Columbus Large Image Format (CLIF) 2007 Dataset Overview

• Overview
• Airborne Sensor Hardware
• CLIF 2007 Data Products
  • Raw Image Data
  • NITF Data
  • Truth/Labels/Tracks
  • Position Data
• Suggested Challenge Problems
• Bulk Data Access - Amazon S3
  • Tools
  • s3cmd Example
  • Optimizing Data Download Costs
• Updates
• Acknowledgments
• Citation
• Copyright information
• Bibliography

Overview

The CLIF dataset contains several hours of imagery from a large format EO platform which was collected in October 2007 over the Ohio State University (OSU) campus. A busy city environment with many vehicles and humans in the scence was captured. Below is a figure from file "20071028142502-01000100-VIS.ntf.r1" with all six cameras stitched then north oriented. At full resolution each frame is approximately 66 mega pixels.

Airborne Sensor Hardware

The sensor is a large format monochromatic electro-optical sensor comprised of a matrix of six cameras. The matrix is 2 rows by 3 columns. Cameras 3, 1, and 5 make-up the top row of the image, respectively. Cameras 2, 0, and 4 make-up the bottom row of the image. Each camera was oriented in such way as to maximize coverage, yet allow enough overlap between images to help in mosaicking the image to form a larger image. Overlap between cameras is apprixmatley 50 pixels. The cameras, for this data collection, collect data at approximately 2 frames per second.

To better understand the camera orientation imagine that you are looking through the CLIF camera head, you would see what is shown in the figure below. In order to optimize performance of the CLIF camera system the data was stored inverted so each image has to be rotated. The images on the top row are rotated counter clockwise 90 degrees while the images on the bottom row are rotated clockwise 90 degrees. Camera numbers have been in embeded in the figure below for illustrative purposes only, the camera numbers are not stored in the imagery.

Each camera was made by Illunis employing a Kodak KAI-11002 charge coupled device (CCD). The optics are Canon 135 MM EF mount lens controlled with a Birger adapter. Unfortunately both the CCD and camera have been discontinued. On the rear of the camera head was a Novatel DL4 Plus SPAN with a Honeywell HG1700_AG11 inertial measurement unit (IMU). The entire integrated system is shown below.

Orientation of the IMU is an important consideration when performing image projections with the RAW data. The IMU orientations are depicted in the figure below.

CLIF 2007 Data Products

Four data products are available with the CLIF data set raw, NITF, truth/labels, and position data. Raw or unprocessed data is available for the CLIF 2007 data set and is described in the sections below. Raw data requires a high level of expertise in remote sensing to be able to process in a meaningful way. For some research like super resolution and 3D reconstruction, raw data is required. In addition we have provided processed data in the DoD's NITF file standard which is described below. NITF data is stitched, orthorectified (north oriented), and scaled for display to the user. NITF data could be used in conjuction with the truth/labels for interesting image classification and tracking problems.

Raw Image Data

The CLIF 2007 data set includes "raw" data which is image data that is unprocessed for each of the six cameras. Each raw image is 4,016 pixels by 2,672 pixels, in 8-bits per pixel grayscale raw format with an approximate size of 11 megabytes for each camera.

Raw File Naming Convention

The first six characters in the file name denote the camera number while the last eight characters represent the flight number (first two digits) and the frame number (last six digits). Both the camera number and the frame number are padded with zeros at the front to insure the file names are automatically sorted correctly when listing a directory. A example file name is 000003-01001000.raw, where 000003 is camera number three, 01 is flight number one, and 001000 is frame number one thousand.

Raw Image Storage

The raw images are stored in standard "tarballs" in Amazon S3 requester pay buckets at s3://sdms-clif-2007/raw_sensor_data with the following file name convention Camera_X_00000Y.tar where X is the camera number and y is the tarball file index. See the bulk data access section at the end of mark down file for instructions on how to download the raw imagery.

NITF Data

National Image Transmission Format (NITF) is the DoD standard for imagery. Bascially a NITF file is a large header with the data concatenated onto the rear of the file. The data or image is stored in blocks of jpeg 6.2 compressed images of 128 x 128 size. The easiest way to read NITF data is to use Geospatial Data Abstraction Layer (GDAL). Example Python code is provided code/NITFPythonGDAL.py that will read the NITF file then plot the image in a matplotlib plot.

