ISPRS ICWG III/IVb

Remote Sensing Data Quality

Working Group Officers
Chair
	Hussein M. Abdulmuttalib   GIS Department, Dubai Municipality Baniyas Road Deira, Dubai UAE + 971 555500451 husseinma@dm.gov.ae home page
Co-Chair
	Guoman HUANG   Chinese Academy of Surveying and Mapping No. 28 Lianhuachixi Road Haidian District Beijing 100830 P.R. CHINA +86 10 63880615 +86 10 63880804 huang.guoman@casm.ac.cn
Co-Chair
	Thomas Blaschke   Department of Geoinformatics - Z_GIS University of Salzburg Schillerstraße 30 5020 Salzburg AUSTRIA +43 662 8044 7525 +43 662 8044 7589 thomas.blaschke@sbg.ac.at home page
Secretary
	Árpád Barsi   Department of Photogrammetry and Geoinformatics Budapest University of Technology and Economics Budapest 1111 Műegyetem rkp. 3 HUNGARY +36 1 463 1186 +36 1 463 3084 barsi.arpad@epito.bme.hu home page
Supporters
Key Support Personnel
	Anna Zih   Dubai Municipality GIS Department Baniyas Road Deira Dubai UAE + 971 56 4898599 grasf@dm.gov.ae home page

Terms of Reference

• Assess the current status of scientific development and applied practices, related to Remote Sensing Data Quality, and suggest development plans, researches, activities and projects for the improvements and advent of the same.
• Liaison with sister commissions and WGs involved partially with data quality, and also with other International organizations to assist the development and improvement of Remote Sensing Data Quality Standards and Protocols, and promote their implementations worldwide.
• Inventory and classify the elements of Remote Sensing Data Quality, and relate it to the quality of other disciplines, the usage and fitness of purpose, and to the simulated world model of data and information.
• Encourage and support scientific works that deal with the improvement of remote sensing data quality processes and management steps, also that clarify and regulate the effects of error propagation due to acquisition, referencing, storing and archiving, processing, analysing, reporting and other operations related to remotely sensed data.
• Clarify the quality aspects of Remote Sensing data, resulting and inherited from raster or vector-raster operations, such as but not limited to pixel processing, segmentation, classification, object base image analysis, image sensitivity analysis, etc.
• Study the reliability and quality measures of Remote Sensing data, in relation to the different application areas that encounter remote sensing, such as environmental monitoring and sustainability, urban planning, city smartness and quality of life, also humanitarian projects for the developing world among other areas of application.
• Assess Remote Sensing Data Quality with respect to the usage of automated major signatures of the electromagnetic spectral different phases, as much as the relations of the type to the sensors used and the area of application, coverage, visits. Sensor positions, sky clearness, etc.