Search

The Global Navigation Satellite System (GNSS) is a key asset for tropospheric monitoring. Currently, GNSS meteorology relies primarily on geodetic-grade stations. However, such stations are too costly to be densely deployed, which limits the contribution of GNSS to tropospheric monitoring. In 2016, Google released the raw GNSS measurement application programming interface for smartphones running on Android version 7.0 and higher. Given that nowadays there are billions of Android smartphones worldwide, utilizing those devices for atmospheric monitoring represents a remarkable scientific opportunity. In this study, smartphone GNSS data collected in Germany as part of the Application of Machine Learning Technology for GNSS IoT Data Fusion (CAMALIOT) crowdsourcing campaign in 2022 were utilized to investigate this idea. Approximately 20 000 raw GNSS observation files were collected there during the campaign. First, a dedicated data processing pipeline was established that consists of two major parts: machine learning (ML)-based data selection and ionosphere-free precise point positioning (PPP)-based zenith total delay (ZTD) estimation. The proposed method was validated with a dedicated smartphone data collection experiment conducted on the rooftop of the ETH campus. The results confirmed that ZTD estimates of millimeter-level precision could be achieved with smartphone data collected in an open-sky environment. The impacts of observation time span and utilization of multi-GNSS observations on ZTD estimation were also investigated. Subsequently, the crowdsourced data from Germany were processed by PPP with the ionospheric delays interpolated using observations from surrounding satellite positioning service of the German National Survey (SAPOS) GNSS stations. The ZTDs derived from ERA5 and an ML-based ZTD product served as benchmarks. The results revealed that an accuracy of better than 10 mm can be achieved by utilizing selected high-quality crowdsourced smartphone data.

To achieve the goals of the 2030 Global Biodiversity Framework, the European Biodiversity Strategy, and the EU Green Deal, biodiversity monitoring is critical. Monitoring efforts in Europe, however, suffer from gaps and biases in taxonomy, spatial coverage, and temporal resolution, resulting in fragmented and disconnected data. To assess user and policy needs in biodiversity monitoring, we employed a four-step user-centered stakeholder engagement process with over 300 stakeholders including a public stakeholder workshop, online survey, interviews, and a meeting with experts from 18 EU member states, the European Commission, and the European Environment Agency. The stakeholders identified policy needs, current challenges, and potential solutions. Based on the policy and stakeholder assessment, we recommend establishing a European Biodiversity Observation Coordinating Centre to optimize existing observation efforts, harmonize data, and enhance our ability to predict and respond to key challenges related to biodiversity loss in Europe.

The scarcity of long-term observational data has limited the use of statistical or machine-learning techniques for predicting intraannual ecological variation. However, time-stamped citizen-science observation records, supported by media data such as photographs, are increasingly available. In the present article, we present a novel framework based on the concept of relative phenological niche, using machine-learning algorithms to model observation records as a temporal sample of environmental conditions in which the represented ecological phenomenon occurs. Our approach accurately predicts the temporal dynamics of ecological events across large geographical scales and is robust to temporal bias in recording effort. These results highlight the vast potential of citizen-science observation data to predict ecological phenomena across space, including in near real time. The framework is also easily applicable for ecologists and practitioners already using machine-learning and statistics-based predictive approaches.

The global spatial extent of croplands is a crucial input to global and regional agricultural monitoring and modeling systems. Although many new remotely-sensed products are now appearing due to recent advances in the spatial and temporal resolution of satellite sensors, there are still issues with these products that are related to the definition of cropland used and the accuracies of these maps, particularly when examined spatially. To address the needs of the agricultural monitoring community, here we have created a hybrid map of global cropland extent at a 500 m resolution by fusing two of the latest high resolution remotely-sensed cropland products: the European Space Agency's WorldCereal and the cropland layer from the University of Maryland. We aggregated the two products to a common resolution of 500 m to produce percentage cropland and compared them spatially, calculating two kinds of disagreement: density disagreement, where the two maps differ by more than 80%, and absence-presence of cropland disagreement, where one map indicates the presence of cropland while the other does not. Based on these disagreements, we selected continuous areas of disagreement, referred to in the paper as hotspots of disagreement, for manual correction by experts using the Geo-Wiki land cover application. The hybrid map was then validated using a stratified random sample based on the disagreement layer, where the sample was visually interpreted by a different set of experts using Geo-Wiki. The results show that the hybrid product improves upon the overall accuracy statistics in the areas where the underlying cropland layer from the University of Maryland was improved with the WorldCereal product, but more importantly, it represents an improved spatially explicit cropland mask for early warning and food security assessment purposes.

