GeoDE: a Geographically Diverse Evaluation Dataset for Object Recognition

• URL: http://arxiv.org/abs/2301.02560v4
• Date: Thu, 11 Sep 2025 19:49:28 GMT
• Title: GeoDE: a Geographically Diverse Evaluation Dataset for Object Recognition
• Abstract summary: GeoDE is a geographically diverse dataset with 61,940 images from 40 classes and 6 world regions.<n>We release the full dataset and code at https://geodiverse-data-collection.cs.princeton.edu.
• Score: 31.345584733507547
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Current dataset collection methods typically scrape large amounts of data from the web. While this technique is extremely scalable, data collected in this way tends to reinforce stereotypical biases, can contain personally identifiable information, and typically originates from Europe and North America. In this work, we rethink the dataset collection paradigm and introduce GeoDE, a geographically diverse dataset with 61,940 images from 40 classes and 6 world regions, with no personally identifiable information, collected by soliciting images from people around the world. We analyse GeoDE to understand differences in images collected in this manner compared to web-scraping. We demonstrate its use as both an evaluation and training dataset, allowing us to highlight and begin to mitigate the shortcomings in current models, despite GeoDE's relatively small size. We release the full dataset and code at https://geodiverse-data-collection.cs.princeton.edu

Related papers

• Scaling Image Geo-Localization to Continent Level [48.7766435870634]
  This paper introduces a hybrid approach that achieves fine-grained geo-localization across a large geographic expanse the size of a continent.<n>We leverage a proxy classification task during training to learn rich feature representations that implicitly encode precise location information.<n>Our evaluation demonstrates that our approach can localize within 200m more than 68% of queries of a dataset covering a large part of Europe.
  arXiv  Detail & Related papers  (2025-10-30T17:59:35Z)
• GeoArena: An Open Platform for Benchmarking Large Vision-language Models on WorldWide Image Geolocalization [21.941170274245223]
  Image geolocalization aims to predict the geographic location of images captured anywhere on Earth.<n>Current evaluation methodologies suffer from two major limitations.<n>We propose GeoArena, a first open platform for evaluating LVLMs on worldwide image geolocalization tasks.
  arXiv  Detail & Related papers  (2025-09-04T15:52:04Z)
• Scalable Geospatial Data Generation Using AlphaEarth Foundations Model [0.1775251182905249]
  We propose and evaluate a methodology which leverages Google DeepMind's AlphaEarth Foundations (AEF) to extend geospatial labeled datasets beyond their initial geographic regions.<n>We show that even basic models like random forests or logistic regression can be used to accomplish this task.<n>We investigate a case study of extending LANDFIRE's Existing Vegetation Type (EVT) dataset beyond the USA into Canada at two levels of granularity: EvtPhys (13 classes) and EvtGp (80 classes)
  arXiv  Detail & Related papers  (2025-08-15T17:09:48Z)
• 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)
• 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. 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. 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)
• Classification Drives Geographic Bias in Street Scene Segmentation [20.14340857253721]
  We investigated geo-biases in real-world driving datasets on a more complex task: instance segmentation. We found that geo-biases came from classification errors rather than localization errors. Our findings show that in region-specific models, geo-biases can be significantly mitigated by using coarser classes.
  arXiv  Detail & Related papers  (2024-12-15T05:33:10Z)
• Diffusion Models as Data Mining Tools [87.77999285241219]
  This paper demonstrates how to use generative models trained for image synthesis as tools for visual data mining. We show that after finetuning conditional diffusion models to synthesize images from a specific dataset, we can use these models to define a typicality measure. This measure assesses how typical visual elements are for different data labels, such as geographic location, time stamps, semantic labels, or even the presence of a disease.
  arXiv  Detail & Related papers  (2024-07-20T17:14:31Z)
• CurriculumLoc: Enhancing Cross-Domain Geolocalization through Multi-Stage Refinement [11.108860387261508]
  Visual geolocalization is a cost-effective and scalable task that involves matching one or more query images taken at some unknown location, to a set of geo-tagged reference images. We develop CurriculumLoc, a novel keypoint detection and description with global semantic awareness and a local geometric verification. We achieve new high recall@1 scores of 62.6% and 94.5% on ALTO, with two different distances metrics, respectively.
