dataesr/scientific-paragraphs-categorization huggingface dataset:
The assessment outcomes then form the basis for the Government of Botswana declaring a drought year with the launch of corresponding relief programmes, thus linking to local levels. In the field of water management, there is an enormous potential to link satellite data with citizen science to improve the assessment and monitoring of water levels in streams, precipitation, and groundwater. As the technical aspects of using satellite EO for hydrological modelling are improving  (Walker et al. 2019) , and more technologies exist such as cameras, web-based mapping, and navigation systems, there are more examples of citizens providing field data that can be directly used to improve water resources management  (Buytaert et al. 2014). For example, in Kenya, residents are sending text messages containing their visual observations of the water level in their local streams to researchers who then use that data to improve models that ultimately improve water allocation  (Weeser et al.
2018). Although this example does not directly involve satellite EO, the potential for the three-way synergy between citizen science, satellite data, and computing and communication technology is obvious. In this example, participants were very enthusiastic, sending text messages costing 1 US cent. Researchers did find that paying a small reward or at least a reimbursement increased the participation rate by seven. Simple cell phones that could send text messages were a reliable enough data source for instantaneous water level monitoring. Motivation for participation stemmed directly from a local susceptibility to floods and droughts and a clear understanding that data can help mitigate those problems. However, that relationship must be reinforced by regular feedback between the research and the citizen scientists.
org). We are grateful to the anonymous reviewers for their constructive comments. This study contributes to the Programme on Ecosystem Change and Society (https:// pecs-scien ce. org/) and the Global Land Programme (https:// www. glp. earth/). Funding Open access funding provided by University of Bern. H2020 European Research Council, 101001200, Chinwe Ifejika Speranza, Bundesministerium für Bildung und Forschung, SASSCAL, Jörg Helmschrot, French National Research Institute for Sustainable Development, David Baratoux. Open Access This article is licensed under a Creative Commons Attribution 4. 0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made.
Acknowledgments This paper is an outcome of the Workshop on " Global Change in Africa: Role of Space Observations " held at the  International Space Science Institute (ISSI) ,  Bern, Switzerland , and remotely on  11-15 January 2021. We are grateful to ISSI, and to the conveners of this Workshop and this Special Issue. CIS' and FAO's contributions are in the context of the  European Research Council (ERC)  funded project under the European Union's  Horizon 2020 research and innovation programme  (grant agreement No  101001200 -SUSTAINFORESTS ). Acknowledgement is given to the  German Federal Ministry  of.
The potential of citizens as a source of geographical information has been recognized for many years. Such activity has grown recently due to the proliferation of inexpensive location aware devices and an ability to share data over the internet. Recently, a series of major projects, often cast as citizen observatories, have helped explore and develop this potential for a wide range of applications. Here, some of the experiences and learnings gained from part of one such project, which aimed to further the role of citizen science within Earth observation and help address environmental challenges, LandSense, are shared. The key focus is on quality assurance of citizen generated data on land use and land cover especially to support analyses of remotely sensed data and products. Particular focus is directed to quality assurance checks on photographic image quality, privacy, polygon overlap, positional accuracy and offset, contributor agreement, and categorical accuracy.
Introduction Citizen science for the provision of geographical data has a long history and is known by a variety of terms  (See et al. 2016 ) but has developed rapidly since the advent of volunteered geography and volunteered geographical information (VGI) pioneered by  Goodchild (2007). The term VGI is taken here to relate to geographical information provided by the public for little reward if any. Developments such as Web 2. 0 and the proliferation of inexpensive location aware devices has greatly eased the acquisition of VGI and resulted in a considerable growth of the subject  (Capineri et al. 2016; Foody et al. 2017a). A range of fundamental issues that are central to the acquisition, storage, management, distribution and use of VGI can be encountered in citizen science projects  (Bastin, Schade, and Schill 2017; Demetriou et al.
2017 ). Here, the focus is on the quality of VGI on land use and land cover. Citizen observatories offer a potential revolution in the field of land use and land cover monitoring. Citizens can greatly increase the capacity and frequency of data collection, enabling, for example, near real-time response to emerging environmental hazards  (Ostermann, and Granell 2017)  as well as facilitating conventional activities such as map updating  (Olteanu Raimond et al. 2017a; Olteanu-Raimond et al. 2017b ). The growing potential of VGI to augment or even replace authoritative geographical data on land cover and land use, which can be expensive to acquire or have restrictions on access and use, has been recognized widely  (Fonte et al. 2015a; Stehman et al. 2018). Indeed, citizens can be an attractive source of data on land cover and land use, addressing concerns such as the amount, spatial distribution and timeliness of authoritative data.
