Galileo: Learning Global & Local Features of Many Remote Sensing Modalities

• URL: http://arxiv.org/abs/2502.09356v3
• Date: Wed, 04 Jun 2025 14:07:47 GMT
• Title: Galileo: Learning Global & Local Features of Many Remote Sensing Modalities
• Authors: Gabriel Tseng, Anthony Fuller, Marlena Reil, Henry Herzog, Patrick Beukema, Favyen Bastani, James R. Green, Evan Shelhamer, Hannah Kerner, David Rolnick,
• Abstract summary: We present a novel self-supervised learning algorithm that extracts multi-scale features across a flexible set of input modalities through masked modeling.<n>Our Galileo is a single generalist model that outperforms SoTA specialist models for satellite images and pixel time series across eleven benchmarks and multiple tasks.
• Score: 34.71460539414284
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We introduce a highly multimodal transformer to represent many remote sensing modalities - multispectral optical, synthetic aperture radar, elevation, weather, pseudo-labels, and more - across space and time. These inputs are useful for diverse remote sensing tasks, such as crop mapping and flood detection. However, learning shared representations of remote sensing data is challenging, given the diversity of relevant data modalities, and because objects of interest vary massively in scale, from small boats (1-2 pixels and fast) to glaciers (thousands of pixels and slow). We present a novel self-supervised learning algorithm that extracts multi-scale features across a flexible set of input modalities through masked modeling. Our dual global and local contrastive losses differ in their targets (deep representations vs. shallow input projections) and masking strategies (structured vs. not). Our Galileo is a single generalist model that outperforms SoTA specialist models for satellite images and pixel time series across eleven benchmarks and multiple tasks.

Related papers

• A Survey on Remote Sensing Foundation Models: From Vision to Multimodality [35.532200523631765]
  Vision and multimodal foundation models for remote sensing have significantly improved the capabilities of intelligent geospatial data interpretation. The diversity in data types, the need for large-scale annotated datasets, and the complexity of multimodal fusion techniques pose significant obstacles to the effective deployment of these models. This paper provides a review of the state-of-the-art in vision and multimodal foundation models for remote sensing, focusing on their architecture, training methods, datasets and application scenarios.
  arXiv  Detail & Related papers  (2025-03-28T01:57:35Z)
• SM3Det: A Unified Model for Multi-Modal Remote Sensing Object Detection [73.49799596304418]
  This paper introduces a new task called Multi-Modal datasets and Multi-Task Object Detection (M2Det) for remote sensing.<n>It is designed to accurately detect horizontal or oriented objects from any sensor modality.<n>This task poses challenges due to 1) the trade-offs involved in managing multi-modal modelling and 2) the complexities of multi-task optimization.
  arXiv  Detail & Related papers  (2024-12-30T02:47:51Z)
• RemoteDet-Mamba: A Hybrid Mamba-CNN Network for Multi-modal Object Detection in Remote Sensing Images [13.98477009749389]
  We propose a multimodal remote sensing network that employs a quad-directional selective scanning fusion strategy called RemoteDet-Mamba. RemoteDet-Mamba simultaneously facilitates the learning of single-modal local features and the integration of patch-level global features. Experimental results on the DroneVehicle dataset demonstrate the effectiveness of RemoteDet-Mamba.
  arXiv  Detail & Related papers  (2024-10-17T13:20:20Z)
• Deep Multimodal Fusion for Semantic Segmentation of Remote Sensing Earth Observation Data [0.08192907805418582]
  This paper proposes a late fusion deep learning model (LF-DLM) for semantic segmentation. One branch integrates detailed textures from aerial imagery captured by UNetFormer with a Multi-Axis Vision Transformer (ViT) backbone. The other branch captures complex-temporal dynamics from the Sentinel-2 satellite imageMax time series using a U-ViNet with Temporal Attention (U-TAE)
  arXiv  Detail & Related papers  (2024-10-01T07:50:37Z)
• Deep Generative Models in Robotics: A Survey on Learning from Multimodal Demonstrations [52.11801730860999]
  In recent years, the robot learning community has shown increasing interest in using deep generative models to capture the complexity of large datasets. We present the different types of models that the community has explored, such as energy-based models, diffusion models, action value maps, or generative adversarial networks. We also present the different types of applications in which deep generative models have been used, from grasp generation to trajectory generation or cost learning.