NITF File Naming Convention

The NITF files are named as "YYYYMMDDHHMMSS-FNXXXXXX-VIS.ntf.r0", where FN is the flight number and XXXXXX is the frame number, an example file name is "20071028142730-01000324-VIS.ntf.r0". On close inspection of the NITF data you will notice that each of the NITF files have six correpsonding files each with r0, r1, r2, r3, r4, and r5. Those files represent the resolution set also known as the r set. The highest resolution is r0 while the lowest resolution is r5.

NITF Storage

The NITF files are stored in standard "tarballs" in Amazon S3 requester pay buckets at s3://sdms-clif-2007/NITF with the file names that start with NITFS_14_*.tar. See the bulk data access section at the end of mark down file for instructions on how to download the NITF imagery.

Truth/Labels/Tracks

A variety of objects in the scene were tracked and truthed (or labeled) by humans. These labels are provided in the data set and stored in s3://sdms-clif-2007/20071028_CLIF_Truth.tar.gz. The truth data was generated with AFRL Sensor Directorate's WAMITT tool and is provided in a MySQL dump text file. The relational database structure is shown in WAMITT relational DB design. Truth or labels were derived from the NITF files.

MySQL Tracks to sqlite3

Sqlite3 is easier to work with for some users because sqlite3 does not require a server install of the MySQL database. To convert from MySQL dump sql file format to sqlite3 see the script on github. The labels in sqlite3 database format are stored s3://sdms-clif-2007/20071028_CLIF_truth_sqlite3.zip. An example command to convert the WAMITT tracks is:

./mysql2sqlite ~/temp/CLIF_Truth/20071028_CLIF_Truth/100-999/wamitt_mysql.sql | sqlite3 ~/temp/CLIF_Truth/sqlite3/wamitt100-999.db
./mysql2sqlite ~/temp/CLIF_Truth/20071028_CLIF_Truth/1000-1900/wamitt_mysql.sql | sqlite3 ~/temp/CLIF_Truth/sqlite3/wamitt1000-1900.db
./mysql2sqlite ~/temp/CLIF_Truth/20071028_CLIF_Truth/2400-5140/wamitt_mysql.sql | sqlite3 ~/temp/CLIF_Truth/sqlite3/wamitt2400-5140.db
./mysql2sqlite ~/temp/CLIF_Truth/20071028_CLIF_Truth/10000-11800/wamitt_mysql.sql | sqlite3 ~/temp/CLIF_Truth/sqlite3/wamitt10000-11800.db

CSV file of the labels

In addition to the MySQL data dump file we have taken the liberty to convert the file into a single comma separated value (CSV) file to ease processing of the truth file. The CSV file is stored here s3://sdms-clif-2007/2007_CLIF_truth_csv.zip. The script code/convert_sqlite3_truth_csv.py was used to convert the SQLite3 database to CSV. It is an easily modifiable script in case your research requires additional fields. The CSV file includes the field names on the first line of the file. A snippet of the CSV file is included below:

track_point.fileId, track_point.time, track_point.frame, track.id, track_point.id, target_type.name, color.color, track.length, track.width, track_point.x, track_point.y, track_point.latitude, track_point.longitude
NITFVIS2007102814250201000100,1193581502783,100,54955,50539097,pickup truck,gray,4.68,1.59,6755,5513,40.0042058848044,-83.0157377751412
NITFVIS2007102814250301000101,1193581503413,101,54955,50539098,pickup truck,gray,4.68,1.59,6802,5538,40.0042089249342,-83.0158241043238
NITFVIS2007102814250401000102,1193581504083,102,54955,50539099,pickup truck,gray,4.68,1.59,6867,5580,40.004212158088,-83.0159159147244

Truth/Label Statistics

The Python code code/truth_stats.py was used to generate the statistics for the truth/label data below.

