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GENERAL INFORMATION
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Title of Dataset: Deep learning for coastal resource conservation: automating detection of shellfish reefs

Authors: Ridge, Justin T; Gray, Patrick C; Windle, Anna E; Johnston, David W

Funder: NCDEQ (2017-H-068)

Associated Publications: Ridge, Justin T; Gray, Patrick C; Windle, Anna E; Johnston, David W. (2019). Deep learning for coastal resource conservation: automating detection of shellfish reefs. Remote Sensing in Ecology and Conservation. In Press.

Contact: Patrick Gray (patrick.c.gray@duke.edu)

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DATA & FILE OVERVIEW
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This repository has the data needed to go along with the Remote Sensing in Ecology and Conservation paper "Deep learning for coastal resource conservation: automating detection of shellfish reefs." This work uses Mask R-CNN for detecting oyster reefs in aerial drone imagery.

All code, model weights, and instructions necessary to process data, analyze, and entirely reproduce this work is available at https://github.com/patrickcgray/oyster_net

The shapefiles containing all training, testing, and validation data are in shapefiles.zip.

The three processed datasets along with .json files necessary for neural network training and testing are available in the "1kx1k_dataset.zip", "2kx2k_dataset.zip", and "4kx4k_dataset.zip" with the number refering to the size of the tiles. 

The original UAS mosaics are also available and are called "180522BirdSEO_WGS84_transparent_mosaic_group1.tif", "180605CarrotEO_transparent_mosaic_group1.tif", and "middlemarshrtk22feb2017_transparent_mosaic_group1.tif".