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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.

This invited presentation was given at an information event about the European Space Agency’s (ESA) Navigation Innovation and Support Programme (NAVISP) hosted by the Austrian Agency for the Promotion of Science (FFG) in preparation for the ESA Ministerial Conference 2022. The presentation was about the CAMALIOT project, which is currently funded through NAVISP and by FFG, outlining the initial results and what the next steps in the project are. In particular, information about the CAMALIOT crowdsourcing campaign (being run by IIASA) was provided as well as the status of the CAMALIOT machine learning infrastructure and the science uses cases in the project.

Picture Pile is a flexible web-based and mobile application for ingesting imagery from satellites, orthophotos, unmanned aerial vehicles and/or geotagged photographs for rapid classification by volunteers. Since 2014, there have been 16 different crowdsourcing campaigns run with Picture Pile, which has involved more than 4000 volunteers who have classified around 11.5 million images. Picture Pile is based on a simple mechanic in which users view an image and then answer a question, e.g., do you see oil palm, with a simple yes, no or maybe answer by swiping the image to the right, left or downwards, respectively. More recently, Picture Pile has been modified to classify data into categories (e.g., crop types) as well as continuous variables (e.g., degree of wealth) so that additional types of data can be collected. The Picture Pile campaigns have covered a range of domains from classification of deforestation to building damage to different types of land cover, with crop type identification as the latest ongoing campaign through the Earth Challenge network. Hence, Picture Pile can be used for many different types of applications that need image classifications, e.g., as reference data for training remote sensing algorithms, validation of remotely sensed products or training data of computer vision algorithms. Picture Pile also has potential for monitoring some of the indicators of the United Nations Sustainable Development Goals (SDGs). The Picture Pile Platform is the next generation of the Picture Pile application, which will allow any user to create their own ‘piles’ of imagery and run their own campaigns using the system. In addition to providing an overview of Picture Pile, including some examples of relevance to SDG monitoring, this presentation will provide an overview of the current status of the Picture Pile Platform along with the data sharing model, the machine learning component and the vision for how the platform will function operationally to aid environmental monitoring.

This application is designed to let users visualize the MAGIC Marginal Land (MAEZ) maps: Marginal Land Marginal Land, combined with all individual factors Maps for each factor separately: chemical climate fertility rooting terrain wetness intensity (current land use combined with intensity and indicators The portal provides cross-platform access to the Marginal Land spatial datasets (MAEZ). Mouse-over the various icons to see what each does. Currently the application requires internet access. In the upper left of the application, you can search for a location. You can also zoom in/out, select the home key to return to the full extent, turn on your GPS, change the background base map, add the legend and select visible layers. The attribute tables can be accessed from the bottom of the screen when maps are visible. To access the ESRI viewing tool for MAEZ open underneath link: https://iiasa-spatial.maps.arcgis.com/apps/webappviewer/index.html?id=c0105c0d94c34048a1c32fba1d65a6b1&nbsp

Peatland comprises around 24% of South Sumatra, a province on the island of Sumatra in Indonesia. Following catastrophic fires in 2015, peat restoration has become a priority for this area. To identify candidate areas for restoration, both land cover over time and land degradation have been mapped using optical and radar remote sensing. Limited field data have been used to help validate these maps but more validation data are still needed. One way to fill this gap is to tap into the power of citizen science, which has become an emerging area of interest. In citizen science, any member of the public can take part in scientific research, whether this is through data collection, analysis of the data or hypothesis generation. Here we present the results from a citizen science campaign using the Urundata mobile gaming application, which has been developed as part of the Restore+ project. Urundata has two main components: a rapid image assessment tool that allows users to classify satellite imagery by the type of land cover/land use visible or to examine pairs of images for detection of change over time (developed from an application called Picture Pile). The second component sends users to specific locations on the ground via a mobile device and asks for information related to land cover and evidence of land degradation (developed from an application called FotoQuest Go). Together these two components have been used to help validate land cover and land degradation maps of South Sumatra through citizen science.