• Total count of images with at least one truth object: 6,343
• Total count of objects truthed: 3,502,401
  • The count of each object type
    • SUV: 287,348
    • SUV w/trailer: 965
    • animal: 666
    • bag: 385
    • bicycle: 7,749
    • boat: 25
    • box truck: 10,330
    • bus: 13,568
    • dismount: 901,811
    • dump truck: 466
    • flatbed truck: 11,907
    • flatbed truck w/trailer: 681
    • motorcycle: 2,463
    • other: 38,372
    • pickup truck: 113,836
    • pickup truck w/trailer: 5,977
    • plane: 3,548
    • sedan: 2,008,068
    • sedan w/trailer: 1,551
    • semi: 6,155
    • semi w/trailer: 4,876
    • trashcan: 56
    • utility pole: 33
    • van: 81,331
    • van w/trailer: 234

Position Data

The CLIF camera head was controlled with a dedicated circuit from camera 0, aka the master camera. First the computer would send a command to camera 0 to take an image, next camera 0 would calculate the automatic exposure values and send those as a pulse over the dedicated circuit to the other five cameras and the Novatel. The Novatel would then compute the INSPVA message which was converted from binary to ascii stored as a comma separated value text file with the fields described below as a file with a .txt extension.

Yaw, pitch, and roll are shown in the IMU orientation graph above. These variables are not written in binary, instead the values of each variable are what is stored in the file. To separate the variables you can use the C scanf function like the following, note the variable types:

sscanf(0, "%lf,%lf,%lf,%lf,%lf,%lf,%lf,%d,%lf,%lf,%lf,%d,%d,%d", &yaw, &pitch,
  &roll, &latitude, &longitude, &altitude, &gps_seconds, &gps_week,
  &north_velocity, &east_velocity, &up_velocity, &imu_status,
  &local_time_zone_adjustment, &day_light_savings_flag );

Name	Type	Notes
yaw	double	0 to 359.9 in the units of degrees, see the graphs above to understand the rotation and placement of IMU. Often referred to as azimuth. Around the Z axis.
pitch	double	-180 to +180 in the units of degrees, see the graphs above to understand the rotation and placement of IMU. Around the X axis.
roll	double	-180 to +180 in the units of degrees, see the graphs above to understand the rotation and placement of IMU. Around the Y axis.
latitude	double	decimal degrees (WGS84)
longitude	double	decimal degrees (WGS84)
altitude	double	Altitude from the IMU, ellipsoidal height (WGS84) in feet
gps_seconds	double	Number of seconds since the last week change. Referenced to UTC. Range is between 0 to 604,799
gps_week	integer	Computed as the full week number starting from week 0 or January 6, 1980. Referenced to UTC.
north_velocity	double	Velocity in a northerly direction (a negative value implies a southerly direction) meters per second
east_velocity	double	Velocity in a easterly direction (a negative value implies a westerly direction) meters per second
up_velocity	double	Velocity in an up direction meters per second
imu_status	integer	Status of the IMU. 0 - GPS Locked, 1 - Insufficient Observations, 2 - No Convergence, 3 - Singularity, 4 - Covariance Trace, 14 - INS Inactive, 15 - INS Aligning, 16 - INS Bad, 17 - IMU Unplugged
local_time_zone_adjustment	integer	Time offset between GMT and local time. To determine the local time of the frame, simply add the local_time_zone_adjustment offset to the time value calculated with gps_seconds and gps_week
day_light_savings_flag	integer	Indicates if daylight savings time was in effect. 0 - No or unknown, 1 - Yes

C code is available from code/convert_gps_time.c to convert the GPS Week and GPS time into year, month, day_of_month, hour, minute, and second.

