u.s. department of the interior u.s. geological survey exploring new ground data sources gfsad30...
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U.S. Department of the InteriorU.S. Geological Survey
Exploring New Ground Data Sources
GFSAD30 April 2015 Meeting Justin Poehnelt, Student Developer
Topics
Mobile Application Image Classifier Digital Globe High Resolution Imagery Google Street View Imagery
Mobile Application
Release Updates Achieved Apple Store Compatibility Google Play Store In Progress
Issues Some issues with slow GPS capture on older devices. Need to work on versioning server application so that updates do
not break mobile application. Android does not ask for compass calibration on compass
initialization unlike IOS devices.
Mobile Application: Collect Data
Classifying Images
Created a functional prototype to classify any type of imagery through crowdsourcing. High Resolution Satellite Imagery Photos from Field Data
Mobile Application Photos Lucas Photos
Other Sources Google Street View Images
Prototype
Prototype Feedback
Class Definitions Size of Area to Focus
Change to a 3x3 grid
Note: Most imagery currently displayed is from Africa and of lower quality.
http://dev.croplands.org/classify
Digital Globe Enhanced View
Programmatic access to partial stack of images through web map tiles. No access to specific bands through this method. Tiles are served at 256 * 256 pixel size in epsg:3857 Zoom levels divide the layer into a 2^n by 2^n grid
where n = 0..18. Max zoom of 18 corresponds to 6.8719476736 x 10^10 tiles.
Can extract acquisition dates.
Images are quickly and quickly downloaded.
Google Street View
Goal: Get Google Street View images into application for classification.
Issues Where to request imagery? API’s automatically snap
to nearest image if available, but where is that? What is the acquisition date?
Google Street View
Goal: Get Google Street View images into application for classification.
Issues Where to request imagery? API’s automatically snap
to nearest image if available, but where is that? When is the acquisition date?
Solving the Where
Two Solutions Easy Way: Use Google Directions API to extract
polyline of Google’s road layer. Limited to 2500 requests per day. Incomplete data set.
Difficult Way: Extract data from Google Street View Coverage Map. Complete data set.
Solving the Where with Directions
1. Query Google Directions API with two Locations
2. With polyline, query Google Street View API with configurable spacing
3. If image exists, add location to database with location and heading of image.
4. Use different Google Street View API to extract acquisition date.
Solving the Where with Directions
Map showing image locations to arbitrary location east of Flagstaff, AZ.
Solving the Where with Directions
Issues Limited to where Google Directions creates route. Need algorithm for creating different transects on
scales such as county, state, country and continent.
Solving the Where with Coverage
http://gmaps-samples.googlecode.com/svn/trunk/streetview_landing/streetview-map.html
Solving the Where with Coverage
Issues Map may not be up-to-date There are 4,294,967,296 tiles to search for at zoom
level 16 where each pixel is approximately 2.38 meters at the equator.
Have already extracted nearly all of zoom level 15 tiles that are not empty.
More significant processing needed but no usage restrictions on obtaining locations.
End