Image recognition datasets for part 4 of ML Mondays
https://dbuscombe-usgs.github.io/MLMONDAYS/
https://github.com/dbuscombe-usgs/MLMONDAYS
TAMUCC
Publicly available imagery of coastal environments in Texas, provided by the Harte Research Institute at TAMUCC (Texas A&M University - Corpus Christi), funded by the Texas General Land Office (GLO).
Images such as the example above are oblique, taken from a low-altitude aircraft with approximate positions. In total, there are over 10,000 images covering the whole Texas coastline, each categorized by one of the following dominant classes ("developed" classes are ***):
* ***Exposed walls and other structures made of concrete, wood, or metal***
* Scarps and steep slopes in clay
* Wave-cut clay platforms
* Fine-grained sand beaches
* Scarps and steep slopes in sand
* Coarse-grained sand beaches
* Mixed sand and gravel (shell) beaches
* Gravel (shell) beaches
* ***Exposed riprap structures***
* Exposed tidal flats
* ***Sheltered solid man-made structures, such as bulkheads and docks***
* ***Sheltered riprap structures***
* Sheltered scarps
* Sheltered tidal flats
* Salt- and brackish-water marshes
* Fresh-water marshes (herbaceous vegetation)
* Fresh-water swamps (woody vegetation)
* Mangroves
The dataset is described further here. The dataset will be provided during the course, and is also available on Google Cloud Storage bucket gs://aju-demos-coastline-images/coastline
Acknowledgements
This dataset is courtesy of Texas A&M University (See https://storage.googleapis.com/tamucc_coastline/GooglePermissionForImages_20170119.pdf for details). Philippe Tissot, Associate Director, Conrad Blucher Institute for Surveying and Science, Texas A&M University - Corpus Christi; James Gibeaut, Endowed Chair for Coastal and Marine Geospatial Sciences, Harte Research Institute, Texas A&M University - Corpus Christi; and Valliappa Lakshmanan, Tech Lead, Google Big Data and Machine Learning Professional Services
Files
tamucc_subset_12class.zip