README: Added dataset description, dataset sources, source data, bias, risks and limitaitons

README.md

@@ -22,16 +22,13 @@ research on generalizability and robustness. The dataset is curated using the `M
 - **Curated by:** Airlab at CMU (Cherie Ho, Jiaye Zou, Omar Alama, Sai Mitheran Jagadesh Kumar, Benjamin Chiang, Taneesh Gupta, Chen Wang, Nikhil Keetha, Katia Sycara, Sebastian Scherer)
 - **License:** The first-person-view images and the associated metadata of MIA dataset is published under CC-By-SA following Mapillary. The bird’s eye view map of MIA dataset is published under ODbL following OpenStreetMap. 
 
-### Dataset Sources [optional]
-
-<!-- Provide the basic links for the dataset. -->
-<!-- Write about mapillary and osm -->
+### Dataset Sources 
+The MIA dataset is generated using the MIA data engine, an open-sourced data curation pipeline for automatically curating paired world-scale FPV & BEV data.
 
 - **Repository:** https://github.com/MapItAnywhere/MapItAnywhere
 
 ## Uses
 
-
 ### Direct Use
 
 
@@ -72,30 +69,38 @@ The MIA data engine and dataset were created to accelerate research progress tow
 
 ### Source Data
 
-<!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). -->
-<!-- Broadly talk about source and annotations (lump Data Collection and Processing and Who are the source data producers?) -->
+The MIA dataset includes data from two sources: [Mapillary](https://www.mapillary.com/) for First-Person-View (FPV) images, and [OpenStreetMap](https://www.openstreetmap.org) for Bird-Eye-View (BEV) maps. 
 
+For FPV retrieval, we leverage Mapillary, a massive public database, licensed under [CC BY-SA](https://creativecommons.org/licenses/by-sa/4.0/), with over 2 billion crowd-sourced images. The images span various weather and lighting conditions collected using diverse camera models and focal lengths. Furthermore, images are taken by pedestrians, vehicles, bicyclists, etc. This diversity enables the collection of more dynamic and difficult scenarios critical for anywhere map prediction.
+When uploading to the Mapillary platform, users submit them under Mapillary's terms and all images shared are under a CC-BY-SA license, more details can be found in [Mapillary License Page](https://help.mapillary.com/hc/en-us/articles/115001770409-Licenses).
+In addition, Mapillary integrates several mechanisms to minimize privacy concerns, such as applying technology to blur any faces and license plates, requiring users to notify if they observe any imageries that may contain personal data. More information can be found on the [Mapillary Privacy Policy page](https://www.mapillary.com/privacy).
 
+For BEV retrieval, we leverage OpenStreetMap (OSM), a global crowd-sourced mapping platform open-sourced under [Open Data Commons Open Database License (ODbL)](https://opendatacommons.org/licenses/odbl/). OSM provides
+rich vectorized annotations for streets, sidewalks, buildings, etc. OpenStreetMap has limitations on mapping private information where "it violates the privacy
+of people living in this world", with guidelines found [here](https://wiki.openstreetmap.org/wiki/Limitations_on_mapping_private_information).
 
-## Bias, Risks, and Limitations
 
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
+## Bias, Risks, and Limitations
 
-[More Information Needed]
+While we show promising generalization performance on conventional datasets, we note that label noise inherently exists, to a higher degree 
+than manually collected data, in crowd sourced data, in both pose correspondence, and in BEV map labeling. Such noise is common across large-scale
+automatically scraped/curated benchmarks such as ImageNet. While we recognize that our sampled dataset is biased towards locations in the US, our MIA data engine is
+applicable to other world-wide locations.
+Our work relies heavily on crowd sourced data putting the burden of data collection on people and open-source contributions.
 
-## Citation [optional]
+<!-- ## 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:**
+<!-- **BibTeX:** -->
 
-[More Information Needed]
+<!-- [More Information Needed] -->
 
-**APA:**
+<!-- **APA:** -->
 
-[More Information Needed]
+<!-- [More Information Needed] -->
 
-## Dataset Card Authors [optional]
+## Dataset Card Authors
 
 Cherie Ho, Jiaye Zou, Omar Alama, Sai Mitheran Jagadesh Kumar, Benjamin Chiang, Taneesh Gupta, Chen Wang, Nikhil Keetha, Katia Sycara, Sebastian Scherer