In this report, we present the analysis of the different available biodiversity data streams at the EU and national level, both baseline biodiversity data and monitoring data. We assess how these biodiversity data inform and trigger policy action and identify the related challenges the different European countries and relevant EU agencies face and the solutions to overcome them. To do this, we consulted with more than 350 expert stakeholders from policy, research and practice. The assessment identified a fragmented biodiversity data landscape that cannot currently easily answer all relevant policy questions. Quantity and quality of biodiversity baseline datasets differ for the different countries, ranging from non-existent biodiversity monitoring due to capacity issues, to regular monitoring of ecosystem processes and state. By engaging stakeholders and experts in both member states and non-member states and from several EU bodies, we identified key challenges and ways to address these with targeted solutions towards building a joint European Biodiversity Monitoring Network. Solutions include focussing on cooperation and coordination, enhanced data standardisation and sharing, as well as the use of models and new technologies. These solutions can however only be realised with dedicated funding and capacity building, in coordination with all stakeholders in partnership.

Observations are key to understanding the state of nature, the drivers of biodiversity loss and the impacts on ecosystem services and ultimately on people. Many EU policies and initiatives call for unbiased, integrated and regularly updated data on biodiversity and ecosystem services. However, biodiversity monitoring efforts are spatially and temporally fragmented, taxonomically biased and not integrated across Europe. EuropaBON has addressed this gap by developing an EU-wide framework for biodiversity monitoring. With this deliverable, EuropaBON proposes the terms of reference for an EU Biodiversity Observation Coordination Centre (EBOCC), a permanent infrastructure that could coordinate and foster the generation and use of high quality data to underpin the biodiversity knowledge-base used across EU policies, providing guidance and trainings when necessary. Such a centre represents one of the key solutions to overcome the critical challenges of biodiversity monitoring in Europe. Having this integrated and continuous monitoring capacity would allow more timely and efficient interventions that would optimise our capacity to revert biodiversity loss and prevent environmental degradation. It would also increase the value-added to the data flows, reaching high-value outputs with some existing low-value inputs. This deliverable offers a critical analysis of the existing monitoring landscape in Europe, extracting key messages about the main challenges, lessons learned and possible solutions. Based on a comprehensive analysis of needs and, most importantly, on an inclusive consultation process, the deliverable designs an EBOCC that tackles the key biodiversity monitoring challenges. The proposal specifies the mission, the tasks, the most urgent topics, the main policies and the key stakeholders that the EBOCC should serve and focus on during the first stage of its implementation. It also includes detailed analyses about governance models and potential costs. With this prop

The era of modern smartphones, running on Android version 7.0 and higher, facilitates nowadays acquisition of raw dual-frequency multi-constellation GNSS observations. This paves the way for GNSS community data to be potentially exploited for precise positioning, GNSS reflectometry or geoscience applications at large. The continuously expanding global GNSS infrastructure along with the enormous volume of prospective GNSS community data bring, however, major challenges related to data acquisition, its storage, and subsequent processing for deriving various parameters of interest. In addition, such large datasets cannot be managed manually anymore, leading thus to the need for fully automated and sophisticated data processing pipelines. Application of Machine Learning Technology for GNSS IoT data fusion (CAMALIOT) was an ESA NAVISP Element 1 project (NAVISP-EL1-038.2) with activities aiming to address the aforementioned points related to GNSS community data and their exploitation for scientific applications with the use of Machine Learning (ML). This contribution provides an overview of the CAMALIOT project with information on the designed and implemented cloud-native software for GNSS processing and ML at scale, developed Android application for retrieving GNSS observations from the modern generation of smartphones through dedicated crowdsourcing campaigns, related data ingestion and processing, and GNSS analysis concerning both conventional and smartphone GNSS observations. With the use of the developed GNSS engine employing an Extended Kalman Filter, example processing results related to the Zenith Total Delay (ZTD) and Slant Total Electron Content (STEC) are provided based on the analysis of observations collected with geodetic-grade GNSS receivers and from local measurement sessions involving Xiaomi Mi 8 that collected GNSS observations using the developed Android application. For smartphone observations, ZTD is derived in a differential manner based on a single

