Themes

Abstract and paper submissions to IGARSS 2027 are solicited under the following Special and General Themes. Accepted Community Contributed Themes (CCTs) will be added to the list following evaluation of the proposals. Authors will be able to select the most relevant theme(s) from the full list of Special, General, and CCTs when submitting their abstracts or papers.

IGARSS 2027 Special Themes (SP):
  • SP.1 Geoscience and Remote Sensing in the Arctic
  • SP.2 Monitoring Rapidly Changing and Hazardous Environments with Remote Sensing
  • SP.3 Agentic AI for Earth Observation
  • SP.4 Computing Paradigms and Quantum Technologies for Earth Observation
  • SP.5 Foundation Models and Embeddings for Earth Observation
  • SP.6 Datasets, Benchmarks, and Evaluation Methodologies for Earth Observation
IGARSS 2027 General Themes:

The traditional IGARSS themes are grouped into five main general areas:

  • AI and Big Data (A): Papers are solicited on novel AI, machine learning, and big data methodologies for analyzing remote sensing data.
  • Theory and Techniques (T): Papers are solicited on the theoretical foundations and techniques used in remote sensing to extract information about Earth and planetary systems.
  • Applications (C, L, M, O, P): Papers are solicited that report the results of studies applying remote sensing data analysis methods to topics in Earth and planetary science.
  • Sensors (S): Papers are solicited that address the hardware and technologies used in remote sensing missions, platforms, and instruments.
  • Data and Society (D): Papers are solicited on the distribution, management, and security of remote sensing data; policies governing those data; and the intersection of remote sensing and society.
General Themes
  • A: AI and Big Data
    • A.1: High Performance Computing in Remote Sensing
    • A.2: Foundation Models in Geoscience
    • A.3: Large-scale Machine Learning
    • A.4: Real-time Analytics, On-board Processing and Edge Computing
    • A.5: IoT in Geoscience and Remote Sensing
    • A.6: Digital Twins
  • T: Theory and Techniques
    • T.1: Interferometry: Along and Across
    • T.2: Differential SAR Interferometry
    • T.3: Multi-Channel DBF Imaging Techniques
    • T.4: PolSAR and POlInSAR
    • T.5: Bistatic and Multistatic SAR
    • T.6: Tomography
    • T.7: Sub-surface Sensing
    • T.8: Feature Extraction and Reduction
    • T.9: Image Segmentation
    • T.10: 3D Mapping
    • T.11: Object Detection and Recognition
    • T.12: Classification and Clustering
    • T.13: Inversion Techniques
    • T.14: Change Detection and Temporal Analysis
    • T.15: Hyperspectral Data Processing and Analysis
    • T.16: Unmixing Analysis
    • T.17: RFI Detection and Mitigation
    • T.18: Multimodal Data Fusion
    • T.19: Spatio-temporal Data Harmonization
    • T.20: Microwave Modeling
    • T.21: Optical Modeling
    • T.22: Polarimetric Modeling
    • T.23: Modeling for Signals of Opportunity (e.g. GNSS-R)
  • C: Cryosphere
    • C.1: Snow Cover
    • C.2: Ice Sheets and Glaciers
    • C.3: Sea Ice
    • C.4: Permafrost
  • L: Land Applications
    • L.1: Land Use Applications
    • L.2: Land Cover Dynamics
    • L.3: Forest and Vegetation: Application and Modelling
    • L.4: Forest and Vegetation: Biomass and Carbon Cycle
    • L.5: Agriculture
    • L.6: Urban and Built Environment
    • L.7: Land Surveying, Geodesy and Topography
    • L.8: Soils and Soil Moisture
    • L.9: Wetlands
    • L.10: Inland Waters
    • L.11: Geology and Geomorphology
  • M: Atmosphere Applications
    • M.1: Precipitation and Clouds
    • M.2: Numerical Weather Prediction and Data Assimilation
    • M.3: Atmospheric Sounding
    • M.4: Aerosols and Atmospheric Chemistry
    • M.5: Ionospheric Remote Sensing
  • O: Oceans
    • O.1: Ocean Biology (Color, coral reefs) and Water Quality
    • O.2: Ocean Surface Winds and Currents
    • O.3: Ocean Temperature and Salinity
    • O.4: Ocean Altimetry
    • O.5: Coastal Zones
  • G: Geosphere
    • G.1: Earth System Modeling
  • P: Remote Sensing of Planetary and other Celestial Bodies
    • P.1: Moon
    • P.2: Mars
    • P.3: Other Celestial Bodies
  • S: Sensors
    • S.1: Spaceborne SAR Missions
    • S.2: Spaceborne Passive Microwave Missions
    • S.3: Spaceborne GNSS-R Missions
    • S.4: Spaceborne Hyperspectral Missions
    • S.5: Spaceborne LIDAR Missions
    • S.6: Multi-platform Earth Observation
    • S.7: High Altitude Platforms
    • S.8: Unmanned Aerial Systems and Payloads
    • S.9: Ground-based Systems
    • S.10: Sensors Using Signals of Opportunity (e.g. GNSS-R)
    • S.11: Lidar Sensors
    • S.12: Passive Optical Multi- and Hyperspectral Sensors
    • S.13: SAR Instruments
    • S.14: Scatterometer, Clouds and Rain Radar
    • S.15: Microwave Radiometer Instruments
    • S.16: Ground Penetrating Radar
    • S.17: Advanced Future Instrument Concepts
  • D: Data and Society
    • D.1: Data Management Systems and Security
    • D.2: Remote Sensing Data and Policy Decisions
    • D.3: Education and Remote Sensing
    • D.4: Standards in Remote Sensing
    • D.5: Geographic Information Systems
    • D.6: Citizen and Open Science
    • D.7: Risk and Disaster Management (Extreme Weather, Earthquakes, Volcanoes, etc)
    • D.8: Remote Sensing for Food Security
    • D.9: Remote Sensing for Sustainable Development
    • D.10: Remote Sensing for Climate Change Impacts
  • SP: Special Themes
    • SP.1: Geoscience and Remote Sensing in the Arctic
    • SP.2: Monitoring Rapidly Changing and Hazardous Environments with Remote Sensing
    • SP.3: Agentic AI for Earth Observation
    • SP.4: Computing Paradigms and Quantum Technologies for Earth Observation
    • SP.5: Foundation Models and Embeddings for Earth Observation
    • SP.6: Datasets, Benchmarks, and Evaluation Methodologies for Earth Observation