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Geo_dataset

This PR significantly improves the dataset card by:

- Adding a concise and informative description of the Geo_dataset.
- Providing links to the paper and the GitHub repository.
- Specifying the `task_categories` as `image-segmentation`.
- Removing unnecessary placeholders.

The dataset description now clearly states the dataset's purpose and contents, making it more discoverable and useful to potential users.

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README.md +11 -116

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 license: apache-2.0
 tags:
 - geospatial
 ---
 # Dataset Card for Geo_dataset
-<!-- Provide a quick summary of the dataset. -->
-The Geo_dataset is a cross-domain segmentation dataset for remote sensing images, comprising four subsets: ISPRS Potsdam, ISPRS Vaihingen, Loveda Urban, and Loveda Rural. It is suitable for conducting domain adaptation and domain generalization experiments.
 ## Dataset Details
-<!-- ### Dataset Description
-<!-- Provide a longer summary of what this dataset is. -->
-<!-- - **Curated by:** [More Information Needed]
-- **Funded by [optional]:** [More Information Needed]
-- **Shared by [optional]:** [More Information Needed]
-- **Language(s) (NLP):** [More Information Needed]
-- **License:** [More Information Needed] -->
- -->
-### Dataset Sources [optional]
-<!-- Provide the basic links for the dataset. -->
-<!-- - **Repository:** [More Information Needed] -->
-- **Paper :** https://arxiv.org/abs/2504.06220
-<!-- - **Demo [optional]:** [More Information Needed] -->
 ## Uses
-<!-- Address questions around how the dataset is intended to be used. -->
 ### Direct Use
-<!-- This section describes suitable use cases for the dataset. -->
-[More Information Needed]
 ### Out-of-Scope Use
-<!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. -->
-[More Information Needed]
 ## Dataset Structure
-<!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. -->
-[More Information Needed]
-## Dataset Creation
-### Curation Rationale
-<!-- Motivation for the creation of this dataset. -->
-[More Information Needed]
-### Source Data
-<!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). -->
-#### Data Collection and Processing
-<!-- This section describes the data collection and processing process such as data selection criteria, filtering and normalization methods, tools and libraries used, etc. -->
-[More Information Needed]
-#### Who are the source data producers?
-<!-- This section describes the people or systems who originally created the data. It should also include self-reported demographic or identity information for the source data creators if this information is available. -->
-[More Information Needed]
-### Annotations [optional]
-<!-- If the dataset contains annotations which are not part of the initial data collection, use this section to describe them. -->
-#### Annotation process
-<!-- This section describes the annotation process such as annotation tools used in the process, the amount of data annotated, annotation guidelines provided to the annotators, interannotator statistics, annotation validation, etc. -->
-[More Information Needed]
-#### Who are the annotators?
-<!-- This section describes the people or systems who created the annotations. -->
-[More Information Needed]
-#### Personal and Sensitive Information
-<!-- State whether the dataset contains data that might be considered personal, sensitive, or private (e.g., data that reveals addresses, uniquely identifiable names or aliases, racial or ethnic origins, sexual orientations, religious beliefs, political opinions, financial or health data, etc.). If efforts were made to anonymize the data, describe the anonymization process. -->
-[More Information Needed]
-## Bias, Risks, and Limitations
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
-[More Information Needed]
-### Recommendations
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
-Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.
-## Citation [optional]
-<!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. -->
-**BibTeX:**
-[More Information Needed]
-**APA:**
-[More Information Needed]
-## Glossary [optional]
-<!-- If relevant, include terms and calculations in this section that can help readers understand the dataset or dataset card. -->
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-## More Information [optional]
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-## Dataset Card Authors [optional]
-[More Information Needed]
-## Dataset Card Contact
-[More Information Needed]

 license: apache-2.0
 tags:
 - geospatial
+task_categories:
+- image-segmentation
 ---
 # Dataset Card for Geo_dataset
+The Geo_dataset is a cross-domain segmentation dataset for remote sensing images, comprising four subsets: ISPRS Potsdam, ISPRS Vaihingen, Loveda Urban, and Loveda Rural.  It's designed for conducting domain adaptation and domain generalization experiments in the context of the Earth-Adapter model.  The dataset consists of 512x512 image patches and their corresponding segmentation masks.
 ## Dataset Details
+- **Paper:** [Earth-Adapter: Bridge the Geospatial Domain Gaps with Mixture of Frequency Adaptation](https://huggingface.co/papers/2504.06220)
+- **Code:** [https://github.com/VisionXLab/Earth-Adapter](https://github.com/VisionXLab/Earth-Adapter)
+- **Dataset Sources:**  ISPRS Potsdam, ISPRS Vaihingen, Loveda Urban, and Loveda Rural.  Available via Baidu Cloud, Hugging Face, and Google Drive links provided in the GitHub repository.
 ## Uses
 ### Direct Use
+The Geo_dataset is intended for research on domain adaptation and domain generalization in remote sensing image segmentation.  It can be used to train and evaluate image segmentation models, particularly those focused on handling domain shifts in geospatial data.
 ### Out-of-Scope Use
+While the dataset can be used for general image segmentation tasks, its primary focus is on geospatial data and the challenges associated with domain adaptation and generalization in this domain.  Therefore, its direct applicability to other domains may be limited.
 ## Dataset Structure
+The dataset is organized into four subsets, each containing a set of 512x512 pixel images and their corresponding segmentation masks.  Further details on the structure are available within the GitHub repository.
+**(The remaining sections – Dataset Creation, Bias, Risks, and Limitations, Citation, Glossary, More Information, Dataset Card Authors, Dataset Card Contact – can be filled in later as more information becomes available.  The initial card provides the core necessary information.)**