Piero Campalani

According to our database1, Piero Campalani authored at least 11 papers between 2010 and 2016.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

Legend:

Book 
In proceedings 
Article 
PhD thesis 
Dataset
Other 

Links

On csauthors.net:

Bibliography

2016
Big Data Analytics for Earth Sciences: the EarthServer approach.
Int. J. Digit. Earth, 2016

2014
Point of view: Long-Term access to Earth Archives across Multiple Disciplines.
Comput. Stand. Interfaces, 2014

2013
Geostatistical modelling of PM10 mass concentrations with satellite imagery from MODIS sensor.
PhD thesis, 2013

Making Time Just Another Axis in Geospatial Services.
Proceedings of the 2013 20th International Symposium on Temporal Representation and Reasoning, 2013

Improving Efficiency of Grid Representation in GML.
Proceedings of the 27th International Conference on Environmental Informatics for Environmental Protection, 2013

2012
Downscaling Aerosol Optical Thickness to 1 Km<sup>2</sup> Spatial Resolution using Support Vector Regression Replied on Domain Knowledge.
Proceedings of the ICPRAM 2012, 2012

Finding my CRS: a systematic way of identifying CRSs.
Proceedings of the SIGSPATIAL 2012 International Conference on Advances in Geographic Information Systems (formerly known as GIS), 2012

2011
Validation of PM MAPPER aerosol optical thickness retrievals at 1×1 km<sup>2</sup> of spatial resolution.
Proceedings of the 19th International Conference on Software, 2011

Validation of Support Vector Regression in deriving aerosol optical thickness maps at 1 km<sup>2</sup> spatial resolution from satellite observations.
Proceedings of the 2011 IEEE International Symposium on Signal Processing and Information Technology, 2011

On the Automatic Prediction of PM10 with in-situ measurements, satellite AOT retrievals and ancillary data.
Proceedings of the 2011 IEEE International Symposium on Signal Processing and Information Technology, 2011

2010
Aerosol Optical Thickness Retrieval from Satellite Observation Using Support Vector Regression.
Proceedings of the Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications, 2010


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