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rit 18 18/07/24

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Informatie rit 18 tdf, route etappe 18, profiel etappe 18, uitgebreid ritverloop 18e etappe tdf.

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High-Resolution Multispectral Dataset for Semantic Segmentation

rmkemker/RIT-18

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Description

This repository contains the RIT-18 dataset we built for the semantic segmentation of remote sensing imagery. It was collected with the Tetracam Micro-MCA6 multispectral imaging sensor flown on-board a DJI-1000 octocopter. The main contributions of this dataset include 1) very-high resolution multispectral imagery from a drone, 2) six-spectral VNIR bands, and 3) 18 object classes (plus background) with a severely unbalanced class distribution. Details about its construction can be found in our paper .

If you use this dataset in a publication, please cite:

This repository contains the following files:

• rit18_data_url: The URL to the current location of the data.
• evaluate_rit18.py: The evaluation script used to score the predicition map
• read_rit18.py: This script opens all of the data in the dataset.

The data, once downloaded, is ~3.0GB (1.58 GB compressed). It is a .mat file containing a dictionary of various elements including:

• 'train_data' : (7 x 9,393 x 5,642) numpy array containing the training ortho. The first six bands are the VNIR spectral bands and the 7th band is the mask of the orthomosaic.
• 'train_labels': (9,393 x 5,642) numpy array containing the training labels.
• 'val_data' : (7 x 8,833 x 6,918) numpy array containing the validation ortho. The first six bands are the VNIR spectral bands and the 7th band is the mask of the orthomosaic.
• 'val_labels' : (8,833 x 6,918) numpy array containing the validation labels.
• 'test_data' : (7 x 12,446 x 7,654) numpy array containing the testing ortho. The first six bands are the VNIR spectral bands and the 7th band is the mask of the orthomosaic.
• 'band_centers' : Spectral band centers
• 'band_center_units' : Units for 'band_centers'
• 'sensor' : Information about the sensor
• 'classes' : List of object classes
• 'info' : Various information about the dataset

Instructions

The dataset contain pixel-wise annotations for both the training and validation folds. Both sets of labels can be used to train a classifier. It is separated as a rough per-class split, but the validation fold does not contain the black and white wooden targets. This is because we want to evaluate our model's ability to perform low-shot learning.

The goal is to have the test labels available on the IEEE GRSS evaluation server . Until then, you can e-mail me your test predictions using the following format:

• Same spatial dimensions as the test image (12,446 x 7,654)
• uint8 datatype (smaller file)
• Either .mat (MATLAB) or .npy (Python) file format
• Compressed (so you don't kill my e-mail account)

I will use your predicitions on the evaluate_rit18.py script that I provided here and send you the output file. I will not score the area outside of the mask, but the background pixels ("class 0") will be scored. As soon as I get this up on the evaluation server, then the user will be able to do all of this themselves.

MATLAB Tutorial

Our dataset was recently featured in a MATLAB Deep Learning Tutorial called Semantic Segmentation of Multispectral Images Using Deep Learning .

Points of Contact

• Ronald Kemker - www.linkedin.com/in/ronald-kemker-66250b115
• Carl Salvaggio - https://www.cis.rit.edu/~cnspci/
• Chris Kanan - http://www.chriskanan.com/

Also Check Out

• EarthMapper Pipeline for Semantic Segmentation of Remote Sensing Imagery - https://github.com/rmkemker/EarthMapper
• Machine and Neuromorphic Perception Laboratory - http://klab.cis.rit.edu/
• New URL for RIT-18 Dataset - https://www.rit.edu/dirs/rit-18
• Python 100.0%

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The RIT-18 dataset was built for the semantic segmentation of remote sensing imagery. It was collected with the Tetracam Micro-MCA6 multispectral imaging sensor flown on-board a DJI-1000 octocopter.

The features this dataset include 1) very-high resolution multispectral imagery from a drone, 2) six-spectral VNIR bands, and 3) 18 object classes (plus background) with a severely unbalanced class distribution.

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Banff Mountain Film Festival World Tour Returns to RIT Feb. 18-20

Film collection looks to captivate the explorer within.

The world tour of the Banff Mountain Film Festival will be stopping at RIT Feb. 18-20.

Rochester-area filmgoers will journey to exotic locations, paddle the wildest waters and climb the highest peaks again this February. The Banff Mountain Film Festival World Tour returns to Rochester Institute of Technology for three nights of adventure.

This year’s tour, which showcases a selection of award-winning films and audience favorites spotlighting landscapes, cultures, action sports and exploration of mountain regions, will be shown at 7 p.m. Feb. 18 and 20 in the Student Alumni Union’s Ingle Auditorium, and Feb. 19 in Webb Auditorium in James E. Booth Hall. Films from the 36th annual Banff Mountain Film Festival will travel to 360 locations in 32 countries across the globe for the tour.

“Even after hosting the film festival for seven years now, my palms still get a little sweaty every time the music for the intro video begins,” says Tom Connelly, program director of RIT’s Interactive Adventures. “This year we are renting a top-of-the-line projector, with a larger screen and new sound systems. This is sure to be the best experience yet.”

Advance tickets can be purchased at RIT’s Gordon Field House and Activities Center box office, or call 585-475-4121 to charge by phone.

For more information on the local festival tour and a complete list of films, contact Tom Connelly at 585-475-2628 or [email protected].

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