readme edit

README.md

@@ -19,7 +19,7 @@ tags:
 - traffic-surveillance
 - vehicle-image
 ---
-# Dataset Card for AIDOVECL
+# AIDOVECL: AI-generated Dataset of Outpainted Vehicles for Eye-level Classification and Localization
 
 <!-- Provide a quick summary of the dataset. -->
 
@@ -40,23 +40,23 @@ Please ensure that all publications and presentations using this data reference
 
 <!-- Provide a longer summary of what this dataset is. -->
 
-Our dataset consists of AI-generated, eye-level vehicle images created by detecting and cropping vehicles from manually chosen seed images. These vehicles are then outpainted onto larger canvases to replicate diverse real-world scenarios. The resulting images are meticulously annotated, offering high-quality ground truth data. Through the use of sophisticated outpainting techniques and stringent image quality assessments, we ensure exceptional visual fidelity and contextual accuracy.
+Our dataset consists of AI-generated, eye-level vehicle images created by detecting and cropping vehicles from manually chosen and classified seed images. These vehicles are then outpainted onto larger canvases to replicate diverse real-world scenarios. The resulting images are meticulously annotated automatically, offering high-quality ground truth data. Through the use of sophisticated outpainting techniques and stringent image quality assessments, we ensure exceptional visual fidelity and contextual accuracy. Moreover, the efficiency and utility of the dataset are demonstrated through benchmark experiments presented in the accompanying paper.
 
 
 - **Curated by:** Amir Kazemi, Qurat ul ain Fatima, Volodymyr Kindratenko, Christopher W Tessum.
-- **Funded by [optional]:** The authors acknowledge support from the University of Illinois National Center for Supercomputing Applications (NCSA) Faculty Fellowship Program, the Zhejiang University and University of Illinois Dynamic Research Enterprise for Multidisciplinary Engineering Sciences (DREMES), and the University of Illinois Discovery Partners Institute (DPI).
+- **Funded by:** The authors acknowledge support from the University of Illinois National Center for Supercomputing Applications (NCSA) Faculty Fellowship Program, the Zhejiang University and University of Illinois Dynamic Research Enterprise for Multidisciplinary Engineering Sciences (DREMES), and the University of Illinois Discovery Partners Institute (DPI).
 This work utilizes resources supported by the National Science Foundation’s (NSF) Major Research Instrumentation program, grant #1725729, as well as the University of Illinois at Urbana-Champaign.
-- **Shared by [optional]:** NA
+- **Shared by:** NA
 - **Language(s) (NLP):** NA
 - **License:** Creative Commons Attribution 4.0 International
 
-### Dataset Sources [optional]
+### Dataset Sources
 
 <!-- Provide the basic links for the dataset. -->
 
 - **Repository:** https://huggingface.co/datasets/amir-kazemi/aidovecl/tree/main
-- **Paper [optional]:** https://arxiv.org/abs/2410.24116
-- **Demo [optional]:** https://github.com/amir-kazemi/aidovecl
+- **Paper:** https://arxiv.org/abs/2410.24116
+- **Demo:** https://github.com/amir-kazemi/aidovecl
 
 ## Uses
 
@@ -65,18 +65,12 @@ This work utilizes resources supported by the National Science Foundation’s (N
 ### Direct Use
 
 <!-- This section describes suitable use cases for the dataset. -->
-This dataset is intended for several key applications:
-Autonomous Driving: Enhancing the training of machine learning models for better vehicle detection and classification in diverse and complex urban environments.
-Urban Planning: Assisting in the analysis and design of urban infrastructure by providing detailed and annotated eye-level vehicle images.
-Environmental Monitoring: Enabling better monitoring and management of urban traffic and its environmental impact through comprehensive visual data.
-By addressing the scarcity of annotated data with high-quality, AI-generated images, this dataset aims to improve the performance and generalization of models in these domains.
+This curated dataset combines real, manually classified vehicle images with AI-generated outpainted variants, offering a diverse yet structured resource for computer vision and mobility-related applications. The images maintain contextual plausibility and consistent labeling, making the dataset suitable for training, evaluation, and augmentation workflows. In autonomous driving, it supports vehicle detection and recognition by exposing models to varied environments and viewpoints. The inclusion of fine-grained classes—reflecting differences in vehicle size, passenger capacity, and utility—enables more detailed analysis in urban planning and transportation modeling, such as estimating fleet composition, mode share, and curb usage. For environmental research, the dataset facilitates modeling of vehicle-type-specific emissions and spatial distribution patterns. By combining real-world fidelity with scalable synthetic augmentation, it contributes to more reproducible and generalizable pipelines across both technical and policy-oriented domains.
 