The creation of crop type maps from satellite data has proven challenging and is often impeded by a lack of accurate in situ data. Street-level imagery represents a new potential source of in situ data that may aid crop type mapping, but it requires automated algorithms to recognize the features of interest. This paper aims to demonstrate a method for crop type (i.e., maize, wheat and others) recognition from street-level imagery based on a convolutional neural network using a bottom-up approach. We trained the model with a highly accurate dataset of crowdsourced labelled street-level imagery using the Picture Pile application. The classification results achieved an AUC of 0.87 for wheat, 0.85 for maize and 0.73 for others. Given that wheat and maize are two of the most common food crops grown globally, combined with an ever-increasing amount of available street-level imagery, this approach could help address the need for improved global crop type monitoring. Challenges remain in addressing the noise aspect of street-level imagery (i.e., buildings, hedgerows, automobiles, etc.) and uncertainties due to differences in the time of day and location. Such an approach could also be applied to developing other in situ data sets from street-level imagery, e.g., for land use mapping or socioeconomic indicators.

In this report, we present the analysis of the different available biodiversity data streams at the EU and national level, both baseline biodiversity data and monitoring data. We assess how these biodiversity data inform and trigger policy action and identify the related challenges the different European countries and relevant EU agencies face and the solutions to overcome them. To do this, we consulted with more than 350 expert stakeholders from policy, research and practice. The assessment identified a fragmented biodiversity data landscape that cannot currently easily answer all relevant policy questions. Quantity and quality of biodiversity baseline datasets differ for the different countries, ranging from non-existent biodiversity monitoring due to capacity issues, to regular monitoring of ecosystem processes and state. By engaging stakeholders and experts in both member states and non-member states and from several EU bodies, we identified key challenges and ways to address these with targeted solutions towards building a joint European Biodiversity Monitoring Network. Solutions include focussing on cooperation and coordination, enhanced data standardisation and sharing, as well as the use of models and new technologies. These solutions can however only be realised with dedicated funding and capacity building, in coordination with all stakeholders in partnership.

In order to assess user and policy needs related to biodiversity monitoring and data, the EuropaBON project invited European experts to fill in this survey in August 2021. The survey was tailored to the European level and aimed at identifying current monitoring efforts, data flows from collection to reporting, data uptake by policymaking, challenges and roadblocks, as well as biodiversity variables and indicators for a desirable future. On behalf of the European Commission (DG Environment) and EuropaBON, the surveys were sent out to key European agencies (e.g., DG ENV, DG AGRI, DG CLIMA, EEA, Eurostat, Biodiversa+). The results of our surveys form the basis for the analysis of the EuropaBON User and Policy Needs Assessment (DOI) and inform the design of the Europa Biodiversity Observation Network and its work programme. We published the original responses of survey sections A and B in the annex of the User and Policy Needs Assessment. Responses of section C were synthesised in the assessment.

In order to assess user and policy needs related to biodiversity monitoring and data, the EuropaBON project invited national experts to fill in this survey in August 2021. The survey was tailored to the national level and aimed at identifying current monitoring efforts, data flows from collection to reporting, data uptake by policymaking, challenges and roadblocks, as well as biodiversity variables and indicators for a desirable future. On behalf of the European Commission (DG Environment) and EuropaBON, the surveys were sent out to all national focal points of the European Environment Information and Observation Network (Eionet) as well as key national agencies. Eionet is a partnership network of the European Environment Agency (EEA) and its 38 member and cooperating countries to gather and develop data, knowledge, and advice to policy makers about Europe's environment. The results of our surveys form the basis for the analysis of the EuropaBON User and Policy Needs Assessment (DOI) and inform the design of the Europa Biodiversity Observation Network and its work programme. We published the original responses of survey sections A and B in the annex of the User and Policy Needs Assessment. Responses of section C were synthesised in the assessment.

