README.md

metadata

license: cc-by-nc-nd-4.0

The Git-10M dataset is a global-scale remote sensing image-text pair dataset, consisting of over 10 million image-text pairs with geographical locations and resolution information.

CC-BY-NC-ND-4.0 License: This dataset is not allowed to be modified or distributed without authorization!

Project Page: https://chen-yang-liu.github.io/Text2Earth/

View samples from the dataset

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.load('lcybuaa/Git-10M', cache_dir=save_path)
train_dataset = ds["train"]


for i, example in enumerate(train_dataset):
    # PIL image:
    image = example["image"]
    # 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 here：[Huggingface | Modelscope]

Compare the Top1-Acc of Zero-shot classification on multiple image classification datasets:

Method	OPTIMAL31	RSC11	RSICB128	WHURS19	RS2800/RSSCN7	CLRS	Average score
CLIP	0.6	0.45	0.25	0.77	0.52	0.56	0.52
RemoteCLIP	0.82	0.67	0.34	0.93	0.52	0.66	0.65
GeoRSCLIP	0.83	0.67	0.35	0.89	0.63	0.69	0.68
SkyCLIP50	0.77	0.60	0.38	0.78	0.55	0.61	0.62
(Git-RSCLIP) Ours	0.95	0.67	0.52	0.94	0.64	0.65	0.73

BibTeX entry and citation info

@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}}