mlsanityData to 14 Oct 2026

TimeSenCLIP: A Time Series Vision-Language Model for Remote Sensing

arXiv 2508.11919 ↗First posted 16 Aug 2025cs.CVcited by 1

On the map: Multimodal LLMs / vision-language models → Embodied and 3D vision-language models

Abstract (arXiv)

Vision-language models (VLMs) have shown significant promise in remote sensing applications, particularly for land-use and land-cover (LULC) mapping via zero-shot classification and retrieval. However, current approaches face several key challenges, such as the dependence on caption-based supervision, which is often not available or very limited in terms of the covered semantics, and the fact of being adapted from generic VLM architectures that are suitable for very high resolution images. Consequently, these models tend to prioritize spatial context over spectral and temporal information, limiting their effectiveness for medium-resolution remote sensing imagery. In this work, we present TimeSenCLIP, a lightweight VLM for remote sensing time series, using a cross-view temporal contrastive framework to align multispectral Sentinel-2 time series with geo-tagged ground-level imagery, without requiring textual annotations. Unlike prior VLMs, TimeSenCLIP emphasizes temporal and spectral signals over spatial context, investigating whether single-pixel time series contain sufficient information for solving a variety of tasks.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Placed in Embodied and 3D vision-language models, one of 16 directions in Multimodal LLMs / vision-language models. Builds on 22 of its 45 references that are in the map. First posted on arXiv 16 Aug 2025.

Builds on

22 of 45 references.

Closest ancestors · 6

Same direction, newest first

  1. 2025Beyond the Visible: Multispectral Vision-Language Learning for Earth Observation20 Mar
  2. 2024SenCLIP: Enhancing zero-shot land-use mapping for Sentinel-2 with ground-level prompting11 Dec
  3. 2024GeoLLaVA: Efficient Fine-Tuned Vision-Language Models for Temporal Change Detection in Remote Sensing25 Oct
  4. 2023SkyScript: A Large and Semantically Diverse Vision-Language Dataset for Remote Sensing20 Dec
  5. 2023Remote Sensing Vision-Language Foundation Models without Annotations via Ground Remote Alignment12 Dec
  6. 2023RemoteCLIP: A Vision Language Foundation Model for Remote Sensing19 Jun

Other references · 16

  1. 2021Learning Transferable Visual Models From Natural Language Supervisioncited by 33,744
  2. 2018Representation Learning with Contrastive Predictive Codingcited by 7,777
  3. 2019Momentum Contrast for Unsupervised Visual Representation Learningcited by 7,513
  4. 2022Visual Classification via Description from Large Language Modelscited by 336
  5. 2023GeoCLIP: Clip-Inspired Alignment between Locations and Images for Effective Worldwide Geo-localizationcited by 187
  6. 2023SatCLIP: Global, General-Purpose Location Embeddings with Satellite Imagerycited by 151
  7. 2017Deep learning in remote sensing: a reviewcited by 128
  8. 2023Waffling around for Performance: Visual Classification with Random Words and Broad Conceptscited by 101
  9. 2018Temporal Convolutional Neural Network for the Classification of Satellite Image Time Seriescited by 76
  10. 2018Multi-Temporal Land Cover Classification with Sequential Recurrent Encoderscited by 61
  11. 2020Lightweight Temporal Self-Attention for Classifying Satellite Image Time Seriescited by 55
  12. 2025Towards a Unified Copernicus Foundation Model for Earth Visioncited by 53
4 more
  1. 2023Improving Position Encoding of Transformers for Multivariate Time Series Classificationcited by 45
  2. 2024TaxaBind: A Unified Embedding Space for Ecological Applicationscited by 35
  3. 2023Sat2Cap: Mapping Fine-Grained Textual Descriptions from Satellite Imagescited by 19
  4. 2016Understanding and Mapping Natural Beautycited by 10

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 1.

1 paper.

  1. 2026Exploring the Potential of Contrastive Language-Image Pre-training for Multi-Source Remote Sensing Datacited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2024Remote Sensing SpatioTemporal Vision-Language Models: A Comprehensive Survey
  2. 2024From Pixels to Prose: Advancing Multi-Modal Language Models for Remote Sensing
  3. 2025Vision-Language Modeling Meets Remote Sensing: Models, Datasets and Perspectives
  4. 2026More with Less: a Large Scale Remote Sensing VLM with a Simple Recipe
  5. 2024UniRS: Unifying Multi-temporal Remote Sensing Tasks through Vision Language Models
  6. 2025Efficient Few-Shot Learning in Remote Sensing: Fusing Vision and Vision-Language Models
  7. 2023RS5M and GeoRSCLIP: A Large Scale Vision-Language Dataset and A Large Vision-Language Model for Remote Sensing
  8. 2025A Recipe for Improving Remote Sensing VLM Zero Shot Generalization