Functional Map of the World - full - trainval v1.0.0
IARPA

@article{,
title= {Functional Map of the World - full - trainval v1.0.0},
keywords= {},
journal= {},
author= {IARPA},
year= {},
url= {https://crowdsourcing.topcoder.com/fmow},
license= {},
abstract= {Input Files

#### Satellite images

Satellite images are available in a variety of formats:

<image_id>_<T>_ms.tif is a 8-band multispectral TIFF image, where

<image_id> is the unique identifier of the scene,

<T> is an integer, representing time. Different T values for the same image_id mean snapshots of the same scene made at different points in time. (T values are not related to absolute time, their order may not correspond to temporal order.)

<image_id>_<T>_rgb.tif is 3-band pan-sharpened version of the above, in TIFF format.

<image_id>_<T>_msrgb.jpg corresponds to format #1 above, converted to a 3-band, JPEG-compressed RGB image.

<image_id>_<T>_rgb.jpg corresponds to format #2 above, converted to a 3-band, JPEG-compressed RGB image.

You may choose any of the above formats (or more of them) to work with, the scene content is the same but the number of spectral bands, the image resolution and the level of image compression are different.

#### Image metadata and ground truth bounding boxes

Metadata on each of the image files is available in JSON files, having the same file name but the .tif or .jpg extension is replaced by .json. The most important pieces of metadata are the following:


*gsd* : ground sample distance, the physical size of one image pixel, in meters.
utm, country code : the approximate geolocation of the object.

*timestamp* : the time when the image was taken, in UTC.

*bounding_boxes* : defines the category label, ID and location (in image space) of rectangles that you must use as annotated training data and as target for your predictions. The 'box' field within a bounding_box object contains 4 integers which define the x and y coordinates of the top left corner of the rectangle, and the width and height of the rectangle, in this order.


#### Differences between training and testing data

The file structure and naming conventions of training and testing data are different. Also the testing data has been altered in several ways to remove ground truth information and increase the difficulty of the challenge.
Training images contain only one bounding box. Testing images may contain more than one.

The <image_id> of a training image contain the category label assigned to the bounding box present in the image. The <image_id> of a testing image is a random numeric value.

The training dataset is organized into folders corresponding to category labels. The testing image file structure is one level shallower.

Category labels have been removed from the testing metadata.

In the testing dataset a small amount of uniform noise has been added to several metadata parameters to reduce their effective precision. The minutes and seconds fields of timestamps are set to random values in the range [0 - 59].

Additional bounding boxes have been added to several testing images. These bounding boxes include content that does not fall into any category in the challenge. If the bounding boxes were generated by some object proposal algorithm then these additional boxes would represent false detections.

Images and bounding boxes within images have been randomized and assigned numeric image_ids.

A note on training and validation data. The training dataset contains data in two folders: train and val. The content of these two folders are similar, they were created by randomly assigning the whole training dataset into two subsets. You can use both subsets as training data.},
superseded= {},
terms= {}
}