 ### Out-of-Scope Use
 
 <!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. -->
-While the dataset offers substantial improvements in data availability and quality, it is not intended for certain uses:
-Malicious Activities: Any use of this dataset for unlawful activities, including surveillance without consent or privacy infringement, is strictly prohibited.
-Unrelated Fields: The dataset is tailored for applications in autonomous driving, urban planning, and environmental monitoring. Its use in unrelated fields where the context and annotations do not align with the dataset’s design may yield poor performance and unreliable results.
+While the dataset improves access to diverse and well-annotated vehicle imagery, it is not intended for all uses. Malicious or unethical applications—such as unauthorized surveillance or activities that infringe on privacy—are strictly prohibited. The dataset is most suitable for domains like autonomous driving, urban planning, and environmental monitoring, where the vehicle-level annotations and scene contexts are relevant. Applying it in unrelated fields, especially those misaligned with its structure or labeling scheme, may lead to poor performance or misleading outcomes.
 
 ## Dataset Structure
 
@@ -100,15 +94,15 @@ Publicly available vehicle recognition datasets have driven progress in the fiel
 
 <!-- This section describes the data collection and processing process such as data selection criteria, filtering and normalization methods, tools and libraries used, etc. -->
 
-Refer to the manuscript currently under review.
+The data collection and processing procedures are documented in detail in the manuscript.
 
 #### 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. -->
 
-Refer to the manuscript currently under review.
+The origin of the source data is described in detail in the manuscript.
 
-### Annotations [optional]
+### Annotations
 
 <!-- If the dataset contains annotations which are not part of the initial data collection, use this section to describe them. -->
 
@@ -116,13 +110,13 @@ Refer to the manuscript currently under review.
 
 <!-- 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. -->
 
-Self-annotated using a method currently under review.
+All annotations in this study were generated automatically using the pipeline described in the manuscript.
 
 #### Who are the annotators?
 
 <!-- This section describes the people or systems who created the annotations. -->
 
-Self-annotated using a method currently under review.
+No manual annotators were involved.
 
 #### Personal and Sensitive Information
 
@@ -142,18 +136,25 @@ Our dataset currently cannot outpaint multiple vehicles in a single image due to
 
 Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.
 
-## Citation [optional]
+## Citation
 
 <!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. -->
 
 **BibTeX:**
 
-[Under Review]
+```bibtex
+@article{kazemi2024aidovecl,
+  title={AIDOVECL: AI-generated Dataset of Outpainted Vehicles for Eye-level Classification and Localization},
+  author={Kazemi, Amir and Kindratenko, Volodymyr and Tessum, Christopher and others},
+  journal={arXiv preprint arXiv:2410.24116},
+  year={2024}
+}
+```
 
 **APA:**
-
-[Under Review]
-
+```bibtex
+Kazemi, A., Kindratenko, V., & Tessum, C. (2024). AIDOVECL: AI-generated Dataset of Outpainted Vehicles for Eye-level Classification and Localization. arXiv preprint arXiv:2410.24116.
+```
 ## Glossary [optional]
 
 <!-- If relevant, include terms and calculations in this section that can help readers understand the dataset or dataset card. -->
@@ -170,4 +171,6 @@ Amir Kazemi
 
 ## Dataset Card Contact
 
-[email protected]
+[email protected]
+
+[email protected]