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AbstractTwitter data are a valuable source of information for rescue and helping activities in case of natural disasters and technical accidents. Several methods for disaster‐ and event‐related tweet filtering and classification are available to analyse social media streams. Rather than processing single tweets, taking into account space and time is likely to reveal even more insights regarding local event dynamics and impacts on population and environment. This study focuses on the design and evaluation of a generic workflow for Twitter data analysis that leverages that additional information to characterize crisis events more comprehensively. The workflow covers data acquisition, analysis and visualization, and aims at the provision of a multifaceted and detailed picture of events that happen in affected areas. This is approached by utilizing agile and flexible analysis methods providing different and complementary views on the data. Utilizing state‐of‐the‐art deep learning and clustering methods, we are interested in the question, whether our workflow is suitable to reconstruct and picture the course of events during major natural disasters from Twitter data. Experimental results obtained with a data set acquired during hurricane Florence in September 2018 demonstrate the effectiveness of the applied methods but also indicate further interesting research questions and directions.

Abstract. 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.

Precise forest inventory data are requested by a wide range of users such as scientists, politicians, administrators, forest owners, or the forest industry. One forest inventory parameter of great importance is the forest stem volume (or growing stock volume, GSV). On the one hand, GSV is related to the monetary value of a forest. On the other hand, the amount of bound carbon can be estimated based on GSV. For the determination of the GSV the stem diameter (usually diameter at breast height, DBH), the tree height, the number of trees per unit area, and a species and forest stand specific form factor are required. In forestry, sample based approaches are used to gather these parameters. For minimizing effort and expense, the number and dimensions of these samples are small compared to the total forest area. Also, the repeat time between two inventories is rather large (in the order of ten years). Accordingly, relative GSV errors of approximately 20% have to be accepted. There exists a great interest to minimize both, effort and inventory errors. Precise inventory data are of particular interest in the research domain. For instance, satellite based methods aiming at GSV estimation suffer from inaccurate reference measurements, as the inventory errors propagate to the final satellite based estimates. Airborne light detection and ranging data (LiDAR) can be utilized to detect single trees and to measure the corresponding tree heights with sufficient accuracy for forestry applications. In some Scandinavian countries forest inventories are supported by LiDAR campaigns by default. Moreover, most European countries execute regular and country-wide LiDAR acquisitions, thus LiDAR based tree height measurements could be achieved. For instance, the LiDAR campaign repetition rate in Germany is five years. However, the stem diameter cannot be measured using airborne LiDAR data. Although some technical ground- and low altitude airborne solutions have been proposed, currently the most efficient approach is manual DBH measurement. The simplicity of DBH measurements makes this task an excellent citizen science exercise. To assess the achievable DBH measurement precision, an experiment involving students of a secondary school was carried out in late 2017. The test site "Roda Forest" is located 20 km in the Southeast of Jena. The selected stand is dominated by pine with an age of 60 years. The reference data for the experiment was generated by means of a terrestrial laser scanner (TLS). Based on the TLS data the precise location and the GSV of approximately 200 trees were delineated. The students were equipped with a smartphone application to localize the single trees. During the campaign the circumference of approximately 100 trees was determined using simple measuring tape. These measurements were converted to DBH after the field campaign. The measured DBH varied between 7 cm and 38 cm. In overall, TLS-based and student campaign based measurements were in great agreement (R² = 0.98). Nevertheless, the identification of the correct trees by the students during the campaign was challenging, which was related to general orientation difficulties and a weak GPS signal underneath the forest canopy. This resulted in a remarkable offset between GPS-based and real coordinates. Forthcoming campaigns have to deal with this issue. One option we will explore in the future is the absolute calibration of the GPS signal using checkpoints with precise coordinates.

Abstract. Compiling and disseminating information about incidents and disasters are 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 disaster and incident databases covering 27 incident types, compile a unified overview of the contained data and their collection processes, and identify the missing or incomplete information. The resulting data collection reveals six major use cases for social media analysis in incident data collection: (1) impact assessment and verification of model predictions, (2) narrative generation, (3) recruiting citizen volunteers, (4) supporting weakly institutionalized areas, (5) narrowing surveillance areas, and (6) reporting triggers for periodical surveillance. Furthermore, we discuss the benefits and shortcomings of using social media data for closing information gaps related to incidents and disasters.

Interest in the formal representation of citizen science comes from portals, platforms, and catalogues of citizen science projects; scientists using citizen science data for their research; and funding agencies and governments interested in the impact of citizen science initiatives. Having a common understanding and representation of citizen science projects, their participants, and their outcomes is key to enabling seamless knowledge and data sharing. In this chapter, we provide a conceptual model comprised of the core citizen science concepts with which projects and data can be described in a standardised manner, focusing on the description of the participants and their activities. The conceptual model is the outcome of a working group from the COST Action CA15212 Citizen Science to Promote Creativity, Scientific Literacy, and Innovation throughout Europe, established to improve data standardisation and interoperability in citizen science activities. It utilises past models and contributes to current standardisation efforts, such as the Public Participation in Scientific Research (PPSR) Common Conceptual Model and the Open Geospatial Consortium (OGC) standards. Its design is intended to fulfil the needs of different stakeholders, as illustrated by several case studies which demonstrate the model's applicability.

24 pages, 4 figures, 3 tables ; Interest in the formal representation of citizen science comes from portals, platforms, and catalogues of citizen science projects; scientists using citizen science data for their research; and funding agencies and governments interested in the impact of citizen science initiatives. Having a common understanding and representation of citizen science projects, their participants, and their outcomes is key to enabling seamless knowledge and data sharing. In this chapter, we provide a conceptual model comprised of the core citizen science concepts with which projects and data can be described in a standardised manner, focusing on the description of the participants and their activities. The conceptual model is the outcome of a working group from the COST Action CA15212 Citizen Science to Promote Creativity, Scientific Literacy, and Innovation throughout Europe, established to improve data standardisation and interoperability in citizen science activities. It utilises past models and contributes to current standardisation efforts, such as the Public Participation in Scientific Research (PPSR) Common Conceptual Model and the Open Geospatial Consortium (OGC) standards. Its design is intended to fulfil the needs of different stakeholders, as illustrated by several case studies which demonstrate the model's applicability ; Peer reviewed

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.

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.