Geolocating Earth Imagery from ISS: Integrating Machine Learning with Astronaut Photography for Enhanced Geographic Mapping

• URL: http://arxiv.org/abs/2504.21194v1
• Date: Tue, 29 Apr 2025 22:00:02 GMT
• Title: Geolocating Earth Imagery from ISS: Integrating Machine Learning with Astronaut Photography for Enhanced Geographic Mapping
• Authors: Vedika Srivastava, Hemant Kumar Singh, Jaisal Singh,
• Abstract summary: This paper presents a novel approach to geolocating images captured from the International Space Station (ISS) using advanced machine learning algorithms.<n>Our research addresses this gap by employing three distinct image processing pipelines: a Neural Network based approach, a SIFT based method, and GPT-4 model.<n>Through extensive evaluation on a diverse dataset of over 140 ISS images, our methods demonstrate significant promise in automated geolocation with varied levels of success.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This paper presents a novel approach to geolocating images captured from the International Space Station (ISS) using advanced machine learning algorithms. Despite having precise ISS coordinates, the specific Earth locations depicted in astronaut-taken photographs often remain unidentified. Our research addresses this gap by employing three distinct image processing pipelines: a Neural Network based approach, a SIFT based method, and GPT-4 model. Each pipeline is tailored to process high-resolution ISS imagery, identifying both natural and man-made geographical features. Through extensive evaluation on a diverse dataset of over 140 ISS images, our methods demonstrate significant promise in automated geolocation with varied levels of success. The NN approach showed a high success rate in accurately matching geographical features, while the SIFT pipeline excelled in processing zoomed-in images. GPT-4 model provided enriched geographical descriptions alongside location predictions. This research contributes to the fields of remote sensing and Earth observation by enhancing the accuracy and efficiency of geolocating space-based imagery, thereby aiding environmental monitoring and global mapping efforts.

Related papers

• EarthMapper: Visual Autoregressive Models for Controllable Bidirectional Satellite-Map Translation [50.433911327489554]
  We introduce EarthMapper, a novel framework for controllable satellite-map translation.<n>We also contribute CNSatMap, a large-scale dataset comprising 302,132 precisely aligned satellite-map pairs across 38 Chinese cities.<n> experiments on CNSatMap and the New York dataset demonstrate EarthMapper's superior performance.
  arXiv  Detail & Related papers  (2025-04-28T02:41:12Z)
• GAIR: Improving Multimodal Geo-Foundation Model with Geo-Aligned Implicit Representations [5.439918212297177]
  Key challenge in multimodal GeoFM development is to explicitly model geospatial relationships across modalities.<n>We propose GAIR, a novel multimodal GeoFM architecture integrating overhead RS data, street view imagery, and their geolocation metadata.<n>We evaluate GAIR across 10 geospatial tasks spanning RS image-based, SV image-based, and location embedding-based benchmarks.
  arXiv  Detail & Related papers  (2025-03-20T19:59:39Z)
• 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. GeoComp, a large-scale dataset; GeoCoT, a novel reasoning method; and GeoEval, an evaluation metric. We demonstrate that GeoCoT significantly boosts geolocation accuracy by up to 25% while enhancing interpretability.
  arXiv  Detail & Related papers  (2025-02-19T14:21:25Z)
• GaGA: Towards Interactive Global Geolocation Assistant [20.342366228855735]
  GaGA is an interactive global geolocation assistant built upon the flourishing large vision-language models (LVLMs)<n>It uncovers geographical clues within images and combines them with the extensive world knowledge embedded in LVLMs to determine the geolocations.<n>GaGA achieves state-of-the-art performance on the GWS15k dataset, improving accuracy by 4.57% at the country level and 2.92% at the city level.
  arXiv  Detail & Related papers  (2024-12-12T03:39:44Z)
• EarthMatch: Iterative Coregistration for Fine-grained Localization of Astronaut Photography [18.978718859476267]
  We present EarthMatch, an iterative homography estimation method that produces fine-grained localization of astronaut photographs. We prove our method's efficacy on this dataset and offer a new, fair method for image matcher comparison. Our method will enable fast and accurate localization of the 4.5 million and growing collection of astronaut photography of Earth.
  arXiv  Detail & Related papers  (2024-05-08T20:46:36Z)
• 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)
• Cross-View Visual Geo-Localization for Outdoor Augmented Reality [11.214903134756888]
  We address the problem of geo-pose estimation by cross-view matching of query ground images to a geo-referenced aerial satellite image database. We propose a new transformer neural network-based model and a modified triplet ranking loss for joint location and orientation estimation. Experiments on several benchmark cross-view geo-localization datasets show that our model achieves state-of-the-art performance.
  arXiv  Detail & Related papers  (2023-03-28T01:58:03Z)
• 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)
• Accurate 3-DoF Camera Geo-Localization via Ground-to-Satellite Image Matching [102.39635336450262]
  We address the problem of ground-to-satellite image geo-localization by matching a query image captured at the ground level against a large-scale database with geotagged satellite images. Our new method is able to achieve the fine-grained location of a query image, up to pixel size precision of the satellite image.
  arXiv  Detail & Related papers  (2022-03-26T20:10:38Z)
• Continuous Self-Localization on Aerial Images Using Visual and Lidar Sensors [25.87104194833264]
  We propose a novel method for geo-tracking in outdoor environments by registering a vehicle's sensor information with aerial imagery of an unseen target region. We train a model in a metric learning setting to extract visual features from ground and aerial images. Our method is the first to utilize on-board cameras in an end-to-end differentiable model for metric self-localization on unseen orthophotos.
  arXiv  Detail & Related papers  (2022-03-07T12:25:44Z)

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.