Related papers: GEO-Bench: Toward Foundation Models for Earth Monitoring

GEO-Bench: Toward Foundation Models for Earth Monitoring

URL: http://arxiv.org/abs/2306.03831v2
Date: Sat, 23 Dec 2023 17:17:05 GMT
Title: GEO-Bench: Toward Foundation Models for Earth Monitoring
Authors: Alexandre Lacoste, Nils Lehmann, Pau Rodriguez, Evan David Sherwin, Hannah Kerner, Bj\"orn L\"utjens, Jeremy Andrew Irvin, David Dao, Hamed Alemohammad, Alexandre Drouin, Mehmet Gunturkun, Gabriel Huang, David Vazquez, Dava Newman, Yoshua Bengio, Stefano Ermon, Xiao Xiang Zhu
Abstract summary: We propose a benchmark comprised of six classification and six segmentation tasks. This benchmark will be a driver of progress across a variety of Earth monitoring tasks.
Score: 139.77907168809085
License: http://creativecommons.org/licenses/by/4.0/
Abstract: Recent progress in self-supervision has shown that pre-training large neural networks on vast amounts of unsupervised data can lead to substantial increases in generalization to downstream tasks. Such models, recently coined foundation models, have been transformational to the field of natural language processing. Variants have also been proposed for image data, but their applicability to remote sensing tasks is limited. To stimulate the development of foundation models for Earth monitoring, we propose a benchmark comprised of six classification and six segmentation tasks, which were carefully curated and adapted to be both relevant to the field and well-suited for model evaluation. We accompany this benchmark with a robust methodology for evaluating models and reporting aggregated results to enable a reliable assessment of progress. Finally, we report results for 20 baselines to gain information about the performance of existing models. We believe that this benchmark will be a driver of progress across a variety of Earth monitoring tasks.

Related papers

AI Foundation Models in Remote Sensing: A Survey [6.036426846159163]
This paper provides a comprehensive survey of foundation models in the remote sensing domain. We categorize these models based on their applications in computer vision and domain-specific tasks. We highlight emerging trends and the significant advancements achieved by these foundation models.
arXiv Detail & Related papers (2024-08-06T22:39:34Z)
GenBench: A Benchmarking Suite for Systematic Evaluation of Genomic Foundation Models [56.63218531256961]
We introduce GenBench, a benchmarking suite specifically tailored for evaluating the efficacy of Genomic Foundation Models. GenBench offers a modular and expandable framework that encapsulates a variety of state-of-the-art methodologies. We provide a nuanced analysis of the interplay between model architecture and dataset characteristics on task-specific performance.
arXiv Detail & Related papers (2024-06-01T08:01:05Z)
Towards A Foundation Model For Trajectory Intelligence [0.0]
We present the results of training a large trajectory model using real-world user check-in data. Our approach follows a pre-train and fine-tune paradigm, where a base model is pre-trained via masked trajectory modeling. Our empirical analysis utilizes a comprehensive dataset of over 2 billion check-ins generated by more than 6 million users.
arXiv Detail & Related papers (2023-11-30T00:34:09Z)
Foundation Models for Generalist Geospatial Artificial Intelligence [3.7002058945990415]
This paper introduces a first-of-a-kind framework for the efficient pre-training and fine-tuning of foundational models on extensive data. We have utilized this framework to create Prithvi, a transformer-based foundational model pre-trained on more than 1TB of multispectral satellite imagery.
arXiv Detail & Related papers (2023-10-28T10:19:55Z)
A Billion-scale Foundation Model for Remote Sensing Images [5.065947993017157]
Three key factors in pretraining foundation models are the pretraining method, the size of the pretraining dataset, and the number of model parameters. This paper examines the effect of increasing the number of model parameters on the performance of foundation models in downstream tasks. To the best of our knowledge, this is the first billion-scale foundation model in the remote sensing field.
arXiv Detail & Related papers (2023-04-11T13:33:45Z)
Improving Visual Grounding by Encouraging Consistent Gradient-based Explanations [58.442103936918805]
We show that Attention Mask Consistency produces superior visual grounding results than previous methods. AMC is effective, easy to implement, and is general as it can be adopted by any vision-language model.
arXiv Detail & Related papers (2022-06-30T17:55:12Z)
Occlusion-Aware Self-Supervised Monocular 6D Object Pose Estimation [88.8963330073454]
We propose a novel monocular 6D pose estimation approach by means of self-supervised learning. We leverage current trends in noisy student training and differentiable rendering to further self-supervise the model. Our proposed self-supervision outperforms all other methods relying on synthetic data.
arXiv Detail & Related papers (2022-03-19T15:12:06Z)
Toward Foundation Models for Earth Monitoring: Proposal for a Climate Change Benchmark [95.19070157520633]
Recent progress in self-supervision shows that pre-training large neural networks on vast amounts of unsupervised data can lead to impressive increases in generalisation for downstream tasks. Such models, recently coined as foundation models, have been transformational to the field of natural language processing. We propose to develop a new benchmark comprised of a variety of downstream tasks related to climate change.
arXiv Detail & Related papers (2021-12-01T15:38:19Z)
Self6D: Self-Supervised Monocular 6D Object Pose Estimation [114.18496727590481]
We propose the idea of monocular 6D pose estimation by means of self-supervised learning. We leverage recent advances in neural rendering to further self-supervise the model on unannotated real RGB-D data.
arXiv Detail & Related papers (2020-04-14T13:16:36Z)

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