AI RESEARCH
TimeSenCLIP: A Time Series Vision-Language Model for Remote Sensing
arXiv CS.CV
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ArXi:2508.11919v3 Announce Type: replace 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.