For example, the use of citizenbased contributions is one effective way to increase the ground data available to support analyses of remotely sensed data  (See et al. 2022 ). However, concerns have been raised about the quality of VGI when compared to the traditional land surveying techniques and methods employed by bodies such as national mapping agencies. There are many concerns about the quality of citizen-derived data such as its accuracy, trustworthiness, distribution, and heterogeneity  (Flanagin, and Metzer 2008; Elwood, Goodchild, and Sui 2012; Fonte et al. 2015a; Fogliaroni, D'Antonio, and Clementini 2018; Vahidi, Klinkenberg, and Yan 2018; Severinsen et al. 2019). Quality assurance (QA) and assessment are, therefore, important, and methods to ensure that the quality of VGI can be characterized so that they are effective for user needs are required if the full potential of VGI is to be realized.
A range of approaches are available for the assessment of VGI quality  (Goodchild, and Li 2012; Senaratne et al. 2017)  and can be impacted by the source of reference data  (Mocnik et al. 2018). Issues of QA have been a major issue in citizen science projects such as LandSense. LandSense was a European Union (EU) funded project that sought to develop the use of citizen observations to enhance Earth observation studies  (Moorthy et al. 2017; Wannemacher et al. 2018). The underlying need for the LandSense project was to help achieve the full potential of remote sensing for Earth observation in environmental monitoring. The ground data requirements needed to support a remote sensing study can be demanding and difficult to satisfy, especially if the spatial and temporal dimensions of the study are large.
However, citizen science offers a means to help address key challenges with ground data. The LandSense project linked Earth observation with contemporary citizen science to help deliver quality-assured ground data to complement and support environmental monitoring systems that use remotely sensed data  (Moorthy et al. 2017). The core motivations of the project were to enhance the quality of land use and land cover products generated from remote sensing, to transform the approach to satellite monitoring and to increase the engagement of citizens in environmental monitoring activity  (Moorthy 2020). In LandSense, citizens were encouraged to collect ground data which could be useful support to environmental monitoring systems and, if appropriate, integrated with authoritative data sets. The activity, thus, enhanced the role of citizen science in Earth observation, increasing the involvement of citizens in science as well as providing a means to generate accurate, timely and low cost ground data to support the use of remote sensing in addressing major environmental challenges.
A range of application themes were studied in the LandSense project, notably urban landscape dynamics, agricultural land use and forest and habitat monitoring. For each of the aforementioned themes, one or more pilot or demonstration studies were undertaken. Here, attention focuses on QA of data acquired from four pilot studies on urban landscape dynamics (OSMlanduse validation, City. Oases, MijnPark. NL and Paysages), one agricultural pilot (CropSupport) and one forest and habit monitoring (Natura. Alert) pilot. As with other studies focused on citizen science contributions to environmental research (e. g. Salk et al. 2016; Mobasheri, Zipf, and Francis 2018; De Marchi, Ficorilli, and Biggeri 2022) , attention was focused on lessons learnt from these studies. This adds to the growing literature on how experiences gained in crowdsourcing projects can enhance remote sensing research, notably as a source of ground reference data  (See et al.
2022) , and aid the design of future research programs. There are many dimensions to data quality (Hickling Arthurs Low Corporation 2012;  Fonte et al. 2015a ). Here, the focus is entirely in relation to the data acquired for the LandSense project and its objectives. LandSense QA processes were founded upon a review of the literature on quality assessment of VGI and the work of two previous EU funded projects: COST Action TD1202  (Foody et al. 2017a ) and the COBWEB project  (Higgins et al. 2016). Some of the key details are revisited here as they impacted the design and execution of QA checks undertaken in LandSense. Building on the foundations provided by the literature and the two prior EU funded projects, a set of QA tools to meet the objectives of the LandSense project were developed and tested.
In the course of this activity, good practices for QA of citizen-contributed data emerged and are reported here. Although the discussion is focused on LandSense applications, many of the practices and lessons learned should have broader relevance and could be applicable or adaptable to other studies. Good practice guidelines and protocols for VGI are emerging (e. g. Fonte et al. 2015b; Mooney et al. 2016; Minghini et al. 2017 ) and some are revisited and expanded upon based on the experiences gained from the LandSense project. The focus throughout is on good practices for projects using VGI recognizing that there is a desire to not alienate the citizen community by making the acquisition process too onerous or difficult  (Fonte et al. 2015b; Foody et al. 2017b; Minghini et al. 2017). Guidelines are provided for each of the QA tools developed and implemented during the LandSense project.
