Update README.md

README.md

@@ -9,14 +9,22 @@ tags:
 - spatial-reasoning
 - benchmark
 - quantitative-reasoning
+- VLM
+- language-understanding
 size_categories:
 - 1K<n<10K
+pretty_name: SQuID
 ---
 
 # SQuID: Satellite Quantitative Intelligence Dataset
 
 A comprehensive benchmark for evaluating quantitative spatial reasoning in Vision-Language Models using satellite imagery.
 
+## Related Resources
+
+- **Code repository**: https://github.com/PeterAMassih/qvlm-squid
+- **Paper (arXiv)**: https://arxiv.org/abs/2601.13401
+
 ## Dataset Overview
 
 - **2000 questions** testing spatial reasoning on satellite imagery
@@ -215,18 +223,43 @@ The acceptable ranges represent the natural variation in human perception for sp
 - All measurements use metric units based on the specified GSD (Ground Sampling Distance)
 - Count ranges use proportional MADc (0.19) so larger counts have wider acceptable ranges
 
+## Source Datasets & Attribution
+
+SQuID is constructed from publicly available remote-sensing datasets. We use only images from published validation or test splits and comply with the original dataset licenses.
+
+- **DeepGlobe**  
+  Ilke Demir et al., *DeepGlobe 2018: A Challenge to Parse the Earth through Satellite Images*, CVPR Workshops 2018.  
+  Source: https://deepglobe.org/
+
+- **EarthVQA**  
+  Junjue Wang et al., *EarthVQA: Towards Queryable Earth via Relational Reasoning-based Remote Sensing Visual Question Answering*, ICCV 2023.  
+  Source: https://github.com/WangJunjue/EarthVQA
+
+- **Photovoltaic (Solar Panels) Dataset**  
+  H. Jiang et al., *Multi-resolution dataset for photovoltaic panel segmentation from satellite and aerial imagery*, Earth System Science Data, 2021.  
+  Source: https://essd.copernicus.org/articles/13/5389/2021/
+
+- **NAIP Imagery**  
+  U.S. Geological Survey, *National Agriculture Imagery Program (NAIP)*.  
+  Source: https://www.usgs.gov/core-science-systems/national-geospatial-program/national-agriculture-imagery-program
+
+All derived annotations, questions, and acceptable answer ranges introduced in SQuID are released under **CC BY 4.0**.
+
+
 ## Citation
 
 If you use this dataset, please cite:
 
 ```bibtex
-@article{massih2025squid,
-  title={Preserving Pixel-Level Precision: SQuID Dataset and QVLM Architecture for Quantitative Geospatial Reasoning},
-  author={Peter A. Massih, Eric Cosatto},
-  journal={arXiv preprint arXiv:XXXX.XXXXX},
-  year=2025
+@misc{massih2026reasoningpixellevelprecisionqvlm,
+      title={Reasoning with Pixel-level Precision: QVLM Architecture and SQuID Dataset for Quantitative Geospatial Analytics}, 
+      author={Peter A. Massih and Eric Cosatto},
+      year={2026},
+      eprint={2601.13401},
+      archivePrefix={arXiv},
+      primaryClass={cs.CV},
+      url={https://arxiv.org/abs/2601.13401}, 
 }
 ```
 
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-*Generated on 2026-01-18 17:13:25*
+---