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NODES will mobilize the tools of citizen and data science combined with Earth observations to monitor, analyze, and foster progress towards the UN Sustainable Development Goals (SDGs).
To realize this vision, NODES will exploit novel data ecosystems in which several actors interact via infrastructure, analytics, and applications to produce, analyze, exchange, and consume data.
Three main cross-fertilizing pillars characterize the research focus of NODES:
- Advancing the field of citizen science: NODES will remain at the forefront of citizen science from conceptual advancements to innovations in citizen science practice, including the development of new means to acquire, analyze, and openly share citizen science data, as well as how citizen science can contribute to the SDGs and sustainability.
- Enriching Earth observation: NODES will utilize new types of Earth observation technology (e.g., high-resolution imagery, drones, and the Internet of things (IoT)) and develop applications that focus on the interplay between Earth observation and citizen science using Geo-Wiki tools, various forms of remote sensing, and on-site observation.
- Exploiting the digital revolution: NODES will harness the opportunities arising from the digital revolution by exploiting advancements in computing capabilities, data science (e.g., machine and deep learning), environmental informatics, and the geospatial sciences, among others, to enhance integrated systems science research and generate new and innovative data sets for further insights.
Projects
Urban ReLeaf - Citizen-powered data ecosystems for inclusive and green urban transitions
Open-Earth-Monitor Cyberinfrastructure (OEMC)
Staff
Zoriana Romanchuk
Research Assistant (NODES)
Inian Moorthy
Research Scholar (NODES)
Juan Carlos Laso Bayas
Research Scholar (NODES)
Gary Watmough
Guest Research Scholar (NODES)
News
16 December 2024
The collaborative power of AI and citizen science in advancing the Sustainable Development Goals
Citizen science and artificial intelligence (AI) offer immense potential for tackling urgent sustainability challenges, from health to climate change. Combined, they offer innovative solutions to accelerate progress on the UN Sustainable Development Goals (SDGs). IIASA researchers explored the synergies between citizen science and AI, specifically highlighting how the integration of citizen science data and approaches into AI can enhance sustainable development monitoring and achievement while mitigating AI risks.
06 December 2024
Knowledge platform Recodo boosts agrobiodiversity action
The FRAMEwork project has unveiled its updated knowledge and action platform, Recodo, enhancing its features and design to better serve stakeholders in agrobiodiversity. Developed by researchers in the IIASA Novel Data Ecosystems for Sustainability Research Group and other project partners, Recodo connects and empowers diverse actors and audiences, including Farmer Clusters and other agrobiodiversity stakeholders.
03 December 2024
MINE-THE-GAP: Advancing responsible mining monitoring with satellite data and AI
As global demand for raw materials intensifies due to the material needs of digitalization and decarbonization, the new MINE-THE-GAP project aims to address a critical knowledge gap in mining's environmental and social impacts by creating scalable, reliable mining indicators using advanced satellite data and artificial intelligence (AI).
Events
Focus
17 October 2024
AI at IIASA for earth monitoring in the era of transition
As the TED AI conference unfolds in Vienna, Ian McCallum, IIASA Novel Data Ecosystems for Sustainability Research Group Leader, brings a fresh perspective on the pivotal role of artificial intelligence in Earth monitoring. He explores how AI can revolutionize our understanding of environmental changes and support sustainable practices in an era marked by significant transitions.
29 August 2024
Experiencing the science that we write about
IIASA researcher, Linda See, shares her experiences at the coalface of where plastic pollution is collected by volunteers during a beach cleanup event.
Publications
Fraisl, D. , See, L. , Fritz, S. , Haklay, M., & McCallum, I. (2024). Leveraging the collaborative power of AI and citizen science for sustainable development. Nature Sustainability 10.1038/s41893-024-01489-2. Gustafson, E.J., Lucash, M.S., Shvidenko, A., Sturtevant, B.R., Miranda, B.R., Shchepashchenko, D. , & Matsumoto, H. (2024). Climate change and disturbance interact to alter landscape reflectivity (albedo) in boreal forests across a large latitudinal gradient in Siberia. Science of the Total Environment 956 e177043. 10.1016/j.scitotenv.2024.177043. Parente, L., Sloat, L., Mesquita, V., Consoli, D., Stanimirova, R., Hengl, T., Bonannella, C., Teles, N., Wheeler, I., Hunter, M., Ehrmann, S., Ferreira, L., Mattos, A.P., Oliveira, B., Meyer, C., Şahin, M., Witjes, M., Fritz, S. , Malek, Z. , & Stolle, F. (2024). Annual 30-m maps of global grassland class and extent (2000–2022) based on spatiotemporal Machine Learning. Scientific Data 11 e1303. 10.1038/s41597-024-04139-6. Barthelme, P., Darbyshire, E., Spracklen, D.V., & Watmough, G. (2024). Detecting Vietnam War bomb craters in declassified historical KH-9 satellite imagery. Science of Remote Sensing 10 e100143. 10.1016/j.srs.2024.100143. Santoro, M., Cartus, O., Quegan, S., Kay, H., Lucas, R.M., Araza, A., Herold, M., Labrière, N., Chave, J., Rosenqvist, Å., Tadono, T., Kobayashi, K., Kellndorfer, J., Avitabile, V., Brown, H., Carreiras, J., Campbell, M.J., Cavlovic, J., Bispo, P.d.C., Gilani, H., Khan, M.L., Kumar, A., Lewis, S.L., Liang, J., Mitchard, E.T.A., Pacheco-Pascagaza, A.M., Phillips, O.L., Ryan, C.M., Saikia, P., Shchepashchenko, D. , Sukhdeo, H., Verbeeck, H., Vieilledent, G., Wijaya, A., Willcock, S., & Seifert, F.M. (2024). Design and performance of the Climate Change Initiative Biomass global retrieval algorithm. Science of Remote Sensing 10 e100169. 10.1016/j.srs.2024.100169.