The Massive Data Institute (MDI) invites you to take part in our Data Workshop series this spring. Please RSVP to any of the events that you are interested in attending.

Network Analysis in Python 2: Network Inferences
Date and Time: Friday, February 15 from 1:00pm - 3:00pm
Location: Healy 103, Healy Hall
RSVP Link:
Description: The Massive Data Institute (MDI) invites you to attend the second of our two-part Python programming workshop, which will focus on manipulating and analyzing data. Led by our Postdoc Fellow, Laila Wahedi, you will have the chance to heighten your skills with expert help along the way! Please be sure to bring your own laptops for the workshop. 

Mapping Infrastructure Categories from GIS Satellite imagery
Date and Time: Monday, April 15 from 10:00am - 12:00pm
Location: Arrupe Multipurpose Room, Arrupe Hall
RSVP Link: 
Description: With increasing urbanization in many developing countries, understanding the evolution and spatial characteristics of informal settlements will be critical for both poverty targeting efforts and the provision of public services.
This workshop will provide practical tools and instructions for mapping informal settlements with satellite imagery using the open source geographic information systems application QGIS. It will introduce attendees to vector and raster layers, ways to create and import vector shapefiles, and methods for accessing online satellite imagery by drawing on research on Indian government-designated categories of unplanned settlements that can be easily identified using remote sensing imagery. No prior experience in GIS mapping is required.

Classifying Infrastructures within Satellite Imagery with Machine Learning
Date and Time: Friday, April 16 from 10:00am - 12:00pm
Location: Arrupe Multipurpose Room, Arrupe Hall
RSVP Link:
Description: The explosion of satellite imagery and recent developments in machine learning create unprecedented opportunities to harness remote sensing for sustainable development and humanitarian efforts. This workshop will provide a practical introduction to machine learning for computer vision on remote sensing imagery using Python and the PyTorch deep learning framework. The workshop will begin with an introduction to remote sensing imagery, including data sources and different forms of resolution (e.g., spatial, temporal, radiometric, spectral). Given satellite imagery and map shapefiles with appropriately labeled polygons, the session will demonstrate how to preprocess the imagery, including rasterizing polygons, tiling rasters into image chips, and image resizing and augmentation. Next, the workshop will describe the machine learning workflow, with a focus on defining training and validation sets, classifying imagery with a neural network in PyTorch, and assessing accuracy of the classifier on the validation set. Python code will be provided in Jupyter notebooks for attendees to follow exercises and adapt for their own projects.


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