The accumulation of plastic litter in marine environments is a major environmental challenge along with the difficulties in their measurement because of the massive size of the oceans and vast circulation of plastic litter, which is being addressed as part of the United Nations (UN) Sustainable Development Goals (SDGs). Citizen science, public participation in scientific research and knowledge production, represents a potential source of data for SDG monitoring and reporting of marine plastic litter, yet there has been no evidence of its use to date. Here, we show how Ghana has become the first country to integrate existing citizen science data on marine plastic litter in their official monitoring and reporting of SDG indicator 14.1.1b for the years 2016–2020, which has also helped to bridge local data collection efforts with global monitoring processes and policy agendas by leveraging the SDG framework. The results have been used in Ghana’s 2022 Voluntary National Review of the SDGs, and reported on the UN SDG Global Database, as well as helping to inform relevant policies in Ghana. In addition, here, we present a pathway that can be adopted by the relevant government authorities in other countries that have an interest in following a similar citizen science data validation and reporting process for this indicator and potentially others.

The Global Navigation Satellite System (GNSS) is a well-recognized tool to probe the Earth’s atmosphere. This contribution highlights how GNSS data collected from smartphones of voluntary contributors can be used to determine parameters of the troposphere and ionosphere. In this regard, the application of machine learning (ML) to characterize the quality of the crowd-sourced data and model atmospheric parameters is discussed. We demonstrate that in certain cases, GNSS data from smartphones can reach a precision that would allow such data to densify observations from existing geodetic infrastructures.

The Amazon forests act as a global reserve for carbon, have very high biodiversity, and provide a variety of additional ecosystem services. These forests are, however, under increasing pressure, coming mainly from deforestation, despite the fact that accurate satellite monitoring is in place that produces annual deforestation maps and timely alerts. Here, we present a proof of concept for rapid deforestation monitoring that engages the global community directly in the monitoring process via crowdsourcing while subsequently leveraging the power of deep learning. Offering no tangible incentives, we were able to sustain participation from more than 5500 active contributors from 96 different nations over a 6-month period, resulting in the crowd classification of 43,108 satellite images (representing around 390,000 km2). Training a suite of AI models with results from the crowd, we achieved an accuracy greater than 90% in detecting new and existing deforestation. These findings demonstrate the potential of a crowd–AI approach to rapidly detect and validate deforestation events. Our method directly engages a large, enthusiastic, and increasingly digital global community who wish to participate in the stewardship of the global environment. Coupled with existing monitoring systems, this approach could offer an additional means of verification, increasing confidence in global deforestation monitoring.

Financial factors are among the most widely cited bottlenecks around biodiversity monitoring but are relatively poorly studied, compared to monitoring methodologies. The existing body of literature on the cost-effectiveness of monitoring focuses heavily on the hypothetical costs of generating data rather than the practical realities of undertaking and managing monitoring. To address this we used a combination of surveys and semi-structured interviews with 67 biodiversity monitoring managers to provide an in-depth exploration of 1) what are the main rivers of their costs, 2) how different factors affect their cost-effectiveness in generating biodiversity monitoring data, 3) What is the scale and economic value of volunteer labor and 4) what are the main cost bottlenecks and spending priorities. Analysis of these responses demonstrates that monitoring efforts are able to generate more data at a lower cost when they have a) higher numbers of volunteers, b) greater densities of sites and c) monitor a wider range of taxa and habitats. Volunteer labour was worth millions of Euros to these organizations, sometimes more than their total costs. Total budgets and volunteer recruitment and retention were the main cost related bottlenecks among respondents, while staff recruitment was the highest priority for new spending. The results and discussion around them highlight the challenges faced by biodiversity monitoring organizations, particularly in recruiting and retaining qualified staff for the long-term. We produce a series of nine key messages and six recommendations for policy-markers and funders going forward.

