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Messages on social media can be an important source of information during crisis situations. They can frequently provide details about developments much faster than traditional sources (e.g., official news) and can offer personal perspectives on events, such as opinions or specific needs. In the future, these messages can also serve to assess disaster risks. One challenge for utilizing social media in crisis situations is the reliable detection of relevant messages in a flood of data. Researchers have started to look into this problem in recent years, beginning with crowdsourced methods. Lately, approaches have shifted towards an automatic analysis of messages. A major stumbling block here is the question of exactly what messages are considered relevant or informative, as this is dependent on the specific usage scenario and the role of the user in this scenario. In this review article, we present methods for the automatic detection of crisis-related messages (tweets) on Twitter. We start by showing the varying definitions of importance and relevance relating to disasters, leading into the concept of use case-dependent actionability that has recently become more popular and is the focal point of the review paper. This is followed by an overview of existing crisis-related social media data sets for evaluation and training purposes. We then compare approaches for solving the detection problem based (1) on filtering by characteristics like keywords and location, (2) on crowdsourcing, and (3) on machine learning technique. We analyze their suitability and limitations of the approaches with regards to actionability. We then point out particular challenges, such as the linguistic issues concerning social media data. Finally, we suggest future avenues of research and show connections to related tasks, such as the subsequent semantic classification of tweets.

This is the English version of theWhite Paper Citizen Science Strategy 2030. This White Paperpresents a strategy with 94 action recommendations in 15 thematic fieldsfor Germany to strengthen Citizen Science by 2030 in order to unleash its innovative potential for science, society and policy. &nbsp

Data collection strategies vary among different citizen science projects. This complicates the intercomparability of parameter values acquired in different studies (e.g., methodological and scale issues) and results in variable data quality. This creates problems regarding the merging of different data sets and hampers the reuse of data from different projects. Modular designed applications for mobile devices (Apps) represent a framework that helps to foster the standardisation of data collection methods. While they encourage the reuse of the software, they provide enough flexibility for an adjustment in accordance with the research question(s) of interest. The currently developed App FieldMApp offers such a framework running under Android and iOS. The related concept includes predefined frame functionalities, like settings for the user account and the user interface, and adaptable application-related functionalities. The latter comprise several modules that are categorized as sensor test, basic functionality, parameter collection and data quality collection modules. The interdependencies of these modules are documented in a wiki. This enables an individual and context-based selection of functionalities. The FieldMApp is based on open-source software libraries (Xamarin, Open Development Kit (ODK), SQLite, CoreCLR-NCalc, LusoV.YamarinUsbSerialForAndroid, Newtonsoft.Json, SharpZipLib) and will be published as open-source software. Hence, the existing catalogue of functionalities can be augmented in the future. The premise for such extensions is that modules are published together with smart, universally applicable data quality recording routines and a proper documentation in the wiki. In this contribution, we present the concept and the structure of the FieldMApp and some current fields of application that are related to the cultivation of arable land, soil mapping, forest monitoring, and Earth Observation. The extension of the functionality catalogue is exemplified by the newly implemented speech recognition module. A related quality recording routine will be introduced. With this contribution we would like to encourage citizens and scientists to elicit which requirements such an App should fulfil from their point of view.

As citizen science projects heavily rely on their members’ voluntary participation in contributing ideas, co-creating projects and especially collecting data, it is crucial to understand motivations and factors that affect an individual’s decision to take part. This study seeks to understand how people’s personality traits affect their appraisal of citizen science projects and their likelihood of participating in them. We selected five typical citizen science projects that represent a broad range of topics, levels of difficulty and execution modes as a basis for our investigation. While other studies only focus on the motivation of participants who already took part in citizen science activities, this study also targets participants without prior experience, who might volunteer in citizen science projects in the future. By using a structured survey that includes questions on personality traits, functional motives and subjective evaluation of the five sample projects, we seek to report respondents’ most important intrinsic motivations for citizen science. Apart from these intrinsic motivations, we also explore the use of monetary incentives to encourage participation in citizen science tasks, which might be used in the future to recruit participants and improve data quality. The study participants’ expectations regarding monetary rewards can be used to improve existing algorithms and design new methods that fairly and efficiently distribute rewards.

