News

Nov 04
0

Introduction to Google Earth Engine – II

By |

Google Earth Engine (GEE) combines a multi-petabyte catalog of satellite imagery and geospatial datasets with planetary-scale analysis capabilities. The instant availability of data, massive compute power, and well-developed API make it a very convenient and powerful platform for geospatial analysis.

GEE provides native APIs in JavaScript and Python. However, recently the user community has developed a package “rgee (https://github.com/r-spatial/rgee)” that allows R users to interact with GEE (via reticulate and Python) and utilize its functionalities.

This workshop will focus on using R (the “rgee” package) to interface with GEE and utilize its power for ultra-fast geospatial analysis. You should attend the first workshop on November 18, if you are new to GEE.

Some familiarity with remote sensing and GIS, and exposure to raster and vector data analysis will be helpful.  You will need to register (free) at signup.earthengine.google.com with Google to use the Earth Engine. Please use your UM email account to register.

Nov 04
0

Introduction to Google Earth Engine – I

By |

Google Earth Engine (GEE) combines a multi-petabyte catalog of satellite imagery and geospatial datasets with planetary-scale analysis capabilities. The instant availability of data, massive compute power, and well-developed API make it a very convenient and powerful platform for geospatial analysis.

GEE provides native APIs in JavaScript and Python. However, recently the user community has developed a package “rgee (https://github.com/r-spatial/rgee)” that allows R users to interact with GEE (via reticulate and Python) and utilize its functionalities.

The two hands-on workshops will introduce GEE and show how to leverage its capacity for spatiotemporal analysis and visualization in R. The first workshop (November 18) is an introduction to GEE and we will primarily use JavaScript API to learn the basics of GEE. The second workshop (November 22) will focus on using R (the “rgee” package) to interface with GEE and utilize its power for ultra-fast geospatial analysis.

Some familiarity with remote sensing and GIS, and exposure to raster and vector data analysis will be helpful.  You will need to register (free) at signup.earthengine.google.com with Google to use the Earth Engine. Please use your UM email account to register.

Apr 30
0

Geospatial analysis with Google Earth Engine

By |

Google Earth Engine (GEE) combines a multi-petabyte catalog of satellite imagery and geospatial datasets with planetary-scale analysis capabilities. This workshop will provide an introduction to GEE. We will cover data models in GEE, basic vector and raster operations, and classification in both feature and image space.

You should be familiar with vector and raster data, GIS and remote sensing. We will use the web-based IDE for the Earth Engine JavaScript API. You will need to register (free) at signup.earthengine.google.com with Google to use the Earth Engine.

Jan 06
0

Image Processing Data Science Challenge

By |

Image Processing Data Science Challenge
Every Friday 1:00 pm to 5:00 pm  in Earl Lewis Room, Rackham Building
CSCAR is organizing a weekly group where participants can learn, sharpen, and
share their image processing, remote sensing, data analysis, and coding skills while
competing against professional and amateur data scientists. You should be familiar
with at least one of remote sensing, image processing, classification algorithms, and
programming. While working through the challenge, we will be using Python, R, and
Matlab. Final solutions have to be in Python or R since Kaggle only accepts solutions
in open source tools.
We are tackling image processing related challenges hosted at Kaggle (kaggle.com),
which is a leading platform to host data science challenges. It is currently running a
competition (https://www.kaggle.com/c/dstl-satellite-imagery-feature-detection)
where participants are asked to identify 10 different types of objects in very high
spatial resolution images collected by Worldview-3
(https://www.digitalglobe.com/) sensor. Some basic details of the data are as
follows.
• Wavebands
o Panchromatic: 450-800 nm
o 8 Multispectral: (red, red edge, coastal, blue, green, yellow, near-IR1 and
near-IR2) 400 nm – 1040 nm
o 8 SWIR: 1195 nm – 2365 nm
• Sensor Resolution (GSD) at Nadir :
o Panchromatic: 0.31m
o Multispectral: 1.24 m
o SWIR: Delivered at 7.5m
• Dynamic Range
o Panchromatic and multispectral : 11-bits per pixel
o SWIR : 14-bits per pixel
Depending on the interests in the group we will also include another image
processing challenge currently running at Kaggle sponsored by Nature Conservancy
– Fish Species Identification in digital camera images
(https://www.kaggle.com/c/the-nature-conservancy-fisheries-monitoring).
Send a mail to manishve@umich.edu if you want to participate or find out more
about it.

Sep 23
0

Geospatial analysis with Python

By |

This workshop will cover basic geospatial analysis in Python. Topics covered will include reading and writing various GIS file formats (shapefile, KML, geojson, csv), geocoding, common geometric operations like finding closest line to a point, point in polygon, spatial indexing and spatial joins etc. The workshop will focus solely on vector data (points, lines, polygons).  The will be mostly accomplished using the Python modules: fiona, shapely, rtree (but not arcpy).