A global dataset of crowdsourced land cover and land use reference data

Global land cover is an essential climate variable and a key biophysical driver for earth system models. While remote sensing technology, particularly satellites, have played a key role in providing land cover datasets, large discrepancies have been noted among the available products. Global land use is typically more difficult to map and in many cases cannot be remotely sensed. In-situ or ground-based data and high resolution imagery are thus an important requirement for producing accurate land cover and land use datasets and this is precisely what is lacking. Here we describe the global land cover and land use reference data derived from the Geo-Wiki crowdsourcing platform via four campaigns. These global datasets provide information on human impact, land cover disagreement, wilderness and land cover and land use. Hence, they are relevant for the scientific community that requires reference data for global satellite-derived products, as well as those interested in monitoring global terrestrial ecosystems in general

Towards operational validation of annual global land cover maps

Annual global land cover maps (GLC) are being provided by several operational monitoring efforts. However, map validation is lagging, in the sense that the annual land cover maps are often not validated. Concurrently, users such as the climate and land management community require information on the temporal consistency of multi-date GLC maps and stability in their accuracy. In this study, we propose a framework for operational validation of annual global land cover maps using efficient means for updating validation datasets that allow timely map validation according to recommendations in the CEOS Stage-4 validation guidelines. The framework includes a regular update of a validation dataset and continuous map validation. For the regular update of a validation dataset, a partial revision of the validation dataset based on random and targeted rechecking (areas with a high probability of change) is proposed followed by additional validation data collection. For continuous map validation, an accuracy assessment of each map release is proposed including an assessment of stability in map accuracy addressing the user needs on the temporal consistency information of GLC map and map quality. This validation approach was applied to the validation of the Copernicus Global Land Service GLC product (CGLS-LC100). The CGLS-LC100 global validation dataset was updated from 2015 to 2019. The update was done through a partial revision of the validation dataset and an additional collection of sample validation sites. From the global validation dataset, a total of 40% (10% for each update year) was revisited, supplemented by a targeted revision focusing on validation sample locations that were identified as possibly changed using the BFAST time series algorithm. Additionally, 6720 sample sites were collected to represent possible land cover change areas within 2015 and 2019. Through this updating mechanism, we increased the sampling intensity of validation sample sites in possible land cover change areas within the period. Next, the dataset was used to validate the annual GLC maps of the CGLS-LC100 product for 2015–2019. The results showed that the CGLS-LC100 annual GLC maps have about 80% overall accuracy showing high temporal consistency in general. In terms of stability in class accuracy, herbaceous wetland class showed to be the least stable over the period. As more operational land cover monitoring efforts are upcoming, we emphasize the importance of updated map validation and recommend improving the current validation practices and guidelines towards operational map validation so that long-term land cover maps and their uncertainty information are well understood and properly used

A global reference database of crowdsourced cropland data collected using the Geo-Wiki platform

A global reference data set on cropland was collected through a crowdsourcing campaign using the Geo-Wiki crowdsourcing tool. The campaign lasted three weeks, with over 80 participants from around the world reviewing almost 36,000 sample units, focussing on cropland identification. For quality assessment purposes, two additional data sets are provided. The first is a control set of 1,793 sample locations validated by students trained in satellite image interpretation. This data set was used to assess the quality of the crowd as the campaign progressed. The second data set contains 60 expert validations for additional evaluation of the quality of the contributions. All data sets are split into two parts: the first part shows all areas classified as cropland and the second part shows cropland average per location and user. After further processing, the data presented here might be suitable to validate and compare medium and high resolution cropland maps generated using remote sensing. These could also be used to train classification algorithms for developing new maps of land cover and cropland extent

A global reference database of crowdsourced cropland data collected using the Geo-Wiki platform

A global reference data set on cropland was collected through a crowdsourcing campaign using the Geo-Wiki crowdsourcing tool. The campaign lasted three weeks, with over 80 participants from around the world reviewing almost 36,000 sample units, focussing on cropland identification. For quality assessment purposes, two additional data sets are provided. The first is a control set of 1,793 sample locations validated by students trained in satellite image interpretation. This data set was used to assess the quality of the crowd as the campaign progressed. The second data set contains 60 expert validations for additional evaluation of the quality of the contributions. All data sets are split into two parts: the first part shows all areas classified as cropland and the second part shows cropland average per location and user. After further processing, the data presented here might be suitable to validate and compare medium and high resolution cropland maps generated using remote sensing. These could also be used to train classification algorithms for developing new maps of land cover and cropland extent