Compiling and disseminating information about incidents and disasters is key to disaster management and relief. But due to inherent limitations of the acquisition process, the required information is often incomplete or missing altogether. To fill these gaps, citizen observations spread through social media are widely considered to be a promising source of relevant information, and many studies propose new methods to tap this resource. Yet, the overarching question of whether, and under which circumstances social media can supply relevant information (both qualitatively and quantitatively) still remains unanswered. To shed some light on this question, we review 37 large disaster and incident databases covering 27 incident types, organize the contained data and its collection process, and identify the missing or incomplete information. The resulting data collection reveals six major use cases for social media analysis in incident data collection: impact assessment and verification of model predictions, narrative generation, enabling enhanced citizen involvement, supporting weakly institutionalized areas, narrowing surveillance areas, and reporting triggers for periodical surveillance. Aside from this analysis, we discuss the advantages and disadvantages of the use of social media data for closing information gaps related to incidents and disasters.

The increasing amount of research data provides the opportunity to link and integrate data to create novel hypotheses, to repeat experiments or to compare recent data to data collected at a different time or place. However, recent studies have shown that retrieving relevant data for data reuse is a time-consuming task in daily research practice. In this study, we explore what hampers dataset retrieval in biodiversity research, a field that produces a large amount of heterogeneous data. We analyze the primary source in dataset search - metadata - and determine if they reflect scholarly search interests. We examine if metadata standards provide elements corresponding to search interests, we inspect if selected data repositories use metadata standards representing scholarly interests, and we determine how many fields of the metadata standards used are filled. To determine search interests in biodiversity research, we gathered 169 questions that researchers aimed to answer with the help of retrieved data, identified biological entities and grouped them into 13 categories. Our findings indicate that environments, materials and chemicals, species, biological and chemical processes, locations, data parameters and data types are important search interests in biodiversity research. The comparison with existing metadata standards shows that domain-specific standards cover search interests quite well, whereas general standards do not explicitly contain elements that reflect search interests. We inspect metadata from five large data repositories. Our results confirm that metadata currently poorly reflect search interests in biodiversity research. From these findings, we derive recommendations for researchers and data repositories how to bridge the gap between search interest and metadata provided.

Space-borne Earth Observation (EO) data depicting vegetation covered land surfaces contain insufficient information for an unambiguous interpretation of the spectral signal in terms of variables that characterize the vegetation state (e.g., leaf area index, leaf chlorophyll content and proportion of senescent material). For the retrieval of vegetation properties from EO data, an optimal estimate of the state variables needs to be found. The uncertainty of such an estimate can be reduced by combining EO data and in situ data. Information provided by citizens represents a valuable and mostly inexpensive source for in situ data. Since the quality of such data can be diverse, the consideration of uncertainties is of great importance. In this contribution, we present a concept for the elicitation of local knowledge from citizens with respect to the application of management practices (e.g., sowing and harvesting date, irrigation) and the instantaneous state of crops. The concept includes the acquisition of in situ data as well as an uncertainty assessment (precision and/or accuracy). The latter involves a profiling in which inherent uncertainties are quantified for individual citizens. This concept was tested for agricultural fields of the Durable Environmental Multidisciplinary Monitoring Information Network (DEMMIN) test site in Northeast Germany. Within the frame of several field seminars, students were requested to assess management practices and the instantaneous state of crops. Furthermore, they assessed their own ability to create valid data. They filled in pseudonymized questionnaires from which we created corresponding datasets. At the same day, field data were collected with appropriate equipment and can be used as reference against which student estimates can be compared. The level of compliance between estimated and measured data was determined on an individual basis. The results of this analysis will be presented. Conclusions will be drawn regarding the ability of the students to evaluate their own skills. In addition, we will demonstrate an approach for a digital ascertainment of in situ data. In the future, this approach will be used to collect in situ data for the setup of refined prior information within the frame of the Earth Observation Land Data Assimilation System (EO-LDAS).

