
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
Staff
News

25 January 2023
Driving inclusive and green urban transitions

05 October 2022
Citizen science goes global

30 August 2022
A new dataset sheds light on the global impacts of mining
Focus
05 December 2022
Building trust in science for a sustainable future

29 November 2022
Where are they now: Olga Danylo
19 June 2022
Celebrating 50 years of systems science

Publications
Labrière, N., Davies, S.J., Disney, M.I., Duncanson, L.I., Herold, M., Lewis, S.L., Phillips, O.L., Quegan, S., Saatchi, S.S., Shchepashchenko, D. , Scipal, K., Sist, P., & Chave, J. (2023). Toward a forest biomass reference measurement system for remote sensing applications. Global Change Biology 29 (3) 827-840. 10.1111/gcb.16497.
Jasansky, S., Lieber, M., Giljum, S., & Maus, V. (2023). An open database on global coal and metal mine production. Scientific Data 10 (1) 10.1038/s41597-023-01965-y.
Tubiello, F.N., Conchedda, G., Casse, L., Pengyu, H., Zhongxin, C., De Santis, G., Fritz, S., & Muchoney, D. (2023). Measuring the world’s cropland area. Nature Food 10.1038/s43016-022-00667-9.
Shvidenko, A., Mukhortova, L., Kapitsa, E., Kraxner, F., See, L. , Pyzhev, A., Gordeev, R., Fedorov, S., Korotkov, V., Bartalev, S., & Shchepashchenko, D. (2022). A Modelling System for Dead Wood Assessment in the Forests of Northern Eurasia. Forests 14 (1) p. 45. 10.3390/f14010045.
Fan, L., Wigneron, J.-P., Ciais, P., Chave, J., Brandt, M., Sitch, S., Yue, C., Bastos, A., Li, X., Qin, Y., Yuan, W., Shchepashchenko, D. , Mukhortova, L., Li, X., Liu, X., Wang, M., Frappart, F., Xiao, X., Chen, J., Ma, M., Wen, J., Chen, X., Yang, H., van Wees, D., & Fensholt, R. (2022). Siberian carbon sink reduced by forest disturbances. Nature Geoscience 10.1038/s41561-022-01087-x.