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SkyScript

[Paper] [Citing] [Project Page] (under construction)

Welcome to the official repository of SkyScript. SkyScript is a large and semantically diverse image-text dataset for remote sensing images. It contains 5.2 million remote sensing image-text pairs in total, covering more than 29K distinct semantic tags. The paper is accepted by and to appear in AAAI 2024.

Remote sensing imagery, despite its broad applications in helping achieve Sustainable Development Goals and tackle climate change, has not fully benefited from the recent advancements of versatile, task-agnostic vision language models (VLMs). A key reason is that large and semantically diverse image-text datasets required for developing VLMs are not readily available for remote sensing images. Unlike natural images, remote sensing images and their associated text descriptions cannot be efficiently collected from the web at scale. Here we bridge this gap by connecting the “web” of geography—OpenStreetMap (OSM) with multi-source, multi-resolution remote sensing images, resulting in SkyScript, a large-scale remote sensing image-text dataset with considerably rich semantic diversity.

The model implementation of CLIP is adapted from the OpenCLIP repository.

News [2024/12] We have converted raw OSM tags into free-form, smoother, and more natural language description using ChatGPT for each image. These new datasets (CSV files) can be downloaded with the URL links Full 5M dataset, Top 50%, Top 30%.

Download

Download SkyScript dataset

Download image files

The full dataset contains 5.2 million image-text pairs. They are partitioned into 6 parts. The image files can be downloaded using the following command:

curl -O https://opendatasharing.s3.us-west-2.amazonaws.com/SkyScript/images2.zip

Please replace images2.zip with filenames ranging from images2.zip to images7.zip to download all 6 parts. Each zip file can be uncompressed into a folder. Image files (.jpg) are contained in these folders. The filename of each image file contains key meta information (object ID, image source, year) about the image. For example, a198234555_CH_19.jpg means:

  1. The image is retrieved with the OSM object with ID 198234555 as the focus object (not necessarily at the image center). Here the leading "a" means "area" (polygon). Other leading letters are "w" ("way", polyline) and "n" ("node", point).
  2. The image source is CH, which is the alias of ORTHO/Switzerland/SWISSIMAGE/10cm (SWISSIMAGE 10cm RGB imagery from Google Earth Engine). The conversion between the image source alias code and Google Earth Engine image collection name is in image_sources.py.
  3. The image was captured in 2019.

Download meta files

In addition to the meta information contained in the image file name, there is a meta file (.pickle) containing other information corresponding to each image. These meta files can be downloaded using the following command:

curl -O https://opendatasharing.s3.us-west-2.amazonaws.com/SkyScript/meta2.zip

Please replace meta2.zip with filenames ranging from meta2.zip to meta7.zip to download all 6 parts. Each zip file can be uncompressed into a folder. Each meta file (.pickle) has the same name as its corresponding image file (except the file extension). Each meta file can be loaded with pickle library as a Python dictionary, which contains the following information:

  1. box: the bounding box (latitute/longitude) of each image, represented as a tuple of four numerical values (westernmost_longitude, southernmost_latitude, easternmost_longitude, northernmost_latitude).
  2. time: image acquisition time, which is extracted from the Google Earth Engine. It is represented as a tuple containing five elements (year, month, day, hour, minute).
  3. center_tags: a dictionary containing the visually groundable semantic tags (key-value pairs) of the focus object. The focus object is used for locating and retrieving the image but not necessarily at the image center.
  4. surrounding_tags: a list of dictionaries. Each dictionary contains the visually groundable semantic tags (key-value pairs) of one of the surrounding objects in the image.

Download captions

Image-text pairs are represented as a CSV file containing (1) a column called filepath (the relative path to the image file); (2) a column called title (the caption describing the focus object only); (3) a column called title_multi_objects (the caption describing both the focus object and surrounding objects.

These CSV files can be downloaded with the following command:

curl -O https://opendatasharing.s3.us-west-2.amazonaws.com/SkyScript/dataframe/{FILENAME}

Here the {FILENAME} should be replaced with the following:

  • SkyScript_train_unfiltered_5M.csv: all image-text pairs for training without any filtering. It contains 5.1 million image-text pairs.

  • SkyScript_train_top30pct_filtered_by_CLIP_openai.csv: The top 30% most similar image-text pairs for training. The similarity is determined by the original CLIP model (OpenAI's checkpoint). It contains 1.5 million image-text pairs.

  • SkyScript_train_top50pct_filtered_by_CLIP_openai.csv: The top 50% most similar image-text pairs for training. The similarity is determined by the original CLIP model (OpenAI's checkpoint). It contains 2.6 million image-text pairs.