Tracking and monitoring the implementation of the SDGs is critical for establishing progress, which requires a systematic review of the social, economic and environmental dimensions of the SDGs. As inputs, accurate, accessible, timely and spatially disaggregated data are needed. Even though data availability and quality have improved over the last decade, more data are needed to ensure that “no one is left behind”, which is a key component of the 2030 Agenda for Sustainable Development, while addressing all aspects of the SDGs. Traditional data collection methods, e.g. administrative records, statistical surveys, censuses, etc. need to be strengthened, and a much wider set of data are needed to address the SDGs. To achieve this, new, innovative ways of data production and analysis using Earth Observation (EO), mobile data, social media, sensors, etc. need to be developed and adopted. In addition to EO and other new geospatial data sets such as mobile phone data, another key source of data to support the SDGs is citizen science (CS), which is defined as the involvement of citizens in scientific research. CS can deliver data swiftly, accurately and at a level of granularity not possible with traditional data gathering methods. This session will outline the results from a workshop hosted by IIASA on 3‐5 October in Austria that brought together major global citizen science associations from Europe, Africa, Asia, Australia and the US. Recommendations and best practices will be outlined to address how non‐traditional data sources, citizen science in particular, can contribute to both monitoring and implementation of the SDGs. A conceptual framework will be proposed to identify how creating an enabling environment for the integration of traditional and non‐traditional approaches can leverage the SDG achievement. Additionally, we will showcase examples of success stories, and demonstrate how selected indicators can be monitored via non‐traditional approaches.

A reliable system for grading operative difficulty of laparoscopic cholecystectomy would standardise description of findings and reporting of outcomes. The aim of this study was to validate a difficulty grading system (Nassar scale), testing its applicability and consistency in two large prospective datasets.Patient and disease-related variables and 30-day outcomes were identified in two prospective cholecystectomy databases: the multi-centre prospective cohort of 8820 patients from the recent CholeS Study and the single-surgeon series containing 4089 patients. Operative data and patient outcomes were correlated with Nassar operative difficultly scale, using Kendall's tau for dichotomous variables, or Jonckheere-Terpstra tests for continuous variables. A ROC curve analysis was performed, to quantify the predictive accuracy of the scale for each outcome, with continuous outcomes dichotomised, prior to analysis.A higher operative difficulty grade was consistently associated with worse outcomes for the patients in both the reference and CholeS cohorts. The median length of stay increased from 0 to 4 days, and the 30-day complication rate from 7.6 to 24.4% as the difficulty grade increased from 1 to 4/5 (both p 0.001). In the CholeS cohort, a higher difficulty grade was found to be most strongly associated with conversion to open and 30-day mortality (AUROC = 0.903, 0.822, respectively). On multivariable analysis, the Nassar operative difficultly scale was found to be a significant independent predictor of operative duration, conversion to open surgery, 30-day complications and 30-day reintervention (all p 0.001).We have shown that an operative difficulty scale can standardise the description of operative findings by multiple grades of surgeons to facilitate audit, training assessment and research. It provides a tool for reporting operative findings, disease severity and technical difficulty and can be utilised in future research to reliably compare outcomes according to case mix and intra-operative difficulty.

To access the version 2 MAP-DB containing the Marginal land Agri-environmental zonation (MAEZ) goto link: http://iiasa-spatial.maps.arcgis.com/apps/webappviewer/index.html?id=3c69a9a930a44814956f4e3bdd18a104 Access to this version was launched at the MAGIC project meeting in Madrid 9-10 July.

