MVRL
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Sat2Cap

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 ---
 library_name: transformers
-tags: []
 ---
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 ---
+license: other
+tags:
+  - remote-sensing
+  - satellite-imagery
+  - aerial-imagery
+  - vision-language
+  - zero-shot-mapping
+  - contrastive-learning
+  - clip
+  - cvprw-2024
+pipeline_tag: image-feature-extraction
 library_name: transformers
+arxiv: 2307.15904
 ---
+# Sat2Cap
+Sat2Cap is a vision-language model for **zero-shot mapping** from overhead imagery. Given satellite or aerial imagery, Sat2Cap predicts representations aligned with ground-level visual/textual concepts, enabling downstream mapping with free-form natural language queries.
+This model is associated with the CVPRW 2024 paper:
+**Sat2Cap: Mapping Fine-Grained Textual Descriptions from Satellite Images**
+Aayush Dhakal, Adeel Ahmad, Subash Khanal, Srikumar Sastry, Hannah Kerner, Nathan Jacobs
+Sat2Cap is designed for mapping concepts that are difficult to express as a fixed set of land-cover or object classes. Instead of training a separate classifier for every attribute, the model learns a shared representation that can be queried using text.
+## Model Details
+- **Developed by:** Multimodal Vision Research Laboratory (MVRL), Washington University in St. Louis
+- **Model type:** Vision-language / cross-view representation model
+- **Primary modality:** Overhead satellite or aerial imagery
+- **Output:** Embeddings aligned with CLIP-style ground-level visual/textual representations
+- **Task:** Zero-shot mapping with free-form text queries
+- **Paper:** [Sat2Cap: Mapping Fine-Grained Textual Descriptions from Satellite Images](https://doi.org/10.1109/CVPRW63382.2024.00058)
+- **arXiv:** [2307.15904](https://arxiv.org/abs/2307.15904)
+## Intended Use
+Sat2Cap can be used for research on:
+- text-based mapping from overhead imagery
+- weakly supervised remote-sensing representation learning
+- cross-view learning between overhead and ground-level imagery
+- geographic vision-language models
+- retrieval or scoring of locations using free-form textual concepts
+Example queries might include concepts such as seasonal activity, land use, visible human activity, scene ambience, or ground-level attributes that may be correlated with overhead appearance.
+## Out-of-Scope Use
+Sat2Cap should not be used as a standalone system for:
+- safety-critical or emergency-response decisions
+- legal, financial, insurance, or eligibility decisions
+- surveillance or individual-level tracking
+- definitive factual claims about a specific property, person, or event
+- applications where errors in geographic inference could cause harm
+The model predicts likely ground-level concepts from overhead imagery and learned correlations. It does not directly observe ground-level conditions at inference time.
+## How It Works
+Sat2Cap learns from paired overhead and ground-level imagery. For a given location and overhead image, the model predicts the expected CLIP embedding of the associated ground-level scenery. These predicted embeddings can then be compared with text embeddings to support free-form textual mapping.
+The paper reports training on a large-scale weakly supervised dataset of **6.1M paired overhead and ground-level images**. Sat2Cap can also incorporate temporal information, allowing it to model concepts that vary over time.
+## Training Data
+Sat2Cap is trained using weak supervision from paired overhead and ground-level imagery. The associated paper reports a dataset of **6.1M overhead/ground-level image pairs**.
+Because the model learns from naturally collected imagery, its behavior can reflect geographic coverage patterns, temporal sampling bias, camera/platform bias, and regional imbalances present in the training data.
 ## Evaluation
+The Sat2Cap paper evaluates the model's ability to capture ground-level concepts and support large-scale mapping of fine-grained textual queries. Please see the paper for the full experimental protocol, baselines, metrics, and qualitative examples.
+## Limitations and Biases
+Sat2Cap has several important limitations:
+- It infers likely ground-level concepts from overhead imagery rather than directly observing ground-level conditions.
+- Predictions may be unreliable in regions underrepresented in the training data.
+- Seasonal, cultural, economic, and geographic correlations may introduce bias.
+- Fine-grained text queries may produce plausible but incorrect geographic patterns.
+- Temporal behavior depends on the temporal coverage and metadata available during training and inference.
+- The model should be validated on the target region and use case before deployment.
+Users should treat Sat2Cap outputs as research signals or hypotheses, not as authoritative observations.
+## License
+This model is currently marked as **research-use only / license pending**.
+Before assigning a standard open license to the model weights, please verify the licensing status of:
+- the Bing Maps overhead imagery used during training
+- the YFCC100M/Flickr ground-level imagery and its per-image Creative Commons licenses
+- any upstream CLIP or model initialization weights
+- the intended redistribution rights for the trained checkpoint
+Because the training data includes third-party imagery with its own terms, users should not assume that this checkpoint is approved for commercial use, redistribution, or deployment in production systems unless a separate license explicitly grants those rights.
+## Citation
+If you use this model, please cite:
+```bibtex
+@inproceedings{dhakal2024sat2cap,
+  title={Sat2Cap: Mapping Fine-Grained Textual Descriptions from Satellite Images},
+  author={Dhakal, Aayush and Ahmad, Adeel and Khanal, Subash and Sastry, Srikumar and Kerner, Hannah and Jacobs, Nathan},
+  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops},
+  pages={533--542},
+  year={2024},
+  doi={10.1109/CVPRW63382.2024.00058}
+}
+```
+## Contact
+For questions, issues, or collaboration inquiries, please contact the Multimodal Vision Research Laboratory:
+- Website: https://mvrl.cse.wustl.edu/
+- Hugging Face: https://huggingface.co/MVRL
+- GitHub: https://github.com/mvrl