Call for Paper


The amount of location data generated and models that are being developed is increasing quickly. Remote sensing provides exabytes of Earth observation data, sensor networks generate measurements with unprecedented velocity, social networks, autonomous cars, smart cities, and the Internet of Things (IoT) add to these collections. Traditionally, geospatial data management is based on curating datasets and catalogue services which provide the ability to filter datasets based on size, location, and thematic focus. For example, the Worldview-3 satellite observes the world at a resolution of 31cm per pixel, which translates into 10.4 million pixels per km2 , and covers 680, 000km2 a day1 resulting in more than 7 trillion pixels per day. Our ability to develop models that can recognize objects on a given image has improved tremendously in the last decade, allowing us to monitor a region to detect flooding, or forest fires using high resolution imagery and videos collected by UAVs. A limiting factor for such approaches is that it is difficult to search the huge collections for interesting patterns. Not only does one need to know where to look to find objects of interest but also what model to use for such a task? What if a forest fire breaks out in an area that is not monitored? What if prior efforts had already created models on an exact or very similar task? How should users search for such models? When models are available how should they be stored? Many applications become possible if we manage to make data collections and models searchable by content, metadata and application tasks. Application users would like to solve such challenges knowing which model to use, which task is the model relevant for and finding all objects of a certain type in a huge data cube or a large point cloud. And users will want to be able to search broadly, interactively, fast and using different or even mixed modalities. For example, you want to search using a text query and retrieve images from a satellite data collection, retrieve models from a database of existing models. Similarly, you want to search with an image for locations on Earth that have a certain similarity. You want to monitor broad areas to rapidly identify changes like emergencies or disasters to alert and guide rescue teams. When you are on the go, you might want to search with audio description of what you aim to find and you want to search across all geospatial data representations (vector, raster, text, object, fields, etc.). Our workshop brings together the art of search engine construction with both geospatial data modeling and data processing and management to provide a forum for researchers and practitioners interested in GeoSearch.


Example topics include but are not limited to:

  • Multimedia Information Retrieval

  • Search in Large Spatio-temporal Data Cubes

  • Spatio-temporal Search Interfaces

  • Broad Area Search

  • Metadata design and cataloguing of Machine Learning Models

  • Design of Data and Model Relational Structures

  • Model Discoverability

  • Design of Data

  • Raw Data Search in Remote Sensing (i.e. with clouds, top-of-atmosphere)

  • Searching Vector Geometry

  • Searching Graphs and Networks

  • Searching Spatial Trajectories

  • Search for Positioning, Localization and Navigation

  • Spatial Representation Learning for Search

  • Keyword-Search in Spatial Data

  • Spatial Index Structures for Search

  • Applications of Search in Geodata

  • Searching for topological relations (near, next, …)

  • Scalable Search Frameworks and Implementations

  • Scalable Search Algorithms for different computing technologies (HPC, cloud computing, edge computing, quantum computing)

  • Data Representations and Containers facilitating content-based retrieval

  • Ontological Methods and Learning

Workshop Co-Chairs

  • Gabriele Cavallaro, Forschungszentrum Jülich, Remote Sensing and High-Performance Computing from the European community

  • Dora B. Heras, University of Santiago de Compostela, Remote Sensing and High-Performance Computing in Europe

  • Dalton Lunga, Oak Ridge National Laboratory, Machine Learning and Remote Sensing Expert from the U.S. community

  • Martin Werner, TU Munich, Big Data and HPC for spatial computing in Europe

  • Andreas Züfle , George Mason University, U.S. community around computational geo-sciences

All inquiries for the 2021 edition shall be sent to

Important Dates

  • Paper submission: September 15, 2021

  • Acceptance decision: September 27, 2021

  • Camera ready version: TBD

  • Workshop date: November 2, 2021


This workshop consists of keynotes and individual paper presentations.

  • Full research paper: 6-8 pages

  • Short research paper or demo paper: 4 pages

Full research papers should present mature research on a specific problem or topic in the context of geospatial search. We also welcome short research articles or industry demonstrations of existing or developing methods, toolkits, and best practices for AI applications in the geospatial domain.

Manuscripts should be submitted in PDF format and formatted using the ACM camera-ready templates available at All submitted papers will be peer reviewed to ensure the quality and the clarity of the presented research work. Submissions will be single-blind — i.e., the names affiliations of the authors should be listed in the submitted version.

Papers should be submitted at:

Program Committee