Update README.md

README.md

@@ -14,38 +14,52 @@ The **Git-10M** dataset is a global-scale remote sensing image-text pair dataset
   <img src="https://github.com/Chen-Yang-Liu/Text2Earth/raw/main/images/dataset.png" width="1000"/>
 </div>
 
-## Load Dataset
-```python
-from modelscope.msdatasets import MsDataset
-ds =  MsDataset.load('lcybuaa/Git-10M')
-```
-
 
 ## View samples from the dataset 
 ```python
 from datasets import load_dataset
+import math
+
+def XYZToLonLat(x,y,z):
+    # Transform tile-location to (longitude,latitude)
+    n = 2**z*1.0
+    lon = x / n * 360.0 - 180.0  # longitude
+    lat = math.atan(math.sinh(math.pi * (1 - 2.0 * y / n)))
+    lat = math.degrees(lat)  # latitude
+    return lon,lat
+
+
+# load dataset
 save_path = 'xxxxx'
-ds = load_dataset('lcybuaa/Git-10M', cache_dir=save_path)
+ds = load_dataset.load('lcybuaa/Git-10M', cache_dir=save_path)
 train_dataset = ds["train"]
 
+
 for i, example in enumerate(train_dataset):
+    # PIL image:
     image = example["image"]
-    # Text Description
-    text = example["text"].split('_GOOGLE_LEVEL_')[-1] 
-    # Image Resolution
-    Level = int(example["text"].split('_GOOGLE_LEVEL_')[0])
-    if Level != 0:
-        Resolution = 2**(17-Level)
-    else:
-        print('This image comes from a public dataset. There is no available resolution metadata.')
-    # save image
-    image.save(f"image_{i}.png")  #
-    print('text:', text)
-    
+    # filename of the image:
+    img_name = example["img_name"]
+    # visual quality score as shown in Fig. 5 of the paper.
+    img_quality_score = example['img_quality_score']
+    # caption of the image
+    caption = example['caption']
+    # word length of the caption as shown in Fig. 6 of the paper.
+    caption_length = example['caption_length']
+    # image spatial resolution as shown in Fig. 4 of the paper.
+    resolution = example['resolution']
+    # image Geolocation as shown in Fig. 3 of the paper.
+    Google_location = example['Google_location']
+    Level_TileZ, TileX, TileY = Google_location.split('_')
+    longitude, latitude = XYZToLonLat(TileX, TileY, Level_TileZ)
+
+    # More Tips:
+    # Resolution = 2 ** (17 - Level_TileZ)
+
 ```
 
 ## Git-RSCLIP: Remote Sensing Vision-Language Contrastive Pre-training Foundation Model
-Git-RSCLIP is pre-trained using the contrastive learning framework on the Git-10M dataset.
+Git-RSCLIP is pre-trained using the contrastive learning framework on the **Git-10M dataset**.
 Git-RSCLIP is here：[[Huggingface](https://huggingface.co/lcybuaa/Git-RSCLIP) | [Modelscope](https://modelscope.cn/models/lcybuaa1111/Git-RSCLIP)]
 
 Compare the Top1-Acc of Zero-shot classification on multiple image classification datasets:
@@ -63,13 +77,13 @@ Compare the Top1-Acc of Zero-shot classification on multiple image classificatio
 # BibTeX entry and citation info
 
 ```bibtex
-@misc{liu2025text2earthunlockingtextdrivenremote,
-      title={Text2Earth: Unlocking Text-driven Remote Sensing Image Generation with a Global-Scale Dataset and a Foundation Model},
-      author={Chenyang Liu and Keyan Chen and Rui Zhao and Zhengxia Zou and Zhenwei Shi},
-      year={2025},
-      eprint={2501.00895},
-      archivePrefix={arXiv},
-      primaryClass={cs.CV},
-      url={https://arxiv.org/abs/2501.00895},
-}
+@ARTICLE{Text2Earth,
+  author={Liu, Chenyang and Chen, Keyan and Zhao, Rui and Zou, Zhengxia and Shi, Zhenwei},
+  journal={IEEE Geoscience and Remote Sensing Magazine}, 
+  title={Text2Earth: Unlocking text-driven remote sensing image generation with a global-scale dataset and a foundation model}, 
+  year={2025},
+  volume={},
+  number={},
+  pages={2-23},
+  doi={10.1109/MGRS.2025.3560455}}
 ```