ISPRS WG II/4

AI/ML for Geospatial Data

Our Mission

ISPRS Working Group II/4 aims at promoting machine learning methods to process, analyze, and interpret geospatial data. Nowadays, a large number of sensors provide an ever-increasing amount of observations at various scales and resolutions, making the processing pipelines strive for efficient and powerful methods to extract valuable geometric and semantic information from the data. Geospatial data such as imagery and point clouds can be collected not only as part of specific aerial/satellite imaging campaigns and mobile mapping but also from publicly available datasets such as OpenStreetMap. This is challenging because data is large and comes with noise, limited labels, and wide biases. Future challenges include the integration of domain knowledge, the extraction of new scientific knowledge, and the combination of multiple complementary views (e.g., street view panoramas and aerial images) from different sensors and campaigns, but also the combined use of different data types (e.g., text and images).

This WG fosters collaboration between the Photogrammetry & Remote Sensing and the Computer Vision & Machine Learning communities. Organized workshops at ISPRS events and CV & ML conferences that address the challenges at the intersection of these communities promote mutual awareness and knowledge exchange. This WG will closely collaborate with other WGs such as WG II/5 ‘Temporal Geospatial Data Understanding’ and sister organizations such as IAPR and IEEE IADF.

 

 

Working Group Officers

Chair

Ribana Roscher, ChairRibana Roscher
 
Forschungszentrum Jülich GmbH
Institute of Bio- and Geosciences (IBG)
Plant Sciences (IBG-2)
Wilhelm-Johnen-Straße
52428 Jülich
GERMANY
+49 2461/61-5957

 

Co-Chair

Devis Tuia, Co-ChairDevis Tuia
 
Environmental Computational Science and Earth Observation
School of Architecture, Civil and Environmental Engineering
Ecole Polytechnique Fédérale de Lausanne
1950 Sion
SWITZERLAND

 

Co-Chair

Jefersson A. dos Santos, Co-ChairJefersson A. dos Santos
 
Department of Computing Science and Mathematics
Faculty of Natural Sciences
University of Stirling
Stirling, FK9 4LA
SCOTLAND, UK

 

Secretary

Maria Vakalopoulou, SecretaryMaria Vakalopoulou
 
MICS Laboratory
University of Paris-Saclay
CentraleSupélec, Gif-sur-Yvette, France, 91190
FRANCE

 


Supporters

Supporter
Michael Kampffmeyer, SupporterMichael Kampffmeyer
 
UiT Machine Learning Group
Department of Physics and Technology
UiT The Arctic University of Norway
9019 Tromsø
NORWAY

 


Terms of Reference

  • Machine learning, deep learning
  • Analysis and interpretation of geospatial data
  • Scene classification, semantic (instance) segmentation, object detection
  • Unsupervised, supervised, weakly supervised, transfer, self-supervised learning
  • Human-in-the-loop learning
  • Multi-view, multi-modal learning
  • Precision agriculture, environmental/urban monitoring
  • Biases analysis, interpretability and explainability of machine learning models

WG II/4

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The International Society for Photogrammetry and Remote Sensing is a non-governmental organization devoted to the development of international cooperation for the advancement of photogrammetry and remote sensing and their applications. The Society operates without any discrimination on grounds of race, religion, nationality, or political philosophy.

Our Contact

ISPRS
c/o
Leibniz University Hannover
Institute of Photogrammetry and GeoInformation
Nienburger Str. 1
D-30167 Hannover
GERMANY
Email: isprs-sg@isprs.org