Social media is perceived as a rich resource for disaster management and relief efforts, but the high class imbalance between disaster-related and non-disaster-related messages challenges a reliable detection. We analyze and compare the effectiveness of three state-of-the-art machine learning models for detecting disaster-related tweets. In this regard we introduce the Disaster Tweet Corpus 2020, an extended compilation of existing resources, which comprises a total of 123,166 tweets from 46 disasters covering 9 disaster types. Our findings from a large experiments series include: detection models work equally well over a broad range of disaster types when being trained for the respective type, a domain transfer across disaster types leads to unacceptable performance drops, or, similarly, type-agnostic classification models behave more robust at a lower effectiveness level. Altogether, the average misclassification rate of 3,8\% on performance-optimized detection models indicates effective classification knowledge but comes at the price of insufficient generalizability.

There is no doubt that unmanned aerial systems (UAS) will play an increasing role in Earth observation in the near future. The field of application is very broad and includes aspects of environmental monitoring, security, humanitarian aid, or engineering. In particular, drones with camera systems are already widely used. The capability to compute ultra-high-resolution orthomosaics and three-dimensional (3D) point clouds from UAS imagery generates a wide interest in such systems, not only in the science community, but also in industry and agencies. In particular, forestry sciences benefit from ultra-high-structural and spectral information as regular tree level-based monitoring becomes feasible. There is a great need for this kind of information as, for example, due to the spring and summer droughts in Europe in the years 2018/2019, large quantities of individual trees were damaged or even died. This study focuses on selective logging at the level of individual trees using repeated drone flights. Using the new generation of UAS, which allows for sub-decimeter-level positioning accuracies, a change detection approach based on bi-temporal UAS acquisitions was implemented. In comparison to conventional UAS, the effort of implementing repeated drone flights in the field was low, because no ground control points needed to be surveyed. As shown in this study, the geometrical offset between the two collected datasets was below 10 cm across the site, which enabled a direct comparison of both datasets without the need for post-processing (e.g., image matching). For the detection of logged trees, we utilized the spectral and height differences between both acquisitions. For their delineation, an object-based approach was employed, which was proven to be highly accurate (precision = 97.5%, recall = 91.6%). Due to the ease of use of such new generation, off-the-shelf consumer drones, their decreasing purchase costs, the quality of available workflows for data processing, and the convincing results presented here, UAS-based data can and should complement conventional forest inventory practices.

