GeoOutageKG: A Multimodal Geospatiotemporal Knowledge Graph for Multiresolution Power Outage Analysis

• URL: http://arxiv.org/abs/2507.22878v1
• Date: Wed, 30 Jul 2025 17:54:38 GMT
• Title: GeoOutageKG: A Multimodal Geospatiotemporal Knowledge Graph for Multiresolution Power Outage Analysis
• Authors: Ethan Frakes, Yinghui Wu, Roger H. French, Mengjie Li,
• Abstract summary: We propose GeoOutageKG, a multimodal knowledge graph that integrates diverse data sources.<n>GeoOutageKG includes over 10.6 million individual outage records spanning from 2014 to 2024, 300,000 NTL images spanning from 2012 to 2024, and 15,000 outage maps.
• Score: 8.339213346405051
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Detecting, analyzing, and predicting power outages is crucial for grid risk assessment and disaster mitigation. Numerous outages occur each year, exacerbated by extreme weather events such as hurricanes. Existing outage data are typically reported at the county level, limiting their spatial resolution and making it difficult to capture localized patterns. However, it offers excellent temporal granularity. In contrast, nighttime light satellite image data provides significantly higher spatial resolution and enables a more comprehensive spatial depiction of outages, enhancing the accuracy of assessing the geographic extent and severity of power loss after disaster events. However, these satellite data are only available on a daily basis. Integrating spatiotemporal visual and time-series data sources into a unified knowledge representation can substantially improve power outage detection, analysis, and predictive reasoning. In this paper, we propose GeoOutageKG, a multimodal knowledge graph that integrates diverse data sources, including nighttime light satellite image data, high-resolution spatiotemporal power outage maps, and county-level timeseries outage reports in the U.S. We describe our method for constructing GeoOutageKG by aligning source data with a developed ontology, GeoOutageOnto. Currently, GeoOutageKG includes over 10.6 million individual outage records spanning from 2014 to 2024, 300,000 NTL images spanning from 2012 to 2024, and 15,000 outage maps. GeoOutageKG is a novel, modular and reusable semantic resource that enables robust multimodal data integration. We demonstrate its use through multiresolution analysis of geospatiotemporal power outages.

Related papers

• A Deep Learning framework for building damage assessment using VHR SAR and geospatial data: demonstration on the 2023 Turkiye Earthquake [1.6070833439280312]
  Building damage identification shortly after a disaster is crucial for guiding emergency response and recovery efforts.<n>We introduce a novel multimodal deep learning (DL) framework for detecting building damage using single-date very high resolution (VHR) Synthetic Aperture Radar (SAR) imagery.<n>Our method integrates SAR image patches, OpenStreetMap (OSM) building footprints, digital surface model (DSM) data, and structural and exposure attributes from the Global Earthquake Model (GEM)<n>Results highlight that incorporating geospatial features significantly enhances detection performance and generalizability to previously unseen areas.
  arXiv  Detail & Related papers  (2025-06-27T15:49:58Z)
• OmniGeo: Towards a Multimodal Large Language Models for Geospatial Artificial Intelligence [51.0456395687016]
  multimodal large language models (LLMs) have opened new frontiers in artificial intelligence.<n>We propose a MLLM (OmniGeo) tailored to geospatial applications.<n>By combining the strengths of natural language understanding and spatial reasoning, our model enhances the ability of instruction following and the accuracy of GeoAI systems.
  arXiv  Detail & Related papers  (2025-03-20T16:45:48Z)
• Geolocation with Real Human Gameplay Data: A Large-Scale Dataset and Human-Like Reasoning Framework [59.42946541163632]
  We introduce a comprehensive geolocation framework with three key components.<n>GeoComp, a large-scale dataset; GeoCoT, a novel reasoning method; and GeoEval, an evaluation metric.<n>We demonstrate that GeoCoT significantly boosts geolocation accuracy by up to 25% while enhancing interpretability.
  arXiv  Detail & Related papers  (2025-02-19T14:21:25Z)
• EarthView: A Large Scale Remote Sensing Dataset for Self-Supervision [72.84868704100595]
  This paper presents a dataset specifically designed for self-supervision on remote sensing data, intended to enhance deep learning applications on Earth monitoring tasks.<n>The dataset spans 15 tera pixels of global remote-sensing data, combining imagery from a diverse range of sources, including NEON, Sentinel, and a novel release of 1m spatial resolution data from Satellogic.<n>Accompanying the dataset is EarthMAE, a tailored Masked Autoencoder developed to tackle the distinct challenges of remote sensing data.
  arXiv  Detail & Related papers  (2025-01-14T13:42:22Z)
• GeoCLIP: Clip-Inspired Alignment between Locations and Images for Effective Worldwide Geo-localization [61.10806364001535]
  Worldwide Geo-localization aims to pinpoint the precise location of images taken anywhere on Earth. Existing approaches divide the globe into discrete geographic cells, transforming the problem into a classification task. We propose GeoCLIP, a novel CLIP-inspired Image-to-GPS retrieval approach that enforces alignment between the image and its corresponding GPS locations.
  arXiv  Detail & Related papers  (2023-09-27T20:54:56Z)
• A General Purpose Neural Architecture for Geospatial Systems [142.43454584836812]
  We present a roadmap towards the construction of a general-purpose neural architecture (GPNA) with a geospatial inductive bias. We envision how such a model may facilitate cooperation between members of the community.
  arXiv  Detail & Related papers  (2022-11-04T09:58:57Z)
• Satellite Image Time Series Analysis for Big Earth Observation Data [50.591267188664666]
  This paper describes sits, an open-source R package for satellite image time series analysis using machine learning. We show that this approach produces high accuracy for land use and land cover maps through a case study in the Cerrado biome.
  arXiv  Detail & Related papers  (2022-04-24T15:23:25Z)
• Tracking Urbanization in Developing Regions with Remote Sensing Spatial-Temporal Super-Resolution [82.50301442891602]
  We propose a pipeline that leverages a single high-resolution image and a time series of publicly available low-resolution images. Our method achieves significant improvement in comparison to baselines using single image super-resolution.
  arXiv  Detail & Related papers  (2022-04-04T17:21:20Z)
• GANmapper: geographical content filling [0.0]
  We present a new method to create spatial data using a generative adversarial network (GAN) Our contribution uses coarse and widely available geospatial data to create maps of less available features at the finer scale in the built environment. We employ land use data and road networks as input to generate building footprints, and conduct experiments in 9 cities around the world.
  arXiv  Detail & Related papers  (2021-08-07T05:50:54Z)
• The Multi-Temporal Urban Development SpaceNet Dataset [7.606927524074595]
  We present the Multi-Temporal Urban Development SpaceNet (MUDS) dataset. This open source dataset consists of medium resolution (4.0m) satellite imagery mosaics. Each building is assigned a unique identifier (i.e. address), which permits tracking of individual objects over time. We demonstrate methods to track building footprint construction (or demolition) over time, thereby directly assessing urbanization.
  arXiv  Detail & Related papers  (2021-02-08T18:28:52Z)
• Attentive Weakly Supervised land cover mapping for object-based satellite image time series data with spatial interpretation [4.549831511476249]
  We propose a new deep learning framework, named TASSEL, that is able to intelligently exploit the weak supervision provided by the coarse granularity labels. Our framework also produces an additional side-information that supports the model interpretability with the aim to make the black box gray.
  arXiv  Detail & Related papers  (2020-04-30T10:23:12Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.