Abstract
Understanding population at risks has been a focus of the LandScan program through its development of population estimates. With advancements in computer vision, deep learning technologies and access to High Performance Computing (HPC) and high resolution imagery, population estimates are now modeled at the building level. However, when those patterns are disrupted, rapid updates to population distribution estimates are needed to support humanitarian aid and response. Oak Ridge National Laboratory (ORNL) recently adapted an existing deep learning building footprint extraction model in development of a scalable approach to Building Damage Assessments (BDA). This new opportunity opens the possibility of automating BDA to support rapid population distribution estimate updates for geographic areas involved in geopolitical conflicts or natural events for humanitarian aid and response or where to focus recovery efforts. In addition, incorporate social surveys to further model human behavior under conflict or other scenarios that disrupt normal patterns of life.
| Original language | English |
|---|---|
| Title of host publication | IGARSS 2023 - 2023 IEEE International Geoscience and Remote Sensing Symposium, Proceedings |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| Pages | 907-910 |
| Number of pages | 4 |
| ISBN (Electronic) | 9798350320107 |
| DOIs | |
| State | Published - 2023 |
| Event | 2023 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2023 - Pasadena, United States Duration: Jul 16 2023 → Jul 21 2023 |
Publication series
| Name | International Geoscience and Remote Sensing Symposium (IGARSS) |
|---|---|
| Volume | 2023-July |
Conference
| Conference | 2023 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2023 |
|---|---|
| Country/Territory | United States |
| City | Pasadena |
| Period | 07/16/23 → 07/21/23 |
Funding
The authors would like to thank J. Epting, J. Kaufman, C. Woody, B. Swan, J. Pyle, D. Roddy, M. O’Shell, J. Gonzales, S. Basford, A. Wilkins, L. Yang, A. Rose, and E. Powell. And to James Gaboardi for his contribution of neighborhood boundaries. This research used resources of the Oak Ridge Leadership Computing Facility at the Oak Ridge National Laboratory, which is supported by the Office of Science of the U.S. Department of Energy under Contract No. DE-AC05-00OR22725. This manuscript has been authored by UT-Battelle, LLC under Contract No. DE-AC05-00OR22725 with the U.S. Department of Energy. The United States Government retains and the publisher, by accepting the article for publication, acknowledges that the United States Government retains a non-exclusive, paid-up, irrevocable, world-wide license to publish or reproduce the published form of this manuscript, or allow others to do so, for United States Government purposes. During times of natural disaster or conflict, human activities are disrupted, and infrastructure may be destroyed or damaged. Depending on the event and its length, updates to population counts and distribution support disaster recovery and effective aid distribution [14]. For example, in the case of the 2022 expansion of the Russian invasion of Ukraine, determining the level of building damage or habitability is key for whether populations may be found within a structure. The scale of the conflict and resulting damage requires an automated scalable workflow for detecting and developing a building damage dataset. To explore how to overcome this challenge, ORNL developed a building damage assessment (BDA) model by augmenting its preexisting BFE workflow to calculate damage levels of each detected building. High-resolution imagery was acquired prior to and after the conflict over several cities in Ukraine impacted by the conflict. These 1 This manuscript has been authored by UT-Battelle, LLC under Contract No. DE-AC05-00OR22725 with the U.S. Department of Energy. The United States Government retains and the publisher, by accepting the article for publication, acknowledges that the United States Government retains a non-exclusive, paid-up, irrevocable, world-wide license to publish or reproduce the published form of this manuscript, or allow others to do so, for United States Government purposes. The Department of Energy will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan (http://energy.gov/downloads/doe-public-access-plan).
Keywords
- building damage assessment
- human security
- machine learning
- population distribution
- remote sensing