Citizen Science - wörtlich übersetzt "Bürgerforschung" oder "Bürgerwissenschaften" - wird in der Wissenschaft, in den Medien und in der Politik vielfach diskutiert als Ansatz, Bürgerinnen und Bürger in Forschungsprozesse einzubeziehen. Die Möglichkeiten der Forschungsbeteiligung sind nicht neu, zahlreiche Ehrenamtliche haben schon immer ihre Umwelt erforscht und zur Vermehrung von Wissen beigetragen. Was neu ist, sind zahlreiche Möglichkeiten der Teilhabe (als sozialpolitisches Konzept für Selbstbestimmung und Eigenverantwortung), der Teilnahme (als eine Form des Mitmachens) und der Teilgabe (verstanden als das Teilen und Zugänglichmachen von Wissen) durch die Entwicklung und breite Anwendung von modernen Internet-Technologien. Unterstützt werden diese Entwicklungen auch durch die Bereitschaft und Erkenntnis der Forschenden, ihre Forschung stärker an den gesellschaftlichen Bedürfnissen auszurichten, und die Akteure der Gesellschaft frühzeitig in Forschungsprozesse einzubinden. Vom 11. bis 12. März 2020 hat die Veranstaltung "Citizen Science - Neues Beteiligungsformat für die Forschung zur Agrar-, Forst-, Fischereiwirtschaft und zu ländlichen Räumen" mit mehr als 30 Teilnehmenden aus dem Thünen-Institut und aus Partner-Organisationen stattgefunden (Foto 1). An Beispielen aus der Agrar-, Forst- und Fischereiwirtschaft und zu den ländlichen Räumen wurden Möglichkeiten diskutiert, wie Citizen Science (CS) in die eigene Forschung integriert werden kann/könnte. Citizen Science hat ein großes Potential für die Themenfelder, die seitens des Thünen-Instituts bearbeitet werden, wobei insbesondere an die Erfahrungen und Erkenntnisse der sozialwissenschaftlichen Forschung am Thünen-Institut anzuknüpfen ist. Citizen Science kann eine ergänzende Perspektive als Teilfrage für wissenschaftliche Projekte bieten, um den Wissenstransfer und den Wissensaustausch zwischen Forschenden und Akteuren der Gesellschaft und Politik zu ermöglichen. Für die weitere Förderung der Integration des Ansatzes in bestehende und zukünftige Forschungstätigkeiten und -vorhaben gilt es, Kapazitäten für Citizen Science zu entwickeln und auszubauen. Hierzu sollen unter anderen weiteren Veranstaltungen zum Thema Citizen Science stattfinden, die sich z.B. mit der Sicherung von Datenqualität und Ehrenamtsmanagement beschäftigen. Erste Erfahrungen zur Konzeption und praktischen Umsetzung von Citizen Science werden aktuell im Verbundprojekt MonViA gesammelt. Citizen science - translated into German as "Bürgerforschung" or "Bürgerwissenschaften" - is widely discussed in science, in the media and in politics as an approach to involve citizens in research processes. The opportunities to participate in research are not new; numerous volunteers have always investigated their natural environment and contributed to the increase in knowledge about the natural world by providing scientific data for the scientific community. What is new are numerous opportunities for participation in various forms such as being part as an citizens in sciences (as a socio-political concept for self-determination and personal responsibility), taking part in a scientific activity and taking responsibility such as caring and sharing knowledge generated through the development and wide use of modern internet technologies. These developments are also supported by the willingness and knowledge of science to align research more closely along societal needs and demands and to involve society's actors in research processes at an early stage. The lunch to lunch workshop entitled "Citizen Science - New Participation Format for Research in the Agricultural, Forestry, Fisheries and Rural Areas" took place from March 11 to March 12, in 2020 with more than 30 participants from the Thünen Institute and partner organizations. Citizen Science projects and established were presented and potentials for the integration of citizen science in research at Thünen were discussed. Among the participants there was an agreement that citizen science has great potential for the respective thematic areas of the Thünen Institute, whereby the experience and knowledge of the social science research at the Thünen Institute is particularly important to be integrated and acknowledged. Citizen Science can offer a supplementary perspective as a sub-question for scientific projects to enable knowledge transfer and knowledge exchange between researchers and stakeholders in society and politics. To further promote the integration of this approach into existing and future research activities and projects, it is important to develop and expand capacities for citizen science at Thünen-Institut. For this purpose, other events on the topic of Citizen Science will take place, e.g, dealing with data quality assurance and volunteer management. Initial experience in the conception and practical implementation of Citizen Science is currently being collected in the MonViA research project.

On 29th of August 2017 the German Aerospace Center (DLR) Institute of Data Science was established in the city of Jena, Germany. This institute will focus on data related research such as big data management and analysis, digital production platforms, and software and IT security. One of the six workgroups of this institute is dedicated to citizen science related scientific questions. The workgroup will benefit from the close cooperation with local and regional research institutes such as the Friedrich-Schiller-University Jena and the Bauhaus University in Weimar. Although the tremendous potential of citizen science has been recognized and partly used within various thematic fields, some important issues lack thorough and systematic research. This includes i) sociologic and psychologic aspects with regard to the engagement and motivation of people, ii) the collection and provision of credible, meaningful and (re-)usable citizen science data, e.g. semantic attribution, data quality & reliability, and iii) strategies for the analysis of the data and the generation of insights from these, e.g. knowledge discovery, automation, visualization, or integration of heterogeneous data sources. The citizen science workgroup will undertake dedicated research on the mentioned topics. Thematically, the research will have close links to aerospace, including crisis management using social media data and volunteered geographic information as well as earth observation (remote sensing), e.g. local to global reference data generation for model training and product validation, but is open to other disciplines as well The activities of the workgroup will benefit from a permanent citizen science lab acting as contact point for interested citizens, providing tools and infrastructure to support citizen projects, and hosting capacity building and education activities.

Social media sources can be helpful in crisis situations, but discovering relevant messages is not trivial. Methods have so far focused on universal detection models for all kinds of crises or for certain crisis types (e.g. floods). Event-specific models could implement a more focused search area, but collecting data and training new models for a crisis that is already in progress is costly and may take too much time for a prompt response. As a compromise, manually collecting a small amount of example messages is feasible. Few-shot models can generalize to unseen classes with such a small handful of examples, and do not need be trained anew for each event. We show how these models can be used to detect crisis-relevant tweets during new events with just 10 to 100 examples and counterexamples. We also propose a new type of few-shot model that does not require counterexamples.

