Update README.md

README.md

@@ -26,4 +26,54 @@ dataset_info:
     num_examples: 504
   download_size: 521529
   dataset_size: 717630
+task_categories:
+- image-classification
+- image-segmentation
+language:
+- en
+tags:
+- Hyperspectral
+- Robotics
+- Robot
+- Autonomous
+- Multispectral
+pretty_name: 'Hyper-Drive: Hyperspectral Driving Dataset'
+size_categories:
+- n<1K
 ---
+Towards automated analysis of large environments, hyperspectral sensors must be adapted into a format where they can be operated from mobile robots. In this dataset, we highlight hyperspectral datacubes collected from the [Hyper-Drive ](https://river-lab.github.io/hyper_drive_data/) imaging system. Our system collects and registers datacubes spanning the visible to shortwave infrared (660-1700 nm) in 33 wavelength channels. The system also simultaneously captures the ambient solar spectrum reflected off a white reference tile. The dataset consists of 500 labeled datacubes from on-road and off-road terrain compliant with the [ATLAS](http://gvsets.ndia-mich.org/documents/AAIR/2022/ATLAS,%20an%20All-Terrain%20Labelset%20for%20Autonomous%20Systems.pdf).
+
+This work first appeared at [WHISPERS](https://www.ieee-whispers.com/) 2023 In Athens, Greece and is a product of the  [Robotics and Intelligent Vehicles Research Laboratory (RIVeR)](https://www.robot.neu.edu/) at [Northeastern University](https://www.northeastern.edu/).
+
+In addition to the 500 labeled hyperspectral datacubes, raw ROS bagfiles generated of each of the sensor feeds at a higher frame rate are available [here](https://river-lab.github.io/hyper_drive_data/Data_Set.html). These files are provided as an additional resource and do not contain semantic labels, but contain ~10,000 additional hyperspectral datacubes of in-between frames from the labeled dataset. It also contains additional datatypes for terrain analysis such as inertial measurement unit (IMU) data. To the best of the authors knowledge, it is the **largest vehicle-centric hyperspectral dataset** currently available!
+
+*This research was sponsored by the United States Army Core of Engineers (USACE) Engineer Research and Development Center (ERDC) Geospatial Research Laboratory (GRL) and was accomplished under Cooperative Agreement Federal Award Identification Number (FAIN) W9132V-22-2-0001. The views and conclusions contained in this document are those of the authors and should not be interpreted as representing the official policies, either expressed or implied, of USACE EDRC GRL or the U.S. Government. The U.S. Government is authorized to reproduce and distribute reprints for Government purposes notwithstanding any copyright notation herein.*
+---
+license: mit
+configs:
+- config_name: default
+  data_files:
+  - split: train
+    path: data/train-*
+dataset_info:
+  features:
+  - name: hsi
+    dtype: string
+  - name: rgb
+    dtype: string
+  - name: segmentation
+    dtype: string
+  - name: spectra
+    dtype:
+      array2_d:
+        shape:
+        - 1
+        - 305
+        dtype: float32
+  splits:
+  - name: train
+    num_bytes: 717630
+    num_examples: 504
+  download_size: 521529
+  dataset_size: 717630
+---