The last decade has witnessed a rise in the field of citizen science which can be described as a collaborative undertaking between citizens and scientists to help gather data and create new scientific knowledge. In the EU, efforts have been channeled into developing the concept of Citizen Observatories (COs), which have been supported via the Seventh Framework Program (FP7) and continue to be funded in Horizon 2020. COs, often supported by innovative technologies including Earth Observation (EO) and mobile devices, are the means by which communities can monitor and report on their environment and access information that is easily understandable for decision making. To improve the coordination between existing COs and related citizen science activities, the WeObserve project tackles three key challenges that face COs: awareness, acceptability and sustainability. The WeObserve mission is to create a sustainable ecosystem of COs that can systematically address these identified challenges and help move citizen science into the mainstream. The WeObserve approach will apply several key instruments to target, connect and coordinate relevant stakeholders. The first is to develop and foster five communities of practice to strengthen the current knowledge base surrounding COs. Topics will include citizen engagement, the value of COs for governance and CO data interoperability. In co-creating this knowledge base, CO practitioners will have a platform to effectively share best practices and avoid duplication. Secondly, the project will expand the geographical reach of the knowledge base to different target groups via toolkits, a Massive Open Online Course (MOOC), capacity development roadshows and an Open Data Exploitation Challenge, to strengthen the uptake of CO-powered science by public authorities and SMEs. A third mechanism will forge links with GEOSS and Copernicus to demonstrate how COs can complement the EU’s Earth Observation monitoring framework. This paper will describe these various mechanisms and issue a call to bring together diverse stakeholders who share a joint (practice-oriented) interest in citizen science. The WeObserve consortium brings together the current H2020 COs (Ground Truth 2.0, GROW, LandSense, Scent) who will actively open up the citizen science landscape through wide ranging networks, users and stakeholders, including ECSA, GEOSS and Copernicus to foster social innovation opportunities. The WeObserve approach and outcomes have the potential to create a step-change in the Earth Observation sector and make COs a valuable component of Earth system science research to manage environmental challenges and empower resilient communities.

Every three years, dating back to 2006, Eurostat conducts an exhaustive Land Use/Cover Area frame Survey (LUCAS), where professional surveyors visit approximately 270,000 locations across EU countries to acquire photos and record detailed in-situ information on land use/cover. This conventional approach to ground-based calibration/ validation data acquisition is rather costly and is limited to detecting changes on a fixed 3-year cycle. As such, within the EU’s Earth Observation (EO) monitoring framework, there is a need for low-cost solutions for acquiring high quality ground-based data to support the delivery of timely, accurate and well-validated environmental monitoring products. By leveraging the proliferation of mobile devices the FotoQuest Go mobile application offers a citizen-centric tool to mapping land use and land cover dynamics. FotoQuest Go engages citizens and crowdsources the needed information in a more participatory approach while directly complementing the LUCAS survey findings. This paper describes the recent results of a 2017 FotoQuest Go crowdsourcing campaign conducted in Austria, where 100+ participants recorded land use and land cover observations from over 895 LUCAS locations. When visiting a location, the application guides the participants through a series of tasks (i.e. photo acquisition, questionnaire) that follows a subset of the standard LUCAS surveyor data collection protocol. Once the protocol is completed, participants upload their observations for quality check. Experts would then review each submission and provide feedback directly to the participants within 24hrs. Combined with the feedback was a monetary incentive of 1 EUR for each successfully completed location or quest. It was discovered that the quality control and assurance process was very effective in not only ensuring useful and high-quality citizen science data, but also providing a means to facilitate learning among the participants. In other words, within the 2017 FotoQuest Go campaign, we learned that timely and detailed feedback helped to improve the data collected by participants when they visited subsequent locations. This paper will elaborate the added value of quality-assured citizen science data to the domain of traditionally-collected data for land use and land cover monitoring. FotoQuest Go has considerable potential to lower expenditure costs on in-situ data collection and greatly extend the current sources of such data for earth system science research, thereby realizing citizen-powered innovations in the processing chain of land use/cover monitoring activities both within and beyond Europe.

Citizen science is quickly becoming one of the most effective tools for the rapid and low-cost collection of environmental information, filling a long recognized gap in in-situ data. Incentivizing citizens to participate, however, remains a challenge, with gaming being widely recognized as an effective solution to overcome the participation barrier. Building upon well-known gaming mechanics, games provide the user with a competitive and fun environment. This paper presents three different applications that employ game mechanics and have generated useful information for environmental science. Furthermore, it describes the lessons learnt from this process to guide future efforts.