Raw observations (carrier-phase and code observations) from the Global Navigation Satellite System (GNSS) can now be accessed from Android mobile phones (Version 7.0 onwards). This paves the way for GNSS data to be utilized for low-cost precise positioning or in ionospheric or tropospheric applications. This paper presents results from data collection campaigns using the CAMALIOT mobile app. In the first campaign, 116.3 billion measurements from 11,828 mobile devices were collected from all continents. Although participation decreased during the second campaign, data are still being collected globally. In this contribution, we demonstrate the potential of volunteered geographic information (VGI) from mobile phones to fill data gaps in geodetic station networks that collect GNSS data, e.g. in Brazil, but also how the data can provide a denser set of observations than current networks in countries across Europe. We also show that mobile phones capable of dual-frequency reception, which is an emerging technology that can provide a richer source of GNSS data, are contributing in a substantial way. Finally, we present the results from a survey of participants to indicate that participation is diverse in terms of backgrounds and geography, where the dominant motivation for participation is to contribute to scientific research.

It is becoming increasingly obvious that in order to address current global challenges and achieve the SDGs in the land-use sector, monitoring and evaluation using remote sensing technologies are essential. In particular, with the Copernicus program of the European Union, unprecedented free and open Earth observation data are becoming available. However, in order to improve our remotely sensed based machine learning models, training data in the form of in-situ or annotated land-use or land cover data which are based on the visual interpretation of aerial photographs or very high resolution satellite data are of utmost importance. Without sufficient training data, many land-use and land cover maps lack sufficient quality. The presentation will provide an overview of existing and open in-situ data in the field of land-use science. It will highlight what land-use data are currently available including data collected though crowdsourcing and the Geo-Wiki toolbox. In particular, it will provide insights into current gaps in land cover, land-use, livestock, forest as well as crop type information globally. It will draw on existing global data products such as those from the Copernicus global land monitoring service, and more recently generated products such as WorldCover and WorldCereal. Furthermore, tools to close those data gaps will be shown. The presentation will furthermore explore current obstacles and limitations to data sharing and debunk current arguments that are often put forth for not sharing in-situ data. These arguments include limited resources, quality issues, competition, as well as time constraints, etc. Specific attention will be given to the role of doners and funders in more clearly defining open and FAIR requirements for in-situ data. The presentation will close by making the audience aware of the LUCKINet consortium, which is trying to make more reference data openly accessible and to build a consistent global land-use change dataset as well as work

Current biodiversity monitoring in Europe is fragmented and often inconsistent across countries. Monitoring efforts face multiple challenges including insufficient technical and human capacity, limited funding, data unavailability, and lack of long-term policy support. To address these challenges, the Europa Biodiversity Observation Network (EuropaBON) proposes five clusters of solutions to improve the collection and uptake of policy-relevant biodiversity data: - Enhance coordination and collaboration of monitoring efforts by identifying priorities, using standardized protocols, and aligning reporting requirements with specific indicators. - Increase data standardization through the combination of Essential Biodiversity and Essential Ecosystem Services Variables (EBVs and EESVs) with dedicated data sharing and exchange mechanisms that adhere to open and FAIR (findable, accessible, interoperable, and reusable) principles. - Leverage modeling efforts and new technologies to integrate with traditional monitoring methods. - Enable additional, consistent, and long-term financial resources for monitoring efforts, including more and better coordination across countries, institutions, and sectors, along with privatesector investments. - Expand, adapt, and deliver capacity building, such as expert training, creating new exchange knowledge platforms, and embracing citizen science initiatives. We propose the creation of a European Biodiversity Monitoring Coordination Centre in the near future to implement lasting improvements in the collection, analysis, reporting, and political uptake of biodiversity data in all European countries.

Observations are key to understand the drivers of biodiversity loss, and the impacts on ecosystem services and ultimately on people. Many EU policies and initiatives demand unbiased, integrated and regularly updated biodiversity and ecosystem service data. However, efforts to monitor biodiversity are spatially and temporally fragmented, taxonomically biased, and lack integration in Europe. EuropaBON aims to bridge this gap by designing an EU-wide framework for monitoring biodiversity and ecosystem services. EuropaBON harnesses the power of modelling essential variables to integrate different reporting streams, data sources, and monitoring schemes. These essential variables provide consistent knowledge about multiple dimensions of biodiversity change across space and time. They can then be analyzed and synthesized to support decision-making at different spatial scales, from the sub-national to the European scale, through the production of indicators and scenarios. To develop essential biodiversity and ecosystem variables workflows that are policy relevant, EuropaBON is built around stakeholder engagement and knowledge exchange (WP2). EuropaBON will work with stakeholders to identify user and policy needs for biodiversity monitoring and investigate the feasibility of setting up a center to coordinate monitoring activities across Europe (WP2). Together with stakeholders, EuropaBON will assess current monitoring efforts to identify gaps, data and workflow bottlenecks, and analyse cost-effectiveness of different schemes (WP3). This will be used to co-design improved monitoring schemes using novel technologies to become more representative temporally, spatially and taxonomically, delivering multiple benefits to users and society (WP4). Finally, EuropaBON will demonstrate in a set of showcases how workflows tailored to the Birds Directive, Habitats Directive, Water Framework Directive, Climate and Restoration Policy, and the Bioeconomy Strategy, can be implemented (WP5).