  arXiv  Detail & Related papers  (2024-08-08T11:34:31Z)
• Multi-view Aggregation Network for Dichotomous Image Segmentation [76.75904424539543]
  Dichotomous Image (DIS) has recently emerged towards high-precision object segmentation from high-resolution natural images. Existing methods rely on tedious multiple encoder-decoder streams and stages to gradually complete the global localization and local refinement. Inspired by it, we model DIS as a multi-view object perception problem and provide a parsimonious multi-view aggregation network (MVANet) Experiments on the popular DIS-5K dataset show that our MVANet significantly outperforms state-of-the-art methods in both accuracy and speed.
  arXiv  Detail & Related papers  (2024-04-11T03:00:00Z)
• Rethinking Transformers Pre-training for Multi-Spectral Satellite Imagery [78.43828998065071]
  Recent advances in unsupervised learning have demonstrated the ability of large vision models to achieve promising results on downstream tasks. Such pre-training techniques have also been explored recently in the remote sensing domain due to the availability of large amount of unlabelled data. In this paper, we re-visit transformers pre-training and leverage multi-scale information that is effectively utilized with multiple modalities.
  arXiv  Detail & Related papers  (2024-03-08T16:18:04Z)
• Physics Sensor Based Deep Learning Fall Detection System [0.9128828609564524]
  We build a complete system named TSFallDetect including data receiving device based on embedded sensor. We exploit the sequential deep-learning methods to address this falling motion prediction problem based on data collected by inertial and film pressure sensors.
  arXiv  Detail & Related papers  (2024-02-29T07:50:06Z)
• Bi-directional Adapter for Multi-modal Tracking [67.01179868400229]
  We propose a novel multi-modal visual prompt tracking model based on a universal bi-directional adapter. We develop a simple but effective light feature adapter to transfer modality-specific information from one modality to another. Our model achieves superior tracking performance in comparison with both the full fine-tuning methods and the prompt learning-based methods.
  arXiv  Detail & Related papers  (2023-12-17T05:27:31Z)
• Physical-Layer Semantic-Aware Network for Zero-Shot Wireless Sensing [74.12670841657038]
  Device-free wireless sensing has recently attracted significant interest due to its potential to support a wide range of immersive human-machine interactive applications. Data heterogeneity in wireless signals and data privacy regulation of distributed sensing have been considered as the major challenges that hinder the wide applications of wireless sensing in large area networking systems. We propose a novel zero-shot wireless sensing solution that allows models constructed in one or a limited number of locations to be directly transferred to other locations without any labeled data.
  arXiv  Detail & Related papers  (2023-12-08T13:50:30Z)
• Learn to Unlearn for Deep Neural Networks: Minimizing Unlearning Interference with Gradient Projection [56.292071534857946]
  Recent data-privacy laws have sparked interest in machine unlearning. Challenge is to discard information about the forget'' data without altering knowledge about remaining dataset. We adopt a projected-gradient based learning method, named as Projected-Gradient Unlearning (PGU) We provide empirically evidence to demonstrate that our unlearning method can produce models that behave similar to models retrained from scratch across various metrics even when the training dataset is no longer accessible.
  arXiv  Detail & Related papers  (2023-12-07T07:17:24Z)
• General-Purpose Multimodal Transformer meets Remote Sensing Semantic Segmentation [35.100738362291416]
  Multimodal AI seeks to exploit complementary data sources, particularly for complex tasks like semantic segmentation. Recent trends in general-purpose multimodal networks have shown great potential to achieve state-of-the-art performance. We propose a UNet-inspired module that employs 3D convolution to encode vital local information and learn cross-modal features simultaneously.
  arXiv  Detail & Related papers  (2023-07-07T04:58:34Z)
• UnLoc: A Universal Localization Method for Autonomous Vehicles using LiDAR, Radar and/or Camera Input [51.150605800173366]
  UnLoc is a novel unified neural modeling approach for localization with multi-sensor input in all weather conditions. Our method is extensively evaluated on Oxford Radar RobotCar, ApolloSouthBay and Perth-WA datasets.
  arXiv  Detail & Related papers  (2023-07-03T04:10:55Z)
• Lightweight, Pre-trained Transformers for Remote Sensing Timeseries [33.44703824007848]
  Presto is a model pre-trained on remote sensing pixel-timeseries data. It excels at a wide variety of globally distributed remote sensing tasks and performs competitively with much larger models.