Suggested Challenge Problems

The AFRL/Sensors Directorate is interested in novel research using this dataset, especially novel approaches to:

• MOSAICKING-Mosaicking (stitching) of the six cameras using both computer vision and photogrammetric approaches
• VIDEO REGISTRATION-Registering and stabilizing video
• GEOREGISTRATION-providing UTM coordinates for every pixel
• GIS FUSION-fusing the data with GIS information
• LAYERED REGISTRATION-Registering (Data Fusion Level 0) of the aerial and building sensor data
• TRACKING-Tracking vehicles
• ATR-Performing Automatic Target Recognition (ATR) on objects of interest

Bulk Data Access - Amazon S3

The CLIF 2007 data is available from Amazon Simple Storage Service (S3) in Requester Pays buckets (i.e., you are charged by Amazon to access, browse, and download this data). Please see Requester Pays Buckets in the Amazon S3 Guide for more information on this service. Your use of Amazon S3 is subject to Amazon's Terms of Use. The accessibility of SDMS data from Amazon S3 is provided "as is" without warranty of any kind, expressed or implied, including, but not limited to, the implied warranties of merchantability and fitness for a particular use. Please do not contact SDMS for assistance with Amazon services, you can post a GitHub issue and the authors will try and assist.

The name of the S3 bucket is sdms-clif-2007 (s3://sdms-clif-2007).

Tools

We do not track development of tools interacting with Amazon S3, nor endorse any particular tool. However, in development of this facility we found the Python package s3cmd to be useful on Mac OS X and Linux. For Windows the AWS team provided the following suggestions s3browser.com and cross ftp.

s3cmd Example

The examples shown below were developed and tested with Linux and Mac OS X using the Python distribution of Enthought Canopy. First you must establish a AWS account and download your AWS_ACCESS_KEY_ID and AWS_SECRET_ACCESS_KEY. Next use the following commands to export your key id and secret access key to the shell environment replacing the values of AWS_ACCESS_KEY_ID and AWS_SECRET_ACCESS_KEY with the values from your newly established AWS account:

export AWS_ACCESS_KEY_ID="JSHSHEUESKSK65"
export AWS_SECRET_ACCESS_KEY="kskjskjsAEERERERlslkhdjhhrhkjdASKJSKJS666789"

Install s3cmd with pip:

pip install s3cmd

To list the directory of the sdms-clif-2007 bucket use the following command:

s3cmd --requester-pays ls s3://sdms-clif-2007/

Please note all the CLIF 2007 data products are in a single directory.

To determine the amount of disk space in the sdms-clif-2007 bucket use the command:

s3cmd --requester-pays --recursive --human du s3://sdms-clif-2007/

To retrieve a file:

s3cmd --requester-pays get s3://sdms-clif-2007/position_text.tar.gz

We do not recommend that you use s3cmd sync capability because the number of files and size of the MAMI data set is large and errors will result.

Optimizing Data Download Costs

The CLIF 2007 data set is large at approximatley 5 TB, to download the entire data set it would cost $450. This assumes AWS download fees are $0.09 a gigabyte. Most researchers will not require the entire CLIF 2007 data set, carefully consider the data that you require to meet your research needs and download costs. We have divided the dataset into small tarballs to make the download easier and to make it possible for a researcher to select the data they need. For instance raw images can be downloaded by camera number and NITF files can be downloaded by flight number. Much smaller index files have been provided that lists the file name and size of each file contained in the tarballs. We attempted to maintain a careful balance between tarball size and number of tarball files. Obviously the smaller the tarball file the more tarball files that would have to be created.

Updates

Please feel free to create a pull request or new issue if you write a paper that uses this data set so we can update the bibliography. In addition we will gladly accept pull requests and new issues for corrections or bug fixes.

Acknowledgments

Todd Rovito and Olga Mendoza-Schrock were supported under an AFOSR grant in Dynamic Data Driven Application Systems.

Citation

When reporting results that use the CLIF 2007 dataset, please cite:

Todd Rovito¹, James Patrick¹, Steve Walls², Daniel Uppenkamp¹, Olga Mendoza-Schrock¹, Vince Velten¹, Chris Curtis¹, and Kevin Priddy¹. Columbus Large Image Format (CLIF) 2007 Dataset. https://github.com/AFRL-RY/data-clif-2007, 2018.

1. AFRL/Sensors Directorate/RYA 2241 Avionics Circle, Wright-Patterson AFB, OH 45433
2. Matrix Research Inc 1300 Research Park Dr, Beavercreek, OH 45432

Copyright information

This page and data set is in the public domain under 17 U.S.C. 105.

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