One of the workgroups of the DLR Institute for Data Science is dedicated to citizen science related questions such as i) psychologic aspects with regard to the engagement of people, ii) the processing of citizen science data, e.g. semantic attribution, data quality & reliability assessment, and iii) strategies to optimize the exploitation of the data, e.g. automation, visualization, or handling of heterogeneous data. The research has close links to crisis management using social media data and volunteered geographic information as well as to Earth Observation, e.g. local to global reference data generation for model training and validation. The work includes the exploration of 2D and 3D data acquired by unmanned aerial vehicles (UAVs) to address the main bottle neck in satellite based Earth Observation: missing reference data. Successful projects such as Geo-Wiki or Crowd4Sat demonstrate the vast potential of citizen science in this field. However, to date all initiatives focus on 2D information such as single photographs or the manual classification of satellite imagery. What is missing is the third dimension which is mandatory for several observation applications such as vegetation biomass estimation or the identification of damage in buildings for disaster management. Currently, up-to-date 3D reference information is hardly available. The acquisition of such data causes high efforts either in terms of labour or in terms of equipment, or both. The advent of low cost drones has the potential to supplement the need for 3D data. Although the great potential of Structure-from-Motion (SfM) based methods for 3D reconstruction has been demonstrated for a wide range of applications, several issues need to be solved to foster trust and acceptance in this kind of data by the scientific community and administrative bodies, in particular if this data are acquired by citizens. The issue with the potentially highest priority is the quality of the generated 3D point clouds derived from multiview images. To address this issue, in 2018 a comprehensive UAV campaign was accomplished close to the city of Jena, Germany. During this campaign, eight drones equipped with dissimilar cameras operating in different modes acquired data over an area featuring different land cover types. This campaign, which included citizen scientists as drone pilots flying their own UAVs, was accompanied by DLR’s 3K sensor to gather a high grade reference dataset. Additionally, airborne LiDAR data are available as well. The processing of the data was accomplished by applying Agisoft Photoscan 1.4.4. The quality assessment of the resulting point clouds comprised the vertical (height) and horizontal (2D location) accuracy for different objects and surfaces within the test site. Although the evaluation of the results is still ongoing, first results promise a wide usability of low cost drone data acquired by citizens. Further research will focus on optimizing acquisition and processing parameters for improving the 3D point cloud data quality. One aspect will be the development of a dedicated 3D point cloud quality measure. The results of this research will be condensed in guidelines and best practices for crowd based UAV data generation.

Many citizen science projects rely on their contributors going to the field and collecting data. Due to their wide availability and increasing capability, modern mobile devices have become an indispensable tool to ease the collection process. Projects can publish mobile apps, that allow contributors to easily collect data and submit their results. The requirements of individual projects oftentimes overlap to a large extent, which triggered the development of multiple generic frameworks. They allow new projects to quickly generate customized apps and reuse existing infrastructure. However, the wide landscape of tools with diverging capabilities requires projects to compare and choose. This report supports data managers in making an informed decision. We report on our experiences primarily on the whole data collection workflow starting from setting up your own instance to finally analyzing the retrieved data. We compare eight tools – both free and commercial - according to the features provided and difficulties encountered.

Messages on social media can be an important source of information during a disaster. They can frequently provide details about developments much faster than traditional sources (e.g. official news) and can offer personal perspectives on events, such as opinions or specific needs. In the future, these messages can also serve to assess disaster risks. One challenge for utilizing social media in disaster situations is the detection of informative messages in a flood of data. Researchers have started to look into this problem in recent years, beginning with crowd-sourced methods. Lately, approaches have shifted towards an automatic analysis of messages. In this study, we present methods for the automatic detection of crisis-related messages (tweets) on Twitter. We start by showing the varying definitions of importance and relevance relating to disasters, as they can serve very different purposes. This is followed by an overview of existing data sets. We then compare approaches for solving this problem based on Machine Learning techniques with regard to their focus, their data requirements, their technical prerequisites, their efficiency and accuracy, and their time scales. These factors determine the suitability of the approaches for different expectations, but also their limitations. We identify which aspects each of them can contribute to the detection of informative tweets, and which areas can be improved upon in the future.We point out particular challenges, such as the linguistic issues concerning this kind of data. Finally, we suggest future avenues of research, and show connections to related tasks, such as the subsequent semantic classification of tweets.