Photographic imagery Photographs, especially if geotagged, are a popular form of VGI  (Brabyn, and Mark 2011; Elwood, Goodchild, and Sui 2012; Feick, and Robertson 2015; Chesnokova, and Purves 2018). Smartphones have, for example, made it effort-free to acquire geotagged photographs, and these can be interpreted to yield useful land cover and land use information  (Antoniou, Morley, and Haklay 2010; Xu et al. 2017). Photographs could be acquired for a variety of purposes and their use as VGI may sometimes have been unplanned. This is, for example, the case with the photography acquired in the Degree Confluence Project (https://confluence. org) where the photographs were acquired primarily as a leisure activity but which could be interpreted to yield land cover information and used as ground reference data to support analyses of remotely sensed data  (Iwao et al.
It is critical that a clear and unambiguous set of classes are defined and that all of the contributors can use these labels effectively. To help achieve this situation, the response design should include means to enhance the consistency in labeling  (Olofsson et al. 2014)  perhaps by provision of supporting documentation (e. g. classification keys and examples) and training in labeling. It may also be useful to acquire self-confidence ratings in labeling (e. g. Comber, See, and Fritz 2014)  to help filter a data set, although these can be problematic as poor contributors may over-estimate their abilities  (Ehrlinger et al. 2008). It is also important to ensure that the contributors are labeling the same geographical feature. In LandSense, a key aid to this is to ensure geolocation and hence checks on positional accuracy can usefully aid the QA of labeled data.
A fundamental issue is the need to define clearly and unambiguously what is being labeled. In some LandSense applications, the focus was on the labeling of remotely sensed imagery, often obtained via an image classification analysis to yield a thematic map. In such circumstances, there is a need to define the minimum mapping unit and a spatial unit for the accuracy assessment  (Olofsson et al. 2014). The former may vary from application to application but can have important impacts on a map and its accuracy which are often influenced by the size and spatial distribution of the land cover patches  (Saura 2002; Knight, and Lunetta 2003). The spatial unit for accuracy assessment is a central feature in popular sitespecific approach to accuracy assessment  (Congalton, and Green 2009). In this approach to accuracy assessment, the analysis is based upon a comparison of the contributed class label and that which exists in reality and shown in the reference data set.
Background details and the LandSense pilot studies The LandSense pilot studies differed in a number of ways, which impacted on QA tasks. For example, pilot studies used dissimilar scripting languages, version control systems and deployment platforms. However, a federated approach was used to accommodate for these differences in terms of accessibility and use. The latter included a common quality control entity to identify, assess and potentially correct data quality concerns across these multiple applications, themes and pilot studies. Data standards for LandSense, and how these interact with QA procedures, included essential and recommended Data Collection Requirements (DCRs). DCRs define the nature of the data collected and how it relates to the QA functions. Essential DCRs relate to mandatory data requirements, such as privacy checks on photographic imagery and time stamps on data.
Recommended DCRs are not mandatory requirements but relate to data protocols aimed at promoting good data quality. For example, the latter include checking photographs for blurring and appropriate brightness levels as well as the level of positional accuracy for contributed data. Throughout the evolution of the LandSense project, all pilot studies were open to change and adaptation throughout their development regarding their concept, audience, content, and technicalities. This enabled them to focus better on user needs and to take into account lessons learnt during initial data collection campaigns. As a result, data protocols evolved and were subjected to reconsideration and reframing. Hence, a key aspect for good practice of the LandSense data protocols was the ability to accommodate these dynamics to ensure that ongoing operational use and development could occur simultaneously.
The pilot studies focused on the urban landscape dynamics theme witnessed changes to the data collection protocol and data schema to take better account of user's needs as well as development of more citizenfriendly tasks such as validation to take into account the lessons learned from initial data collection campaigns. This resulted in the simplification of the data collection protocol and data model. In contrast, the pilot studies for of the agricultural (CropSupport) and Forest Monitoring (Natura. Alert) themes evolved more gradually without any fundamental changes within their foci, content, or data schema. Balancing the need for each pilot study to develop its own bespoke methods whilst maintaining some standardization across the LandSense project was a challenge throughout the project.
This is not surprising given the diversity of the pilot studies. In addition, the differing requirements of the various stakeholders to the LandSense project (e. g. users, government, commercial and academic) should also be considered. For example, commercial partners may be reluctant to release their intellectual property under open access conditions whereas funding organizations (e. g. the EU, national funding bodies, etc. ) may require open access as part of the funding conditions. The concepts of cohesion, adaptability, and accessibility governed the design of data structures for the LandSense pilot studies. Cohesion is the capacity to use the pilot studies in a combined manner such that they each could benefit from the others and may reuse parts where appropriate. Cohesion was aided by agreeing on shared standards.