  • SkyScript_val_5K_filtered_by_CLIP_openai.csv: Validation set used during training. The similarity is determined by the original CLIP model (OpenAI's checkpoint). It contains 5K image-text pairs.

  • SkyScript_test_30K_filtered_by_CLIP_openai.csv: Test set used for testing cross-modal retrieval performance. The similarity is determined by the original CLIP model (OpenAI's checkpoint). It contains 30K image-text pairs.

  • SkyScript_train_top30pct_filtered_by_CLIP_laion_RS.csv: The top 30% most similar image-text pairs for training. The similarity is determined by the CLIP-laion-RS model. It contains 1.5 million image-text pairs.

  • SkyScript_train_top50pct_filtered_by_CLIP_laion_RS.csv: The top 50% most similar image-text pairs for training. The similarity is determined by the CLIP-laion-RS model. It contains 2.6 million image-text pairs.

  • SkyScript_val_5K_filtered_by_CLIP_laion_RS.csv: Validation set used during training. The similarity is determined by the CLIP-laion-RS model. It contains 5K image-text pairs.

  • SkyScript_test_30K_filtered_by_CLIP_laion_RS.csv: Test set used for testing cross-modal retrieval performance. The similarity is determined by the CLIP-laion-RS model. It contains 30K image-text pairs.

  • SkyScript_train_unfiltered_5M.csv: (New!) Used ChatGPT to convert raw OSM tags into free-formed, smoother, and more natural language descripion. It contains 4.7 million image-text pairs.

  • SkyScript_train_top30pct_filtered_by_CLIP_laion_RS_language_polished.csv: (New!) Used ChatGPT to convert raw OSM tags into free-formed, smoother, and more natural language descripion. The top 30% most similar image-text pairs (1.5 million). The similarity is determined by the CLIP-laion-RS model.

  • SkyScript_train_top50pct_filtered_by_CLIP_laion_RS_language_polished.csv: (New!) Used ChatGPT to convert raw OSM tags into free-formed, smoother, and more natural language descripion. The top 50% most similar image-text pairs (2.6 million). The similarity is determined by the CLIP-laion-RS model.

NOTE: Here the captions are automatically assembled from semantic tags using a rule-based approach, as described in the paper. We welcome and encourage researchers to use more advanced techniques (e.g., Large Language Model) to generate more natural and diverse captions from semantic tags (semantic tags are provided in the meta files).

Download benchmark datasets

The benchmark datasets can be downloaded using the following command:

curl -O https://opendatasharing.s3.us-west-2.amazonaws.com/SkyScript/benchmark/{FILENAME}

Here the {FILENAME} should be replaced with the following:

  • Scene classification: {FILENAME} should be replaced with aid.zip (AID), eurosat.zip (EuroSAT), fmow.zip (fMoW), millionaid.zip (MillionAID), nwpu.zip (NWPU-RESISC45), patternnet.zip (PatterNet), rsicb256.zip (RSI-CB256), and SkyScript_cls.zip. Here SkyScript_cls.zip is the in-domain test set (containing 70 classes) while the remaining ones are out-of-domain test sets (datasets not used for training).
  • Fine-grained classification: {FILENAME} should be replaced with roof_shape.zip (roof shape classification), smoothness.zip (road smoothness classification), and surface.zip (road surface material classification).
  • Cross-modal retrieval: {FILENAME} should be replaced with RSICD.zip, RSITMD.zip, and ucmcaptions.zip (UCM-Captions).

Download model checkpoints

The model checkpoints can be downloaded using the following command:

curl -O https://opendatasharing.s3.us-west-2.amazonaws.com/SkyScript/ckpt/{MODEL_NAME}

Here the {MODEL_NAME} should be replaced with the following:

  • SkyCLIP_ViT_L14_top30pct.zip: CLIP ViT-L14 model. Continual pretraining with SkyScript_train_top30pct_filtered_by_CLIP_openai.csv (using the captions from the title column).
  • SkyCLIP_ViT_L14_top50pct.zip: CLIP ViT-L14 model. Continual pretraining with SkyScript_train_top50pct_filtered_by_CLIP_openai.csv (using the captions from the title column).
  • SkyCLIP_ViT_L14_top30pct_filtered_by_CLIP_laion_RS.zip: CLIP ViT-L14 model. Continual pretraining with SkyScript_train_top30pct_filtered_by_CLIP_laion_RS.csv (using the captions from the title column).
  • SkyCLIP_ViT_L14_top30pct_multi_objects.zip: CLIP ViT-L14 model. Continual pretraining with SkyScript_train_top30pct_filtered_by_CLIP_openai.csv (using the captions from the title_multi_objects column).
  • SkyCLIP_ViT_B32_top50pct.zip: CLIP ViT-B32 model. Continual pretraining with SkyScript_train_top50pct_filtered_by_CLIP_openai.csv (using the captions from the title column).
  • CLIP_ViT_L14_LAION_RS.zip: CLIP ViT-L14 model. Continual pretraining with the remote sensing image subset of the LAION-2B dataset.

