This special GEOBIA track will be a 1.5 days conference within the multi-faceted GI_Week, taking place from July 7 – 10, 2020. As continuation of seven successful GEOBIA conferences since its launch in 2006, this special event will return “home” – to the place where it all began.

The primary goal of Earth observation (EO) is to map, analyse, and monitor the status and dynamics of complex Earth system phenomena. Through the spatial lens and ‘imaging’ reality, we shall obtain quantifiable indicators and other objective information for improved decision-making and status reporting. Within EO applications, object-based image analysis (OBIA) has become a key methodological enabler to address spatial, topological, and hierarchical properties of image objects  with an underlying conceptual model close to the way how we perceive (and mentally organise) reality.

Recent advances in big EO data management and analytics, including data cube solutions, massive data storage and access infrastructure, has pushed for higher grounds in operational image processing and analysis-ready data; but often re-emphasizing per-pixel analysis. Machine learning, in particular deep learning using CNNs, can also employ scale and neighbourhood, while the performance largely depends on quality and amount of samples.  Hybrid approaches may foster the combined use of knowledge-based and (physical) model-based approaches and machine learning. On finer levels of elementary image objects, where sufficient high-quality samples are abundant, ML-strategies may outperform the tedious process of explicit object descriptors and segmentation tuning; complementary, when generating composite objects on higher organisational levels, where samples are sparse and clear models and definitions exist, a knowledge-based direct modelling of object properties and relationships may be more cost- and time-efficient.

Topics include, but are not limited, to:

  • Spatio-temporal concepts in image understanding
  • Multi-scale image analysis
  • Object-specific quality indicators
  • Hybrid approaches (combining machine learning and machine teaching)
  • State of play of OBIA methods and tools in open source and commercial software environments
  • Best practice OBIA in operational solutions (e.g. Copernicus info services, SDG indicators, etc.)
  • Big EO data – back to pixels?

Information: Call for Papers (as pdf-file)


Contributions can be submitted as full paper or short paper until February 1, 2020 or as poster until May 1, 2020 as part of a GI_Forum “Thematic Focus”. Any contribution needs to be submitted via the conference submission website and will be subject to double-blind peer review. Authors of accepted full and short papers are invited to present and discuss their papers in the especially designed GEOBIA track. For guidelines on submission, please visit

Accepted full and short papers will be published in the Open Access GI_Forum Journal. Publication will be online (see The publication is included in Thomson Reuters Conference Proceedings Citation Index.

Summer School

Building on the success of Copernicus/EO-related summer schools over the last years, Z_GIS and partner organizations offer a Summer School, which provides a more in-depth understanding for the topics to be discussed during the conference. It will be a fruitful mix of conceptual and practical sessions with the opportunity for participants to interact and discuss with teachers and trying out various tools in a collaborative group effort. GEOBIA 2020 will be an integral part of the Summer School (which starts in the week before), where participants will also share their experience and outcome.

For more information, please check the summer school’s website.


Beyond the GEOBIA and the wider remote sensing community, we address all interested in learning more about the high integrative power of object-based approaches, from GI, computer vision, AI, and other neighbouring disciplines.


For questions, please contact Stefan Lang ( or Dirk Tiede (, University of Salzburg – Z_GIS.