An earth observation data cube is a software infrastructure enabling the ingestion, storage, access, and analysis of satellite data in the form of multidimensional time series at regular locations. This PhD work will consist in the development of the concepts and tools leading to implementation of an Atlantic data cubes and the development of user-targeted applications based on time series analysis. The data cube will be based on the Open Data Cube software suite. Research will be performed on the development of Analysis Ready Data (ARD), focusing on Landsat imagery and Sentinel-2 data, and on procedures and tools for data ingestion and running of analytic processes.
AIR Centre PhD grants are aimed at applicants enrolled or that comply with the requirements to enrol for PhD related studies and who wish to carry out research towards this degree. Following candidates may apply:
a) Holders of a Master’s degree, or legal equivalent, in Informatics Engineering, Informatics, Computer Science or closely related areas as considered by the Scientific Committee.
b) Holders of a “Licenciatura” degree (previous to Bologna process) with a relevant academic or scientific curriculum recognized by the Scientific Committee as adequate in Informatics Engineering, Informatics, Computer Science or closely related areas.
c) Those with an academic, scientific or professional curriculum recognized by the Scientific Committee as adequate to the Programme.
Summary of work plan
An earth observation data cube is a software infrastructure enabling the ingestion, storage, access, and analysis of satellite data in the form of multidimensional time series at regular locations. Earth Observation (EO) data cubes are emerging as a fundamental tool enabling Open and reproducible science based on earth observation data [e.g. Giuliani et al, 2020]. Although remote sensing observations are increasingly made freely available [e.g. Zhu et al, 2016], the complexity, volume, and heterogeneity of satellite data hinders its efficient use, as it requires significant expert knowledge and computing capabilities. Data cubes enable to lower the cost and effort of using satellite imagery, making it accessible to users from different domains and with diverse technical expertise, a key aspect for translating data into actionable information, and harness the full potential of earth observation data to address societal challenges.
The Macaronesian region, the area including the archipelagos of the Azores, Madeira, Canary Islands and Cape Verde, is a particularly appealing region for the exploitation of EO information. Due to its remote oceanic location, the available in-situ information is very limited, satellites being the main source of information on the oceanic and atmospheric processes taking place in the Macaronesian region. The area is a rich biodiversity spot, harboring +10 UNESCO’s Biosphere Reserves and with +100 sites identified as key biodiversity areas, and the use of satellite information is of paramount importance to support policies for biodiversity protection. The integration of EO information with coastal and in-situ observations, and the analysis of time series, is crucial for change detection and to provide reliable information to decision makers in terms of natural and anthropogenic pressures. These islands experience fast ecological and climatic changes as a result of the volcanic and erosional processes typical of oceanic islands and are among the first and worst affected by climate change, particularly sea level change.
This PhD will consist in the development of the concepts and tools leading to implementation of a Macaronesian region data cube and the development of user-targeted applications based on time series analysis. The data cube will be based on the Open Data Cube (ODC) software suite, a python-based collection of open source software supporting the deployment and operation of data cubes [Killough, 2018]. The PhD student will develop procedures and tools for data ingestion and running of analytic processes. Research will be performed on the development and ingestion of Analysis Ready Data (ARD), focusing on Landsat imagery (from USGS) and Sentinel-2 data (from ESA) [Claverie et al, 2018; Frantz, 2009]. The use of ARD imagery will enable the ingestion into the data cube of consistent (geometrically aligned and radiometrically comparable) EO information. In addition to the satellite imagery, further data sources will be considered. Procedures and tools will be developed for encoding other sources of data in the geospatial data cube such as socioeconomic information at local and regional levels, citizen-science observations, and model simulation results. The research work should make the best use and explore synergies of the AIR Centre’s EO Lab infrastructure and interaction with local stakeholders and potential users of the data will be pursued in order to gather user needs and translate them into functional and technical requirements for the data cube. Furthermore, tools facilitating the use of time series methods and AI-based approaches upon the data cube information will be developed in order to support future development of specific applications in the target area.
Claverie, M., Ju, J., Masek, J.G., Dungan, J.L., Vermote, E.F., Roger, J.C., Skakun, S.V. and Justice, C., 2018. The Harmonized Landsat and Sentinel-2 surface reflectance data set. Remote sensing of environment, 219, pp.145-161.
Frantz, D., 2019. FORCE—Landsat+ Sentinel-2 analysis ready data and beyond. Remote Sensing, 11(9), p.1124.
Giuliani, G., Chatenoux, B., Piller, T., Moser, F. and Lacroix, P., 2020. Data Cube on Demand (DCoD): Generating an earth observation Data Cube anywhere in the world. International Journal of Applied Earth Observation and Geoinformation, 87, p.102035.
Killough, B., 2018, July. Overview of the open data cube initiative. In IGARSS 2018-2018 IEEE International Geoscience and Remote Sensing Symposium (pp. 8629-8632). IEEE.
Zhu, Z., Wulder, M.A., Roy, D.P., Woodcock, C.E., Hansen, M.C., Radeloff, V.C., Healey, S.P., Schaaf, C., Hostert, P., Strobl, P. and Pekel, J.F., 2019. Benefits of the free and open Landsat data policy. Remote Sensing of Environment, 224, pp.382-385.
Name(s) of supervisor(s)
Supervisor: Susana Alexandra Barbosa, INESC TEC
Co-supervisor: João Rocha Silva, University of Porto, Faculty of Engineering
Name(s) of hosting institution(s)
INESCTEC – Institute for Systems and Computer Engineering, Technology and Science, University of Porto
Identification of PhD program and its deadlines
PhD in Informatics Engineering
Please check PhD program’s website for application deadlines here.
José Borbinha (Chairman), IST, University of Lisbon, Portugal
Eduardo Brito Azevedo, University of Azores, Portugal
Mateo Valero, Barcelona Supercomputing Center, Spain
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