Testing

Please specify the root directory of benchmark datasets (BENCHMARK_DATASET_ROOT_DIR) in benchmark_dataset_info.py

BENCHMARK_DATASET_ROOT_DIR = '/PATH/TO/THE/ROOT/DIRECTORY/OF/BENCHMARK/DATASETS'

Testing zero-shot classification performance (scene classification and fine-grained classification)

Please follow the test_scene_and_fine_grained_classification.ipynb. Note: Please specify the local paths to benchmark datasets and model checkpoints in this notebook.

For reference, the average top-1 zero-shot scene classification performances (average of aid, eurosat, fmow, millionaid, patternnet, nwpu, and rsicb) for different model checkpoints are listed as below (run on a single NVIDIA A100 GPU):

Model checkpoint name Model type Avg. top-1 accuracy (%)
laion2b_e16 (OpenCLIP) CLIP ViT-B32 49.66
SkyCLIP_ViT_B32_top50pct CLIP ViT-B32 53.02
OpenAI CLIP ViT-L14 53.76
CLIP_ViT_L14_LAION_RS CLIP ViT-L14 57.87
SkyCLIP_ViT_L14_top30pct CLIP ViT-L14 59.91
SkyCLIP_ViT_L14_top30pct_filtered_by_CLIP_laion_RS CLIP ViT-L14 60.69
SkyCLIP_ViT_B32_top50pct CLIP ViT-L14 59.93

Testing zero-shot cross-modal retrieval (image-to-text and text-to-image)

Please follow the cross_modal_retrieval.ipynb. Note: Please specify the local paths to benchmark datasets and model checkpoints in this notebook.

Training

Multi-GPU training can be performed using the following command:

torchrun --nproc_per_node $NUM_GPUS customized_train_and_test.py \
    --root-data-dir=$ROOT_DATA_DIR \
    --name=$MODEL_NAME \
    --save-frequency 1 \
    --val-frequency 1 \
    --train-data=$TRAINING_CSV_PATH  \
    --val-data=$VAL_CSV_PATH  \
    --csv-img-key filepath \
    --csv-separator=',' \
    --csv-caption-key $CAPTION_KEY \
    --random-rotation \
    --warmup 1000 \
    --batch-size=$BATCH_SIZE \
    --lr=$LR \
    --wd=$WD \
    --epochs=20 \
    --workers=8 \
    --model=$MODEL \
    --pretrained=$PRETRAINED \
    --aug-cfg use_timm=True color_jitter=0.4 scale="(0.67, 1.0)" ratio="(0.5, 2.0)"

Here $ROOT_DATA_DIR is the root directory to the SkyScript dataset (e.g., if $ROOT_DATA_DIR = '/home/ubuntu/data/SkyScript', then the full path to an image file is /home/ubuntu/data/SkyScript/images2/....jpg). $NUM_GPUS is the number of GPUs used for training (we used 4). $MODEL_NAME is a given name (string) of the model. $TRAINING_CSV_PATH is the local path to the training CSV file (e.g., SkyScript_train_top50pct_filtered_by_CLIP_openai.csv). $VAL_CSV_PATH is the local path to the validation CSV file (e.g., SkyScript_val_5K_filtered_by_CLIP_openai.csv). $CAPTION_KEY is the column name of captions (title or title_multi_objects). $BATCH_SIZE is the batch size (we used 128). $LR is the learning rate (we used 3e-9 for CLIP ViT-L14 and 1e-9 for CLIP ViT-B32). $WD is the weight decay (we used 1.0). $MODEL is the model type (we used ViT-L-14 or ViT-B-32). $PRETRAINED is the pretrained checkpoint for model initialization (we used openai for ViT-L-14 and laion2b_e16 for ViT-B-32).

Citing

If you found this dataset useful, please consider citing:

@article{wang2023skyscript,
  title={SkyScript: A Large and Semantically Diverse Vision-Language Dataset for Remote Sensing},
  author={Wang, Zhecheng and Prabha, Rajanie and Huang, Tianyuan and Wu, Jiajun and Rajagopal, Ram},
  journal={arXiv preprint arXiv:2312.12856},
  year={2023}
}