Developing a community-based worldwide urban morphology and materials database (WUDAPT) using remote sensing and crowdsourcing for improved urban climate modelling

This paper outlines the WUDAPT (World Urban Database and Access Portal Tools) concept and highlights progress to date in developing this database for cities around the world. The next steps in the WUDAPT project are outlined, both in the immediate and longer term. Ultimately the goal is to provide an open access resource on urban morphology, materials and metabolism for all major cities that can be used for many different applications, in particular for climate and weather modelling, and urban climate change studies

Developing and applying a multi-purpose land cover validation dataset for Africa

The production of global land cover products has accelerated significantly over the past decade thanks to the availability of higher spatial and temporal resolution satellite data and increased computation capabilities. The quality of these products should be assessed according to internationally promoted requirements e.g., by the Committee on Earth Observation Systems-Working Group on Calibration and Validation (CEOS-WGCV) and updated accuracy should be provided with new releases (Stage-4 validation). Providing updated accuracies for the yearly maps would require considerable effort for collecting validation datasets. To save time and effort on data collection, validation datasets should be designed to suit multiple map assessments and should be easily adjustable for a timely validation of new releases of land cover products. This study introduces a validation dataset aimed to facilitate multi-purpose assessments and its applicability is demonstrated in three different assessments focusing on validating discrete and fractional land cover maps, map comparison and user-oriented map assessments. The validation dataset is generated primarily to validate the newly released 100m spatial resolution land cover product from the Copernicus Global Land Service (CGLS-LC100). The validation dataset includes 3617 sample sites in Africa based on stratified sampling. Each site corresponds to an area of 100m×100 m. Within site, reference land cover information was collected at 100 subpixels of 10m×10m allowing the land cover information to be suitable for different resolution and legends. Firstly, using this dataset, we validated both the discrete and fractional land cover layers of the CGLS-LC100 product. The CGLS-LC100 discrete map was found to have an overall accuracy of 74.6 ± 2.1% (at 95% confidence level) for the African continent. Fraction cover products were found to have mean absolute errors of 9.3, 8.8, 16.2, and 6.5% for trees, shrubs, herbaceous vegetation and bare ground, respectively. Secondly, for user-oriented map assessment, we assessed the accuracy of the CGLS-LC100 map from four user groups' perspectives (forest monitoring, crop monitoring, biodiversity and climate modelling). Overall accuracies for these perspectives vary between 73.7% ± 2.1% and 93.5% ± 0.9%, depending on the land cover classes of interest. Thirdly, for map comparison, we assessed the accuracy of the Globeland30-2010 map at 30m spatial resolution. Using the subpixel level validation data, we derived 15,252 sample pixels at 30m spatial resolution. Based on these sample pixels, the overall accuracy of the Globeland30-2010 map was found to be 66.6 ± 2.4% for Africa. The three assessments exemplify the applicability of multi-purpose validation datasets which are recommended to increase map validation efficiency and consistency. Assessments of subsequent yearly maps can be conducted by augmenting or updating the dataset with sample sites in identified change areas.JRC.D.6-Knowledge for Sustainable Development and Food Securit

Combining expert-based and crowd-sourcing in a two-tier sampling design for validation: global experimental results for cropland maps

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A global dataset of crowdsourced land cover and land use reference data (2011-2012)

Global land cover is an essential climate variable and a key biophysical driver for earth system models. While remote sensing technology, particularly satellites, have played a key role in providing land cover datasets, large discrepancies have been noted among the available products. Global land use is typically more difficult to map and in many cases cannot be remotely sensed. In-situ or ground-based data and high resolution imagery are thus an important requirement for producing accurate land cover and land use datasets and this is precisely what is lacking. Here we describe the global land cover and land use reference data derived from the Geo-Wiki crowdsourcing platform via four campaigns. These global datasets provide information on human impact, land cover disagreement, wilderness and land cover and land use. Hence, they are relevant for the scientific community that requires reference data for global satellite-derived products, as well as those interested in monitoring global terrestrial ecosystems in general

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