update README

README.md

@@ -19,52 +19,68 @@ annotations_creators:
   - "expert-generated"
 ---
 
-🗺️ GeoChain Dataset
+## GeoChain: Multimodal Chain-of-Thought for Geographic Reasoning
 
-Welcome to the Hugging Face repository for the GeoChain Benchmark data. GeoChain is a large-scale benchmark introduced in the paper "GeoChain: Multimodal Chain-of-Thought for Geographic Reasoning" for evaluating step-by-step geographic reasoning in multimodal large language models (MLLMs).
+**[Paper on arXiv](https://arxiv.org/abs/2506.00785) [[Code on GitHub](https://github.com/sahitiy/geochain)]**
 
-Leveraging 1.46 million Mapillary street-level images, GeoChain pairs each image with a 21-step chain-of-thought (CoT) question sequence, resulting in over 30 million Q&A pairs. These sequences are designed to guide models from coarse attributes to fine-grained localization, covering four key reasoning categories: visual, spatial, cultural, and precise geolocation, with annotations for difficulty. Images within the dataset are also enriched with semantic segmentation (150 classes) and a visual locatability score.
+Sahiti Yerramilli*, Nilay Pande*, Rynaa Grover*, and Jayant Sravan Tamarapalli*, *equal contributions.
 
-Our benchmarking of contemporary MLLMs (including GPT-4.1 variants, Claude 3.7, and Gemini 2.5 variants) on a diverse 2,088-image subset reveals consistent challenges: models frequently exhibit weaknesses in visual grounding, display erratic reasoning, and struggle to achieve accurate localization, especially as reasoning complexity escalates. GeoChain offers a robust diagnostic methodology, critical for fostering significant advancements in complex geographic reasoning within MLLMs.
+![](assets/geochain-teaser.png)
 
-This Hugging Face repository provides the GeoChain dataset files, ready to be used with the datasets library. For all associated code, including data generation scripts, evaluation utilities, and a PyTorch Dataset class, please visit our GitHub repository:
+<p align="justify">
+GeoChain is a large-scale benchmark introduced for evaluating step-by-step geographic reasoning in multimodal large language models (MLLMs). Leveraging 1.46 million Mapillary street-level images, GeoChain pairs each image with a 21-step chain-of-thought (CoT) question sequence, resulting in over 30 million Q&A pairs. These sequences are designed to guide models from coarse attributes to fine-grained localization, covering four key reasoning categories: visual, spatial, cultural, and precise geolocation, with annotations for difficulty. Images within the dataset are also enriched with semantic segmentation (150 classes) and a visual locatability score. Our benchmarking of contemporary MLLMs reveals consistent challenges: models frequently exhibit weaknesses in visual grounding, display erratic reasoning, and struggle to achieve accurate localization, especially as reasoning complexity escalates. GeoChain offers a robust diagnostic methodology, critical for fostering significant advancements in complex geographic reasoning within MLLMs.
+</p>
 
-➡️ GeoChain Code on GitHub: https://github.com/sahitiy/geochain
+## How to Use
 
-📦 Dataset Structure & Content:
+The dataset can be loaded using the Hugging Face `datasets` library:
 
-This dataset provides two main splits for evaluation, processed from the test_samples.csv and main_test_samples.csv files:
+```python
+from datasets import load_dataset
 
-mini_test Split: A smaller subset for quick evaluation runs.
+# Load the mini_test split for quick experiments
+mini_dataset = load_dataset("sahitiy51/geochain", split="mini_test")
 
-Features:
-image: A PIL Image object representing the street-level image, cropped by removing a predefined number of rows from the bottom (e.g., 50 rows, as per the generation script logic detailed in the GitHub repository).
-locatability_score: (float) The visual locatability score of the image.
-lat: (float) Latitude of the image.
-lon: (float) Longitude of the image.
-class_mapping: (string) Associated class mapping.
-sequence_key: (string) Unique sequence identifier.
-Note: key, sub_folder, and city fields from the source CSV are used for image pathing during generation and are not present as distinct features in this processed split.
+# Load the full test split
+full_dataset = load_dataset("sahitiy51/geochain", split="test")
 
+print(mini_dataset[0])
+```
 
-test Split: The full-scale test set for comprehensive evaluation.
+## Dataset Structure
 
-Features:
+This dataset provides two main splits for evaluation:
 
-key: (string) Unique identifier for the image.
-locatability_score: (float) The visual locatability score.
-lat: (float) Latitude of the image.
-lon: (float) Longitude of the image.
-city: (string) City where the image was taken.
-sub_folder: (string) Sub-folder information related to image storage/organization.
-class_mapping: (string) Associated class mapping.
-sequence_key: (string) Unique sequence identifier.
-image: This feature will be None for the test split, as this split primarily provides metadata for evaluation against models that might generate or retrieve images.
+### `mini_test` Split
+A smaller subset for quick evaluation runs.
 
-Citation:
+**Features:**
+* `image`: A PIL Image object representing the street-level image.
+* `locatability_score`: (float) The visual locatability score of the image.
+* `lat`: (float) Latitude of the image.
+* `lon`: (float) Longitude of the image.
+* `class_mapping`: (string) Associated class mapping.
+* `sequence_key`: (string) Unique sequence identifier.
 
-If you use GeoChain benchmark for your research, please cite us
-```
+### `test` Split
+The full-scale test set for comprehensive evaluation.
+
+**Features:**
+* `key`: (string) Unique identifier for the image.
+* `locatability_score`: (float) The visual locatability score.
+* `lat`: (float) Latitude of the image.
+* `lon`: (float) Longitude of the image.
+* `city`: (string) City where the image was taken.
+* `sub_folder`: (string) Sub-folder information related to image storage/organization.
+* `class_mapping`: (string) Associated class mapping.
+* `sequence_key`: (string) Unique sequence identifier.
+* `image`: This feature is `None` for the test split, as this split primarily provides metadata.
+
+## Citation
+
+If you find our work useful, please cite the following paper:
+
+```bibtex
 @misc{yerramilli2025geochainmultimodalchainofthoughtgeographic,
       title={GeoChain: Multimodal Chain-of-Thought for Geographic Reasoning}, 
       author={Sahiti Yerramilli and Nilay Pande and Rynaa Grover and Jayant Sravan Tamarapalli},
@@ -72,6 +88,6 @@ If you use GeoChain benchmark for your research, please cite us
       eprint={2506.00785},
       archivePrefix={arXiv},
       primaryClass={cs.AI},
-      url={https://arxiv.org/abs/2506.00785}, 
+      url={[https://arxiv.org/abs/2506.00785](https://arxiv.org/abs/2506.00785)}, 
 }
-```
+```