Datasets:
metadata
license: apache-2.0
task_categories:
- question-answering
language:
- en
tags:
- remote_sensing
- vlm
size_categories:
- 10K<n<100K
configs:
- config_name: Classification
data_files:
- split: AID_sample
path: >-
Eardial_downstream_task_datasets/Classification/AID/test/data-00000-of-00001.arrow
- split: UCM_sample
path: >-
Eardial_downstream_task_datasets/Classification/UCM/data-00000-of-00001.arrow
- split: BigEarthNet_RGB_sample
path: >-
Eardial_downstream_task_datasets/Classification/BigEarthNet_RGB/BigEarthNet_test/data-00000-of-00004.arrow
- split: WHU_sample
path: >-
Eardial_downstream_task_datasets/Classification/WHU_19/data-00000-of-00001.arrow
- config_name: GeoChat_Bench
data_files:
- split: GeoChat_Bench_sample
path: >-
Eardial_downstream_task_datasets/Detection/Geochat_Bench/data-00000-of-00001.arrow
- config_name: Detection
data_files:
- split: NWPU_VHR_10_sample
path: >-
Eardial_downstream_task_datasets/Detection/NWPU_VHR_10_test/data-00000-of-00001.arrow
- split: Swimming_pool_dataset_sample
path: >-
Eardial_downstream_task_datasets/Detection/Swimming_pool_dataset_test/data-00000-of-00001.arrow
- split: ship_dataset_v0_sample
path: >-
Eardial_downstream_task_datasets/Detection/ship_dataset_v0_test/data-00000-of-00001.arrow
- split: urban_tree_crown_sample
path: >-
Eardial_downstream_task_datasets/Detection/urban_tree_crown_detection_test/data-00000-of-00001.arrow
- config_name: Region_captioning
data_files:
- split: NWPU_VHR_10
path: >-
Eardial_downstream_task_datasets/Region_captioning/NWPU_VHR_10_test_region_captioning/data-00000-of-00001.arrow
- config_name: Image_captioning
data_files:
- split: sydney_Captions
path: >-
Eardial_downstream_task_datasets/Image_captioning/sydney_Captions/sydney_Captions_test/data-00000-of-00001.arrow
- split: UCM_Captions
path: >-
Eardial_downstream_task_datasets/Image_captioning/UCM_Captions/UCM_Captions_test/data-00000-of-00001.arrow
- split: RSICD_Captions
path: >-
Eardial_downstream_task_datasets/Image_captioning/RSICD_Captions/RSICD_Captions_test/data-00000-of-00001.arrow
🌍 EarthDial-Dataset
The EarthDial-Dataset is a curated collection of evaluation-only datasets focused on remote sensing and Earth observation downstream tasks. It is designed to benchmark vision-language models (VLMs) and multimodal reasoning systems on real-world scenarios involving satellite and aerial imagery.
📚 Key Features
- Evaluation-focused: All datasets are for inference/testing only — no train/val splits.
- Diverse Tasks:
- Classification
- Object Detection
- Change Detection
- Grounding Description
- Region Captioning
- Image Captioning
- Visual Question Answering (GeoChat Bench)
- Remote Sensing Specific: Tailored for multispectral, RGB, and high-resolution satellite data.
- Multimodal Format: Includes images, questions, captions, annotations, and geospatial metadata.
🗂️ Dataset Structure
The dataset is structured under the root folder:EarthDial_downstream_task_datasets/
Each task has its own subdirectory with .arrow-formatted shards, structured as:
EarthDial_downstream_task_datasets/
│
├── Classification/
│ ├── AID/
│ │ └── test/data-00000-of-00001.arrow
│ └── ...
│
├── Detection/
│ ├── NWPU_VHR_10_test/
│ ├── Swimming_pool_dataset_test/
│ └── ...
│
├── Region_captioning/
│ └── NWPU_VHR_10_test_region_captioning/
│
├── Image_captioning/
│ ├── RSICD_Captions/
│ └── UCM_Captions/
│...
## 🗂️ Example data usage
from datasets import load_dataset
dataset = load_dataset(
"akshaydudhane/EarthDial-Dataset",
data_dir="EarthDial_downstream_task_datasets/Classification/AID/test"
)
## Example Demo Usage
import argparse
import torch
from PIL import Image
from transformers import AutoTokenizer
from earthdial.model.internvl_chat import InternVLChatModel
from earthdial.train.dataset import build_transform
def run_single_inference(args):
# Load model and tokenizer from Hugging Face Hub
print(f"Loading model and tokenizer from Hugging Face: {args.checkpoint}")
tokenizer = AutoTokenizer.from_pretrained(args.checkpoint, trust_remote_code=True, use_fast=False)
model = InternVLChatModel.from_pretrained(
args.checkpoint,
low_cpu_mem_usage=True,
torch_dtype=torch.bfloat16,
device_map="auto" if args.auto else None,
load_in_8bit=args.load_in_8bit,
load_in_4bit=args.load_in_4bit
).eval()
if not args.load_in_8bit and not args.load_in_4bit and not args.auto:
model = model.cuda()
# Load and preprocess image
image = Image.open(args.image_path).convert("RGB")
image_size = model.config.force_image_size or model.config.vision_config.image_size
transform = build_transform(is_train=False, input_size=image_size, normalize_type='imagenet')
pixel_values = transform(image).unsqueeze(0).cuda().to(torch.bfloat16)
# Generate answer
generation_config = {
"num_beams": args.num_beams,
"max_new_tokens": 100,
"min_new_tokens": 1,
"do_sample": args.temperature > 0,
"temperature": args.temperature,
}
answer = model.chat(
tokenizer=tokenizer,
pixel_values=pixel_values,
question=args.question,
generation_config=generation_config,
verbose=True
)
print("\n=== Inference Result ===")
print(f"Question: {args.question}")
print(f"Answer: {answer}")
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument('--checkpoint', type=str, required=True, help='Model repo ID on Hugging Face Hub')
parser.add_argument('--image-path', type=str, required=True, help='Path to local input image')
parser.add_argument('--question', type=str, required=True, help='Question to ask about the image')
parser.add_argument('--num-beams', type=int, default=5)
parser.add_argument('--temperature', type=float, default=0.0)
parser.add_argument('--load-in-8bit', action='store_true')
parser.add_argument('--load-in-4bit', action='store_true')
parser.add_argument('--auto', action='store_true')
args = parser.parse_args()
run_single_inference(args)
python demo_infer.py \
--checkpoint akshaydudhane/EarthDial_4B_RGB \
--image-path ./test.jpg \
--question "Which road has more vehicles?" \
--auto