mlsanityData to 14 Oct 2026

Tile2Vec: Unsupervised representation learning for spatially distributed data

arXiv 1805.02855 ↗First posted 8 May 2018cs.CV cs.LG stat.MLcited by 110

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Abstract (arXiv)

Geospatial analysis lacks methods like the word vector representations and pre-trained networks that significantly boost performance across a wide range of natural language and computer vision tasks. To fill this gap, we introduce Tile2Vec, an unsupervised representation learning algorithm that extends the distributional hypothesis from natural language -- words appearing in similar contexts tend to have similar meanings -- to spatially distributed data. We demonstrate empirically that Tile2Vec learns semantically meaningful representations on three datasets. Our learned representations significantly improve performance in downstream classification tasks and, similar to word vectors, visual analogies can be obtained via simple arithmetic in the latent space.

Key facts. Cited by 110 papers in the map (data through 14 Oct 2026). First posted on arXiv 8 May 2018.

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Citing papers per year: 2021: 24; 2022: 22; 2023: 22; 2024: 14; 2025: 17; 2026: 11.

110 papers.

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  1. 2023RemoteCLIP: A Vision Language Foundation Model for Remote Sensingcited by 363
  2. 2021Seasonal Contrast: Unsupervised Pre-Training from Uncurated Remote Sensing Datacited by 215
  3. 2023CROMA: Remote Sensing Representations with Contrastive Radar-Optical Masked Autoencoderscited by 169
  4. 2023SatCLIP: Global, General-Purpose Location Embeddings with Satellite Imagerycited by 151
  5. 2022Self-supervised Learning in Remote Sensing: A Reviewcited by 137
  6. 2023SkyScript: A Large and Semantically Diverse Vision-Language Dataset for Remote Sensingcited by 125
  7. 2023GEO-Bench: Toward Foundation Models for Earth Monitoringcited by 124
  8. 2023Towards Geospatial Foundation Models via Continual Pretrainingcited by 109
  9. 2024Neural Plasticity-Inspired Multimodal Foundation Model for Earth Observationcited by 104
  10. 2023Lightweight, Pre-trained Transformers for Remote Sensing Timeseriescited by 103
  11. 2023UrbanCLIP: Learning Text-enhanced Urban Region Profiling with Contrastive Language-Image Pretraining from the Webcited by 78
  12. 2024Mission Critical -- Satellite Data is a Distinct Modality in Machine Learningcited by 77
38 more
  1. 2021TorchGeo: Deep Learning With Geospatial Datacited by 73
  2. 2023CSP: Self-Supervised Contrastive Spatial Pre-Training for Geospatial-Visual Representationscited by 69
  3. 2021Self-Supervised Learning of Remote Sensing Scene Representations Using Contrastive Multiview Codingcited by 58
  4. 2021SustainBench: Benchmarks for Monitoring the Sustainable Development Goals with Machine Learningcited by 54
  5. 2023SSL4EO-L: Datasets and Foundation Models for Landsat Imagerycited by 49
  6. 2023Feature Guided Masked Autoencoder for Self-supervised Learning in Remote Sensingcited by 48
  7. 2025Galileo: Learning Global & Local Features of Many Remote Sensing Modalitiescited by 43
  8. 2022Self-supervised Vision Transformers for Joint SAR-optical Representation Learningcited by 40
  9. 2022Open High-Resolution Satellite Imagery: The WorldStrat Dataset -- With Application to Super-Resolutioncited by 40
  10. 2024On the Foundations of Earth and Climate Foundation Modelscited by 33
  11. 2023USat: A Unified Self-Supervised Encoder for Multi-Sensor Satellite Imagerycited by 28
  12. 2024Multi-Label Guided Soft Contrastive Learning for Efficient Earth Observation Pretrainingcited by 28
  13. 2021Self-supervised Audiovisual Representation Learning for Remote Sensing Datacited by 24
  14. 2022Self-supervised remote sensing feature learning: Learning Paradigms, Challenges, and Future Workscited by 24
  15. 2022Region Embedding with Intra and Inter-View Contrastive Learningcited by 24
  16. 2025OlmoEarth: Stable Latent Image Modeling for Multimodal Earth Observationcited by 24
  17. 2021Hex2vec -- Context-Aware Embedding H3 Hexagons with OpenStreetMap Tagscited by 23
  18. 2023Knowledge-infused Contrastive Learning for Urban Imagery-based Socioeconomic Predictioncited by 23
  19. 2021Toward Foundation Models for Earth Monitoring: Proposal for a Climate Change Benchmarkcited by 22
  20. 2022DDPM-CD: Denoising Diffusion Probabilistic Models as Feature Extractors for Change Detectioncited by 19
  21. 2024MuseCL: Predicting Urban Socioeconomic Indicators via Multi-Semantic Contrastive Learningcited by 17
  22. 2021Focus on the Positives: Self-Supervised Learning for Biodiversity Monitoringcited by 16
  23. 2022Semantic-aware Dense Representation Learning for Remote Sensing Image Change Detectioncited by 16
  24. 2024Generic Knowledge Boosted Pre-training For Remote Sensing Imagescited by 16
  25. 2024Geolocation Representation from Large Language Models are Generic Enhancers for Spatio-Temporal Learningcited by 15
  26. 2021Representation Learning for Remote Sensing: An Unsupervised Sensor Fusion Approachcited by 14
  27. 2021Contrastive Multiview Coding with Electro-optics for SAR Semantic Segmentationcited by 14
  28. 2023Urban Region Representation Learning with Attentive Fusioncited by 12
  29. 2022A Contextual Master-Slave Framework on Urban Region Graph for Urban Village Detectioncited by 10
  30. 2024Interpreting Neurons in Deep Vision Networks with Language Modelscited by 10
  31. 2024Contrastive ground-level image and remote sensing pre-training improves representation learning for natural world imagerycited by 10
  32. 2021Learning Neighborhood Representation from Multi-Modal Multi-Graph: Image, Text, Mobility Graph and Beyondcited by 9
  33. 2024Self-Supervised Representation Learning for Geospatial Objects: A Surveycited by 9
  34. 2022AtmoDist: Self-supervised Representation Learning for Atmospheric Dynamicscited by 8
  35. 2022Resolving label uncertainty with implicit posterior modelscited by 8
  36. 2021Evaluating Self and Semi-Supervised Methods for Remote Sensing Segmentation Taskscited by 7
  37. 2021Clustering augmented Self-Supervised Learning: Anapplication to Land Cover Mappingcited by 6
  38. 2022Transfer Learning with Pretrained Remote Sensing Transformerscited by 6

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