Abstract
Deep learning has proven its effectiveness in numerous tasks for remote sensing scene understanding. However there is an increasing interest to explore fusion of domain-specific background information to the deep neural network to further improve its performance. Remote sensing researchers are also working towards developing models that generalize and adapt to multiple applications. Generalization challenges coupled with the scarcity of large corpora of high-quality noise-free labelled data, have together fueled an interest for leveraging background information. Knowledge graphs serve as excellent choice to represent domain-specific information in a structured, standardized and extensible manner. Integrating symbolic knowledge representations in the form of Knowledge Graph Embedding (KGE) to perform neuro-symbolic reasoning is an emerging research direction promising significant impacts. This vision paper seeks to position ideas and provoke early thoughts toward advancing neuro-symbolic artificial intelligence in the context of geospatial challenges. Specifically, it conceptualizes and elaborates on an architecture for infusing geospatial knowledge from knowledge graph in a deep neural network pipeline. As guiding case studies - land-use land-cover classification, object detection and instance segmentation can benefit from infusing spatio-contextual information with remote sensing imagery. The discussion further reflects on and articulates the challenges and explainable AI opportunities anticipated when scaling and maintaining large-scale geospatial knowledge graphs.
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 | 1400-1403 |
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
This manuscript has been authored by UT-Battelle, LLC, under contract DE-AC05-00OR22725 with the US Department of Energy (DOE). The US government retains and the publisher, by accepting the article for publication, acknowledges that the US government retains a nonexclusive, paid-up, irrevocable, worldwide license to publish or reproduce the published form of this manuscript, or allow others to do so, for US government purposes. DOE 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
- deep learning
- geospatial knowledge graphs
- knowledge graph embedding
- knowledge-infused learning
- neuro-symbolic
- remote sensing
- representation learning
- scene understanding