Introducing SpaceNet 9 - Cross-Modal Satellite Imagery Registration for Natural Disaster Responses

Conference · Mon Jul 01 00:00:00 EDT 2024

DOI:https://doi.org/10.1109/IGARSS53475.2024.10640611· OSTI ID:2447322

Hansch, Ronny ^[1]; Arndt, Jacob ^[2]; Ambrozio Dias, Philipe ^[2]; Potnis, Abhishek ^[2]; Lunga, Dalton ^[2]; Bacastow, Todd ^[3]

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1. German Aerospace Center (DLR)
2. ORNL
3. IEEE GRSS

Computer vision algorithms are increasingly leveraged to accelerate geospatial analysis for disaster response and recovery. As the diversity of remote sensing imagery grows with optical, SAR, and other modalities, a perquisite for analytics is cross-modal image registration. There is a high potential to harness computer vision for this pre-processing requirement toward enabling downstream analytics such as heterogeneous change detection, automated feature extraction, and data fusion. Advancement in these areas has the potential to simplify data wrangling tasks and further accelerate disaster response timelines. The SpaceNet 9 challenge (launching in mid-2024) focuses on addressing the cross-modal image registration problem and demonstrating the utility of such modules on earthquake impacted scenarios. This paper describes the motivation for the SpaceNet 9 and provides a first overview of the dataset, the baseline algorithm, and implications for seeking cross-modal image registration in Earth observation. Code is available at https://github.com/SpaceNetChallenge/SpaceNet9.

• Research Organization: Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)
• Sponsoring Organization: USDOE
• DOE Contract Number: AC05-00OR22725
• OSTI ID: 2447322
• Resource Type: Conference paper/presentation
• Conference Information: 2024 IEEE International Geoscience and Remote Sensing Symposium (IGARSS) - Athens, Greece - 7/7/2024-7/12/2024
• Country of Publication: United States
• Language: English