Wir stellen das Projekt Nachtlicht-BüHNE vor, welches als Gemeinschaftsprojekt von DLR, GFZ und UFZ im Rahmen der Ausschreibung „CitizenScience@Helmholtz" gefördert wird. Nachtlicht-BüHNE adressiert Citizen Science im Kontext der wissenschaftlichen Anwendungsbereiche Astronomie, Weltraumforschung und Lichtverschmutzung. Ziel ist die Entwicklung eines Co-Design-Ansatzes für App-basierte Citizen-Science-Projekte bei dem Bürger mit Wissenschaftlern zusammenarbeiten. Im Rahmen von zwei Pilotstudien zu den Themen Lichtverschmutzung und Meteorforschung entwickeln Wissenschaftler und Bürger zwei Citizen-Science-Projekte. Die resultierende Co-Designmethodik wird im Rahmen einer Webplattform mit geeigneten kollaborativen Werkzeugen bereitgestellt. Untersucht werden auch mögliche Synergiepotentiale durch die Beteiligung interessierter Bürger an mehreren inhaltlich angrenzenden Projekten.

In den Bürgerwissenschaften spielt die Datenerfassung mittels mobiler Anwendungen eine immer wichtigere Rolle. In den letzten Jahren sind dazu eine ganze Reihe von Software-Frameworks entstanden, welche die einfache Erstellung von Umfragen und das Sammeln von Daten über Smartphone-Anwendungen ermöglichen. Diese Frameworks unterstützen zwar die Erstellung und Durchführung solcher Datenerfassungsumfragen, der Datenexport beschränkt sich jedoch meist auf tabellarische Standardformate wie CSV oder Excel. Da neben den eigentlichen Daten nur wenige Metadaten erhoben werden, bleibt zudem oft die Semantik der Daten (Was wurde wie gemessen/beobachtet?) unerfasst. Dies erschwert die Nachnutzung dieser Citizen-Science-Daten über den initialen Kontext hinaus, da Interpretierbarkeit und Integration mit anderen Daten beeinträchtigt werden. Unser Beitrag stellt eine Methode und zugehörige Implementierung vor, welches es Forschern ermöglicht ihre Umfragen für Kampagnen einfach semantisch anzureichern und die erhobenen Daten flexibel zu exportieren. Neben klassischen Formaten wie XML, wird auch der Export nach RDF (Linked Open Data) unterstützt, was die Verknüpfung der Daten mit ihrer maschinenlesbaren Bedeutung ermöglicht. Die Implementierung wurde als Erweiterung des weit verbreiteten Datenerfassungs-Frameworks Open Data Kit 1 (ODK1) realisiert und ist frei verfügbar. Damit leistet sie einen Beitrag zur Interoperabilität und Interpretierbarkeit von Citizen-Science-Daten.

WG5 Deliverable 1 – Citizen-science ontology. A WG5’s specific objective was to help develop an ontology of citizen-science projects (including a vocabulary of concepts and metadata) to support data sharing among citizen-science projects. WG5 coordinated with activities on data and service interoperability carried out in Europe, Australia and the USA (e.g., the CSA’s international Data and Metadata Working Group [http://www.citizenscience.org/association/about/working-groups/data-and-metadata-working-group/]), and took into account existing standards., Background The main aim of COST Action CA15212 "Citizen Science to promote creativity, scientific literacy, and innovation throughout Europe" (2016-2020) [https:// www.cs-eu.net/] is to bundle capacities across Europe to investigate and extend the impact of the scientific, educational, policy, and civic outcomes of citizen science with the stakeholders from all sectors concerned (e.g., policymakers, social innovators, citizens, cultural organizations, researchers, charities and NGOs), to gauge the potential of citizen science as an enabler of social innovation and socio-ecological transition. One goal of the COST Action is to help create an ontology (including a vocabulary) for describing citizen-science projects, observations and analyses, building upon prior research and existing standards, which any organization can model their database structure upon. This goal is also linked to the larger objectives of the international Data and Metadata Working Group of the Citizen Science Association (CSA) and the European Citizen Science Association (ECSA).