  arXiv  Detail & Related papers  (2023-11-20T08:40:01Z)
• 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)
• infoVerse: A Universal Framework for Dataset Characterization with Multidimensional Meta-information [68.76707843019886]
  infoVerse is a universal framework for dataset characterization. infoVerse captures multidimensional characteristics of datasets by incorporating various model-driven meta-information. In three real-world applications (data pruning, active learning, and data annotation), the samples chosen on infoVerse space consistently outperform strong baselines.
  arXiv  Detail & Related papers  (2023-05-30T18:12:48Z)
• CSP: Self-Supervised Contrastive Spatial Pre-Training for Geospatial-Visual Representations [90.50864830038202]
  We present Contrastive Spatial Pre-Training (CSP), a self-supervised learning framework for geo-tagged images. We use a dual-encoder to separately encode the images and their corresponding geo-locations, and use contrastive objectives to learn effective location representations from images. CSP significantly boosts the model performance with 10-34% relative improvement with various labeled training data sampling ratios.
  arXiv  Detail & Related papers  (2023-05-01T23:11:18Z)
• GeoNet: Benchmarking Unsupervised Adaptation across Geographies [71.23141626803287]
  We study the problem of geographic robustness and make three main contributions. First, we introduce a large-scale dataset GeoNet for geographic adaptation. Second, we hypothesize that the major source of domain shifts arise from significant variations in scene context. Third, we conduct an extensive evaluation of several state-of-the-art unsupervised domain adaptation algorithms and architectures.
  arXiv  Detail & Related papers  (2023-03-27T17:59:34Z)
• Where We Are and What We're Looking At: Query Based Worldwide Image Geo-localization Using Hierarchies and Scenes [53.53712888703834]
  We introduce an end-to-end transformer-based architecture that exploits the relationship between different geographic levels. We achieve state of the art street level accuracy on 4 standard geo-localization datasets.
  arXiv  Detail & Related papers  (2023-03-07T21:47:58Z)
• G^3: Geolocation via Guidebook Grounding [92.46774241823562]
  We study explicit knowledge from human-written guidebooks that describe the salient and class-discriminative visual features humans use for geolocation. We propose the task of Geolocation via Guidebook Grounding that uses a dataset of StreetView images from a diverse set of locations. Our approach substantially outperforms a state-of-the-art image-only geolocation method, with an improvement of over 5% in Top-1 accuracy.
  arXiv  Detail & Related papers  (2022-11-28T16:34:40Z)
• Rethinking Visual Geo-localization for Large-Scale Applications [18.09618985653891]
  We build San Francisco eXtra Large, a new dataset covering a whole city and providing a wide range of challenging cases. We design a new highly scalable training technique, called CosPlace, which casts the training as a classification problem. We achieve state-of-the-art performance on a wide range of datasets and find that CosPlace is robust to heavy domain changes.
  arXiv  Detail & Related papers  (2022-04-05T15:33:45Z)
• Dataset Cartography: Mapping and Diagnosing Datasets with Training Dynamics [118.75207687144817]
  We introduce Data Maps, a model-based tool to characterize and diagnose datasets. We leverage a largely ignored source of information: the behavior of the model on individual instances during training. Our results indicate that a shift in focus from quantity to quality of data could lead to robust models and improved out-of-distribution generalization.
  arXiv  Detail & Related papers  (2020-09-22T20:19:41Z)
• Robust Image Retrieval-based Visual Localization using Kapture [10.249293519246478]
  We present a versatile pipeline for visual localization that facilitates the use of different local and global features. We evaluate our methods on eight public datasets where they rank top on all and first on many of them. To foster future research, we release code, models, and all datasets used in this paper in the kapture format open source under a permissive BSD license.
  arXiv  Detail & Related papers  (2020-07-27T21:10:35Z)
• Google Landmarks Dataset v2 -- A Large-Scale Benchmark for Instance-Level Recognition and Retrieval [9.922132565411664]
  We introduce the Google Landmarks dataset v2 (GLDv2), a new benchmark for large-scale, fine-grained instance recognition and image retrieval. GLDv2 is the largest such dataset to date by a large margin, including over 5M images and 200k distinct instance labels. The dataset is sourced from Wikimedia Commons, the world's largest crowdsourced collection of landmark photos.
  arXiv  Detail & Related papers  (2020-04-03T22:52:17Z)

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.