  arXiv  Detail & Related papers  (2023-04-27T09:52:35Z)
• Extending global-local view alignment for self-supervised learning with remote sensing imagery [1.5192294544599656]
  Self-supervised models acquire general feature representations by formulating a pretext task that generates pseudo-labels for massive unlabeled data. Inspired by DINO, we formulate two pretext tasks for self-supervised learning on remote sensing imagery (SSLRS) We extend DINO and propose DINO-MC which uses local views of various sized crops instead of a single fixed size.
  arXiv  Detail & Related papers  (2023-03-12T14:24:10Z)
• Semantic Segmentation of Vegetation in Remote Sensing Imagery Using Deep Learning [77.34726150561087]
  We propose an approach for creating a multi-modal and large-temporal dataset comprised of publicly available Remote Sensing data. We use Convolutional Neural Networks (CNN) models that are capable of separating different classes of vegetation.
  arXiv  Detail & Related papers  (2022-09-28T18:51:59Z)
• MetaGraspNet: A Large-Scale Benchmark Dataset for Scene-Aware Ambidextrous Bin Picking via Physics-based Metaverse Synthesis [72.85526892440251]
  We introduce MetaGraspNet, a large-scale photo-realistic bin picking dataset constructed via physics-based metaverse synthesis. The proposed dataset contains 217k RGBD images across 82 different article types, with full annotations for object detection, amodal perception, keypoint detection, manipulation order and ambidextrous grasp labels for a parallel-jaw and vacuum gripper. We also provide a real dataset consisting of over 2.3k fully annotated high-quality RGBD images, divided into 5 levels of difficulties and an unseen object set to evaluate different object and layout properties.
  arXiv  Detail & Related papers  (2022-08-08T08:15:34Z)
• DDPM-CD: Denoising Diffusion Probabilistic Models as Feature Extractors for Change Detection [31.125812018296127]
  We introduce a novel approach for change detection by pre-training a Deno Diffusionising Probabilistic Model (DDPM) DDPM learns the training data distribution by gradually converting training images into a Gaussian distribution using a Markov chain. During inference (i.e., sampling), they can generate a diverse set of samples closer to the training distribution. Experiments conducted on the LEVIR-CD, WHU-CD, DSIFN-CD, and CDD datasets demonstrate that the proposed DDPM-CD method significantly outperforms the existing change detection methods in terms of F1 score, I
  arXiv  Detail & Related papers  (2022-06-23T17:58:29Z)
• Self-supervised Audiovisual Representation Learning for Remote Sensing Data [96.23611272637943]
  We propose a self-supervised approach for pre-training deep neural networks in remote sensing. By exploiting the correspondence between geo-tagged audio recordings and remote sensing, this is done in a completely label-free manner. We show that our approach outperforms existing pre-training strategies for remote sensing imagery.
  arXiv  Detail & Related papers  (2021-08-02T07:50:50Z)
• Transformer-Based Behavioral Representation Learning Enables Transfer Learning for Mobile Sensing in Small Datasets [4.276883061502341]
  We provide a neural architecture framework for mobile sensing data that can learn generalizable feature representations from time series. This architecture combines benefits from CNN and Trans-former architectures to enable better prediction performance.
  arXiv  Detail & Related papers  (2021-07-09T22:26:50Z)
• Inertial Sensor Data To Image Encoding For Human Action Recognition [0.0]
  Convolutional Neural Networks (CNNs) are successful deep learning models in the field of computer vision. In this paper, we use 4 types of spatial domain methods for transforming inertial sensor data to activity images. For creating a multimodal fusion framework, we made each type of activity images multimodal by convolving with two spatial domain filters.
  arXiv  Detail & Related papers  (2021-05-28T01:22:52Z)
• Model-Based Visual Planning with Self-Supervised Functional Distances [104.83979811803466]
  We present a self-supervised method for model-based visual goal reaching. Our approach learns entirely using offline, unlabeled data. We find that this approach substantially outperforms both model-free and model-based prior methods.
  arXiv  Detail & Related papers  (2020-12-30T23:59:09Z)
• Invariant Feature Learning for Sensor-based Human Activity Recognition [11.334750079923428]
  We present an invariant feature learning framework (IFLF) that extracts common information shared across subjects and devices. Experiments demonstrated that IFLF is effective in handling both subject and device diversion across popular open datasets and an in-house dataset.
  arXiv  Detail & Related papers  (2020-12-14T21:56:17Z)

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.