In this report, we present the analysis of the different available biodiversity data streams at the EU and national level, both baseline biodiversity data and monitoring data. We assess how these biodiversity data inform and trigger policy action and identify the related challenges the different European countries and relevant EU agencies face and the solutions to overcome them. To do this, we consulted with more than 350 expert stakeholders from policy, research and practice. The assessment identified a fragmented biodiversity data landscape that cannot currently easily answer all relevant policy questions. Quantity and quality of biodiversity baseline datasets differ for the different countries, ranging from non-existent biodiversity monitoring due to capacity issues, to regular monitoring of ecosystem processes and state. By engaging stakeholders and experts in both member states and non-member states and from several EU bodies, we identified key challenges and ways to address these with targeted solutions towards building a joint European Biodiversity Monitoring Network. Solutions include focussing on cooperation and coordination, enhanced data standardisation and sharing, as well as the use of models and new technologies. These solutions can however only be realised with dedicated funding and capacity building, in coordination with all stakeholders in partnership.

It is well established that nighttime radiance, measured from satellites, correlates with economic prosperity across the globe. In developing countries, areas with low levels of detected radiance generally indicate limited development – with unlit areas typically being disregarded. Here we combine satellite nighttime lights and the world settlement footprint for the year 2015 to show that 19% of the total settlement footprint of the planet had no detectable artificial radiance associated with it. The majority of unlit settlement footprints are found in Africa (39%), rising to 65% if we consider only rural settlement areas, along with numerous countries in the Middle East and Asia. Significant areas of unlit settlements are also located in some developed countries. For 49 countries spread across Africa, Asia and the Americas we are able to predict and map the wealth class obtained from ~2,400,000 geo-located households based upon the percent of unlit settlements, with an overall accuracy of 87%.

This dataset updates the global-scale mining polygons (Version 1) available from https://doi.org/10.1594/PANGAEA.910894. It contains 44,929 polygon features, covering 101,583 km² of land used by the global mining industry, including large-scale and artisanal and small-scale mining. The polygons cover all ground features related to mining, .e.g open cuts, tailing dams, waste rock dumps, water ponds, processing infrastructure, and other land cover types related to the mining activities. The data was derived using a similar methodology as the first version by visual interpretation of satellite images. The study area was limited to a 10 km buffer around the 34,820 mining coordinates reported in the S&P metals and mining database. We digitalized the mining areas using the 2019 Sentinel-2 cloudless mosaic with 10 m spatial resolution (https://s2maps.eu by EOX IT Services GmbH - Contains modified Copernicus Sentinel data 2019). We also consulted Google Satellite and Microsoft Bing Imagery, but only as additional information to help identify land cover types linked to the mining activities. The main data set consists of a GeoPackage (GPKG) file, including the following variables: ISO3_CODE, COUNTRY_NAME, AREA in squared kilometres, FID with the feature ID, and geom in geographical coordinates WGS84. The summary of the mining area per country is available in comma-separated values (CSV) file, including the following variables: ISO3_CODE, COUNTRY_NAME, AREA in squared kilometres, and N_FEATURES number of mapped features. Grid data sets with the mining area per cell were derived from the polygons. The grid data is available at 30 arc-second resolution (approximately 1x1 km at the equator), 5 arc-minute (approximately 10x10 km at the equator), and 30 arc-minute resolution (approximately 55x55 km at the equator). We performed an independent validation of the mining data set using control points. For that