The adoption of established geodata standards acted to simplify access and reduce friction for data access, particularly with an interlinked federation environment. For LandSense, geojson was used for vector data since it is robust, adaptable, and suited for web applications. In addition, the World Geodetic System, which is the default projection of geojson, was used as it supports deployment scalability which is important for integrating data collection over the world. Furthermore, coding in R and Python was encouraged as they can be installed at no cost, are human readable and understandable by a broader academic and non-academic public. The code was maintained within a project managed git (gitLab, gitHub) environment allowing multiple access. Adaptability is the capacity to keep a citizen science project flexible in terms of the requirements dictated by commercial, technical, scientific or policy constraints.
Examples of photographs over a range of blur levels are shown in Figure  1  and a summary of the degree of blurring from each pilot study is provided in Table  2. Note that a photograph may pass this check but still be of limited value because of other concerns such as brightness. In addition to identifying good practice for ascertaining whether an image is blurred, it is also important to consider what options are available once a blurred image has been detected. For the purposes of LandSense, and given the low number of blurred images that were found, it was determined either to exclude blurred images from the pilot study datasets or to include them with metadata highlighting their quality issues. Another option that could be considered is to apply a sharpening algorithm to reduce the blurriness of an image.
A potential model for such manual interventions was explored during the LandSense project. Using IIASA's Picture Pile software  (Danylo et al. 2018) , photographs acquired in the City. Oases pilot study (>1700 images) were manually filtered to exclude all images that did not require privacy checks. The same process could be employed post-privacy check to ensure that all privacy features have been successfully identified and anonymized. Another option explored by the City. Oases pilot was for contributors to identify manually in the pilot app any privacy features during the image upload process. In the upload page, the users were asked to blur out parts of the images which showed a face or vehicle license plate before being uploaded. When the users click the edit button on the upload page, they get to the edit page on which they can select a photograph.
The performance of the LandSense LPDS used could be improved through further training. However, it is also clear that even with training, some license plates may not be identifiable due to the unconstrained nature of the images collected. The low number of images with license plates in the LandSense dataset may not be sufficient for retraining. Suitable images would need to be sourced for retraining the algorithm. In addition, it is debatable whether the cost of retraining the LPDS would be a worthwhile use of time and resources given the low prevalence of license plates in the data collected and there would be no guarantee that any improvement would be substantial given the performance of the commercial LPDS.
Of these, 25,000 are housed in workers' hostels, 50,000 in centers for asylum seekers and emergency shelters, and 35,000 in social hostels  [16]. 2,800 are estimated to be permanently living rough in Paris, and around 1,500 in Marseille  [17]. No official French data for COVID-19 vaccine coverage in migrants, homeless, or roofless populations exist. Few global data are available on vaccine uptake among PEH/PH; we identified only three quantitative  [18] [19] [20]  studies and no reports on drivers of or barriers to COVID-19 vaccination. Given higher exposure to COVID-19 and the lack of data, it is crucial to estimate vaccination coverage among PEH/PH and to better understand determinants of vaccination. We aimed to estimate vaccination coverage in PEH and those precariously housed, and to analyse factors associated with vaccination status.
Total estimated required sample size was 3,751 (Appendix 3). In Ile-de-France, we built sampling frames for each stratum using data provided by various actors involved with these populations; these listed location and size of each site (Appendix 3). In each stratum, recruitment sites were randomly selected (first stage) proportionally to their size. Sample size per site was calculated in proportion to expected sites population, with participant sampling in the second stage depending on site type. In shelters, migrant workers' hostels, and centres for asylum seekers, individuals were selected using simple random sampling when resident lists or room lists existed, and systematic random sampling otherwise. To ensure the selected person was included, sites were visited repeatedly at different times, including weekends and evenings.
Data collection After obtaining verbal informed consent, questionnaires were administered by trained interviewers in participant's preferred language. Interviews were conducted in French, English, Arabic, Farsi, Spanish, Turkish, Wolof, and Pulaar or were translated by phone in any other language. Responses were recorded using tablets (interview form in Appendix 4). COVID-19 vaccine status was verified via the national "Pass sanitaire" phone app (TousAntiCovid).
DISCUSSION To our knowledge, few studies exist reporting COVID-19 vaccine uptake in PEH. One study examines uptake among US military veterans attending homelessness centres; this was conducted much earlier in the vaccination campaign, and reports that 46% of homeless veterans were vaccinated in August 2021, as compared to 51% of all veterans  [18]. Two recent studies in Ontario, Canada, and Denmark used database mining to derive coverage estimates for homeless individuals, which were similar to those in our Streets stratum  [19, 20]. Our study highlights that PEH/PH are less likely to receive COVID-19 vaccination than the general population (79% vs 91%). This aligns with evidence for other vaccines: 24% of immigrants to London were vaccinated against influenza vs 53% of UK-born participants [10]; 17% of Italian immigrants were vaccinated against influenza vs 40% of Italy-born citizens  [11].