Update README.md

README.md

@@ -46,7 +46,7 @@ dataset_summary: '
 
   # Note: other available arguments include ''max_samples'', etc
 
-  dataset = load_from_hub("harpreetsahota/SkyScenes")
+  dataset = load_from_hub("Voxel51/SkyScenes")
 
 
   # Launch the App
@@ -60,9 +60,6 @@ dataset_summary: '
 
 # Dataset Card for SkyScenes
 
-<!-- Provide a quick summary of the dataset. -->
-
-
 
 
 
@@ -84,7 +81,7 @@ from fiftyone.utils.huggingface import load_from_hub
 
 # Load the dataset
 # Note: other available arguments include 'max_samples', etc
-dataset = load_from_hub("harpreetsahota/SkyScenes")
+dataset = load_from_hub("Voxel51/SkyScenes")
 
 # Launch the App
 session = fo.launch_app(dataset)
@@ -93,9 +90,14 @@ session = fo.launch_app(dataset)
 
 ## Dataset Details
 
-
 SkyScenes is a comprehensive synthetic dataset for aerial scene understanding that was recently accepted to ECCV 2024. The dataset contains 33,600 aerial images captured from UAV perspectives using the CARLA simulator.
 
+- **Curated by:** [Sahil Khose](https://sahilkhose.github.io/), Anisha Pal, Aayushi Agarwal, Deepanshi, Judy Hoffman, Prithvijit Chattopadhyay
+- **Funded by:** Georgia Institute of Technology
+- **Shared by:** [Harpreet Sahota](https://huggingface.co/harpreetsahota), Hacker-in-Residence at Voxel51
+- **Language(s) (NLP):** en
+- **License:** MIT License
+
 ### Dataset Structure
 - **Images**: RGB images captured across multiple variations:
   - 8 different town layouts (7 urban + 1 rural)
@@ -123,85 +125,49 @@ Each image comes with dense pixel-level annotations for:
   - H_60_P_0 (60m height, 0° pitch)
   - 
 - **Weather Condition**: ClearNoon only
-- 
 - **Town Layouts**: Town01, Town02, Town05, Town07
-- 
 - **Data Modalities**:
   - RGB Images
   - Depth Maps
   - Semantic Segmentation
 
+If you wish to work with the full dataset in FiftyOne format, you can use the [following repo](https://github.com/harpreetsahota204/skyscenes-to-fiftyone).
 
+### Dataset Sources
 
-- **Curated by:** [More Information Needed]
-- **Funded by [optional]:** [More Information Needed]
-- **Shared by [optional]:** [More Information Needed]
-- **Language(s) (NLP):** en
-- **License:** [More Information Needed]
-
-### Dataset Sources [optional]
-
-<!-- Provide the basic links for the dataset. -->
-
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **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. -->
+- **Repository:** https://github.com/hoffman-group/SkyScenes
+- **Paper:** https://arxiv.org/abs/2312.06719
+- **Demo:** https://hoffman-group.github.io/SkyScenes/
 
-[More Information Needed]
+# Uses
 
-### Annotations [optional]
+The dataset contains 33.6k densely annotated synthetic aerial images with comprehensive metadata and annotations, making it suitable for both training and systematic evaluation of aerial scene understanding models.
 
-<!-- If the dataset contains annotations which are not part of the initial data collection, use this section to describe them. -->
+## Training and Pre-training
+- Functions as a pre-training dataset for real-world aerial scene understanding models
+- Models trained on SkyScenes demonstrate strong generalization to real-world scenarios
+- Can effectively augment real-world training data to improve overall model performance
 
-#### Annotation process
+## Model Evaluation and Testing
+**Diagnostic Testing**
+- Serves as a test bed for assessing model sensitivity to various conditions including:
+  - Weather changes
+  - Time of day variations 
+  - Different pitch angles
+  - Various altitudes
+  - Different layout types
 
-<!-- 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. -->
+**Multi-modal Development**
+- Enables development of multi-modal segmentation models by incorporating depth information alongside visual data
+- Supports testing how additional sensor modalities can improve aerial scene recognition capabilities
 
-[More Information Needed]
+## Research Applications
+- Enables studying synthetic-to-real domain adaptation for aerial imagery
+- Provides controlled variations for analyzing model behavior under different viewing conditions
+- Supports development of models for:
+  - Semantic segmentation
+  - Instance segmentation
+  - Depth estimation
 
 ## References