è coerente con il tradizionale concetto di dominanza statistica basata sulle frequenze retro-cumulate. L’anno successivo, lo stesso Maffenini e Mariangela Zenga hanno esteso la media bipolare alle variabili discrete e hanno introdotto una misura di variabilità per questo tipo di variabili: lo scostamento medio dalla media bipolare. Tale indice si presta a essere utilizzato anche nel caso in cui alle categorie del carattere ordinale siano attribuiti dei punteggi (ranghi). Come per i più comuni indici di variabilità, può essere interessante definire il valore massimo che lo scostamento medio dalla media bipolare può assumere. Ciò richiede preventivamente di definire la distribuzione di massima variabilità, la quale, però, è diversa a seconda che la numerosità dei casi sia pari o dispari. Da tale distribuzione si ricava la corrispondente media bipolare, che risente non solo della numerosità pari o dispari, ma anche del fatto che le modalità del carattere discreto (o i ranghi attribuiti al carattere ordinale) possono, a loro volta, essere pari o dispari. Obiettivo principale del presente lavoro è fornire, ove possibile, un’espressione generale del massimo dello scostamento medio dalla media bipolare e mostrare alcuni esempi che verificano l’utilità d’impiego di questo indice

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

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

A Crowdsourced Global Data Set for Validating Built-up Surface Layers

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). 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). Finally, the file Strata.csv contains the mapping between the grid location and the sampling stratum used in the design of the sample