A Paripaadal Perspective on the Unique Identification of Pandiya Nadu Poets

Paripadal is one of the texts of Sangam literature. Among the 13 poets who sang paripadal, the people of Pandiya Nadu have been singled out in this article. Everyone knows that Paripaadal is a Pandiya Nadu. In addition to Madurai, Vaiyai, Tiruparangunram, and Azagarkoil are found as hymns in this book. Its songs are sung on the topics of Vaiyai, Sevvel, and Thirumal. It is not possible to conclude that all the poets who sang were poets of Pandiya Nadu, as the scene of the song was in the Pandiya Nadu areas around Madurai. Some of the poets have sung the praises of the Pandiya region or the Pandiya king in allegorical and laudatory terms in the context of praising the Pandiya region or the Pandiya king in their songs. However, this structure is not found in the songs of some poets. They sing only what they have come to sing. In the songs sung by poets, knowingly or unknowingly, they write about their land, country, and king in a state of admiration and through metaphors. On the basis of this, it is possible to identify the teachers Nallanthuvanar, Ilamperuvazhuthiyar, Karumpillaipoothanar, Keeranthaiyar, Kundramboothanar, Nappannar, Nalvazhuthiyar, Nallazhisiyar, and Mayodakovanar as Pandiya Nadu poets. However, this is not the case in the verses of some of the poets, and the four poets, Kaduvan Ilaveinanar, Kesavanar, Nallasthanar, and Nallazhuniyar could not be distinguished as Pandiya folk

LandSense: A Citizen Observatory and Innovation Marketplace for Land Use and Land Cover Monitoring

The LandSense Citizen Observatory is aggregating innovative EO technologies, mobile devices, community-based environmental monitoring, data collection, interpretation and information delivery systems to empower communities to monitor and report on their environment

Urban ReLeaf: Citizen-powered data ecosystems for inclusive and green urban transitions

Session: Citizen science uptake in official data and decision ecosystems – establishing a new common practice In recent years citizen science has become widely recognised for its potential beyond science, e.g., to provide data to official assessment and accounting systems, helping to close spatial and temporal data gaps, or to provide methods to enhance public participation in the scientific and policy discourse, amongst others. Lighthouse projects across the globe have demonstrated that citizen science data can serve SDG monitoring (Ghana), outweigh official data sources for official air quality reporting (Flanders) or lead to urban re-design of public spaces to reduce noise pollution (Barcelona). At the same time, citizen science practices are debated for their legitimacy, in a narrow sense related to data quality, in a wider sense related to their potential of calling existing accountability systems and power structures into question. This session aims at reflecting current developments by bringing practitioners together to enhance knowledge exchange and to jointly sketch trends and key leverage points towards facilitating the uptake of citizen science data and practice in authoritative data flows and policy makin

Urban ReLeaf – Athens Pilot: Citizen-powered data ecosystems for inclusive and green urban transitions.

Session: Pathways to sustainable development through use cases in the Athens metropolis sessio

Crowdsourcing EO datasets to improve cloud detection algorithms and land cover change

Involving citizens in science is gaining considerable traction of late. With positive examples (e.g. Geo-Wiki, FotoQuest Austria), a number of projects are exploring the options to engage the public in contributing to scientific research, often by asking participants to collect some data or validate some results. The International Institute for Applied Systems Analysis (IIASA), with extensive experience in crowdsourcing and gamification, has joined Sinergise, Copernicus Masters 2016 winners, to engage the public in an initiative involving ESA’s Sentinel-2 satellite imagery. Sentinel-2 imagery offers high revisit times and sufficient resolution for land change detection applications. Unfortunately, simple (but fast) algorithms often fail due to many false-positives: changes in clouds are perceived as land changes. The ability to discriminate of cloudy pixels is thus crucial for any automatic or semi-automatic solutions that detect land change. A plethora of algorithms to distinguish clouds in Sentinel-2 data are available. However, there is a need for better data on where and when clouds occur to help improve these algorithms. To overcome this current gap in the data, we are engaging the public in this task. Using a number of tools, developed at IIASA, and Sentinel Hub services, which provide fast access to the entire global archive of Sentinel-2 data, the aim is to obtain a large data resource of curated cloud classifications. The resulting dataset will be published as open data and made available through Geopedia platform. The gamified process will start by asking users if there are clouds on a small image (e.g. 8x8 pixels at the highest Sentinel-2 resolution of 10 m/px), which will provide us with a screening process to pinpoint cloudy areas, employing Picture Pile crowdsourcing game from IIASA. The next step will involve a more detailed workflow, as users will get a slightly larger image (e.g. 64x64 pixels) and will then be asked to delineate different types of clouds: opaque clouds (nothing is seen through the clouds), thick clouds (where the surface is still discernible through the clouds), and thin clouds (where the surface is unequivocally covered by a cloud); the rest of the image will be implicitly cloud-free. The resulting data will be made available through the Geopedia portal, both for exploring and downloading. This paper will demonstrate this process and show some results from a crowdsourcing campaign. The approach will also allow us to collect other datasets in a rapid and efficient manner. For example, using a slightly modified configuration, a similar workflow could be used to obtain a manually curated land cover classification data set, which could be used as training data for machine learning algorithms

Using Citizen Science to Help Monitor Urban Landscape Changes and Drive Improvements

Citizen Science has become a vital source for data collection when the spatial and temporal extent of a project makes it too expensive to send experts into the field. However, involving citizens can go further than that – participatory projects focusing on subjective parameters can fill in the gap between local community needs and stakeholder approaches to tackle key social and environmental issues. LandSense, a Horizon 2020 project that is deeply rooted in environmental challenges and solutions, aims to establish a citizen observatory that will provide data to stakeholders, from researchers to businesses. Within this project, a mobile application has been developed that aims not only to stimulate civic engagement to monitor changes within the urban environment, but also to enable users to drive improvements by providing city planners with information about the public perception of urban spaces. The launch of a public version of such an app requires preparation and testing by focus groups. Recently, a prototype of the app was used by both staff and students from Vienna University of Technology, who contributed valuable insights to help enhance this citizen science tool for engaging and empowering the inhabitants of the city