Visual Analytics of Remote Sensing Data from Satellites: An Investigation of Health-related Atmosphere Pollutants

Theoretical (Analytical):

Practical (Implementation):

Literature Work:


The number of active satellites in the earth's orbit is growing. Simultaneously, the size of gathered remote sensing datasets is ever increasing. This vast amount of near real-time information provides a lot of potential for various research areas and application problems. For instance, the investigation of the influence of atmospheric pollutants as nitrogen dioxide and particulate matter on air quality is of great interest for traffic and city planners, because reduced air quality negatively affects the urban population. Despite that, processing of satellite data and creating visual models is a complex task that is crucial to retrieve relevant information.

Problem Statement

The primary objective of this project is to transform public accessible satellite data to develop visualization methods. These models shall support interactive analysis and task-oriented decision-making. Remote sensing information from satellites have some similar movement features. Is it possible to create visualizations having a modular structure that enables users to decide which features are relevant to their specific concern? Can we integrate additional information as spatial characteristics to provide meaningful context to the user? Which domain-specific tasks can benefit from a performant application providing near real-time information? The focus of this project is to develop a fast and performant pipeline to process current remote sensing information. Afterward, a task-oriented application can visualize the prepared data. The development of suitable methods dealing with spatial and temporal information builds on the transformation pipeline..


  • Implementation of a performant modular pipeline that preprocesses and transforms remote sensing near real-time data.
  • Integration of satellite information with spatial context.
  • Develop a visual application presenting meaningful information to the user.
  • Evaluation of the implemented pipeline and developed visualization models.


  • Excellent programming skills in Java, D³, JavaScript
  • Advanced knowledge of spatial databases (Postgres)
  • Good knowledge about information visualization
  • Readiness to do creative research work independently
  • Basic understanding of client-server interaction, RESTful web services, JSON, Maven
  • Version control with Git


  • Scope: Master
  • 6 Month Project
  • 6 Month Thesis
  • Start: 2018



  • Engel-Cox, J. A., Holloman, C. H., Coutant, B. W., & Hoff, R. M. (2004). Qualitative and quantitative evaluation of MODIS satellite sensor data for regional and urban scale air quality. Atmospheric environment38(16), 2495-2509.
  • Duncan, B. N., Prados, A. I., Lamsal, L. N., Liu, Y., Streets, D. G., Gupta, P., ... & Burton, S. P. (2014). Satellite data of atmospheric pollution for US air quality applications: Examples of applications, summary of data end-user resources, answers to FAQs, and common mistakes to avoid. Atmospheric environment94, 647-662.
  • Engel-Cox, J. A., Hoff, R. M., & Haymet, A. D. J. (2004). Recommendations on the use of satellite remote-sensing data for urban air quality. Journal of the Air & Waste Management Association54(11), 1360-1371.
  • Chi, M., Plaza, A., Benediktsson, J. A., Sun, Z., Shen, J., & Zhu, Y. (2016). Big data for remote sensing: Challenges and opportunities. Proceedings of the IEEE104(11), 2207-2219.