The growing demand for minerals has pushed mining activities into new areas increasingly affecting biodiversity-rich natural biomes. Mapping the land use of the global mining sector is, therefore, a prerequisite for quantifying, understanding and mitigating adverse impacts caused by mineral extraction. This paper updates our previous work mapping mining sites worldwide. Using visual interpretation of Sentinel-2 images for 2019, we inspected more than 34,000 mining locations across the globe. The result is a global-scale dataset containing 44,929 polygon features covering 101,583 km2 of large-scale as well as artisanal and small-scale mining. The increase in coverage is substantial compared to the first version of the dataset, which included 21,060 polygons extending over 57,277 km2. The polygons cover open cuts, tailings dams, waste rock dumps, water ponds, processing plants, and other ground features related to the mining activities. The dataset is available for download from https://doi.org/10.1594/PANGAEA.942325 and visualisation at www.fineprint.global/viewer .

Spatially explicit information on forest management at a global scale is critical for understanding the status of forests, for planning sustainable forest management and restoration, and conservation activities. Here, we produce the first reference data set and a prototype of a globally consistent forest management map with high spatial detail on the most prevalent forest management classes such as intact forests, managed forests with natural regeneration, planted forests, plantation forest (rotation up to 15 years), oil palm plantations, and agroforestry. We developed the reference dataset of 226 K unique locations through a series of expert and crowdsourcing campaigns using Geo-Wiki (https://www.geo-wiki.org/). We then combined the reference samples with time series from PROBA-V satellite imagery to create a global wall-to-wall map of forest management at a 100 m resolution for the year 2015, with forest management class accuracies ranging from 58% to 80%. The reference data set and the map present the status of forest ecosystems and can be used for investigating the value of forests for species, ecosystems and their services.

Involving members of the public in image classification tasks that can be tricky to automate is increasingly recognized as a way to complete large amounts of these tasks and promote citizen involvement in science. While this labor is usually provided for free, it is still limited, making it important for researchers to use volunteer contributions as efficiently as possible. Using volunteer labor efficiently becomes complicated when individual tasks are assigned to multiple volunteers to increase confidence that the correct classification has been reached. In this paper, we develop a system to decide when enough information has been accumulated to confidently declare an image to be classified and remove it from circulation. We use a Bayesian approach to estimate the posterior distribution of the mean rating in a binary image classification task. Tasks are removed from circulation when user-defined certainty thresholds are reached. We demonstrate this process using a set of over 4.5 million unique classifications by 2783 volunteers of over 190,000 images assessed for the presence/absence of cropland. If the system outlined here had been implemented in the original data collection campaign, it would have eliminated the need for 59.4% of volunteer ratings. Had this effort been applied to new tasks, it would have allowed an estimated 2.46 times as many images to have been classified with the same amount of labor, demonstrating the power of this method to make more efficient use of limited volunteer contributions. To simplify implementation of this method by other investigators, we provide cutoff value combinations for one set of confidence levels.

The development of remotely sensed products such as land cover requires large amounts of high-quality reference data, needed to train remote sensing classification algorithms and for validation. However, due to the lack of sharing and the high costs associated with data collection, particularly ground-based information, the amount of reference data available has not kept up with the vast increase in the availability of satellite imagery, e.g. from Landsat, Sentinel and Planet satellites. To fill this gap, the Geo-Wiki platform for the crowdsourcing of reference data was developed, involving visual interpretation of satellite and aerial imagery. Here we provide an overview of the crowdsourcing campaigns that have been run using Geo-Wiki over the last decade, including the amount of data collected, the research questions driving the campaigns and the outputs produced such as new data layers (e.g. a global map of forest management), new global estimates of areas or percentages of land cover/land use (e.g. the amount of extra land available for biofuels) and reference data sets, all openly shared. We demonstrate that the amount of data collected and the scientific advances in the field of land cover and land use would not have been possible without the participation of citizens. A relatively conservative estimate reveals that citizens have contributed more than 5.3 years of the data collection efforts of one person over short, intensive campaigns run over the last decade. We also provide key observations and lessons learned from these campaigns including the need for quality assurance mechanisms linked to incentives to participate, good communication, training and feedback, and appreciating the ingenuity of the participants.