Large scale machine learning for geospatial problems in computational sustainability

Robinson, David Caleb

Title:

Large scale machine learning for geospatial problems in computational sustainability

dc.contributor.advisor	Dilkina, Bistra
dc.contributor.author	Robinson, David Caleb
dc.contributor.committeeMember	Chau, Duen Horng
dc.contributor.committeeMember	Zhang, Chao
dc.contributor.committeeMember	Moreno-Cruz, Juan
dc.contributor.committeeMember	Jojic, Nebojsa
dc.contributor.department	Computational Science and Engineering
dc.date.accessioned	2020-09-08T12:44:09Z
dc.date.available	2020-09-08T12:44:09Z
dc.date.created	2020-08
dc.date.issued	2020-05-14
dc.date.submitted	August 2020
dc.date.updated	2020-09-08T12:44:09Z
dc.description.abstract	The UN laid out 17 Sustainable Development Goals as part of the “The 2030 Agenda for Sustainable Development”. Each goal consists of broad targets - such as increasing the percentage of forested land (indicator 15.1.1) - for the world to work towards in order to achieve a more sustainable future. Part of achieving these goals involves determining how to actually measure the progress that is being made towards them. Measurements of progress are necessary in order to ensure accountability, determine where resources are most needed, and weigh the effectiveness of existing sustainability efforts. However, many of the targets/indicators - like “the percentage of forested land” - are prohibitively difficult to measure at global scales without algorithmic support. Tackling these, and other problems originating in pursuit of goals in sustainability, present unique challenges in machine learning. My dissertation addresses several such challenges, and explores how to enable large scale machine learning with geospatial data in a variety of application areas: (1) With remotely sensed imagery in land cover mapping and human population prediction. We use convolutional neural networks to tackle the land cover mapping problem, i.e. segmenting aerial imagery into different land cover classes (water, forests, fields, etc.). We develop: methods for improving the spatial generalization of land cover models, a human-in-the-loop active learning framework for fine-tuning such models to new areas, and a self-supervised training method for initializing such models when labeled data is scarce or unavailable. We use our models to generate the first 1m resolution land cover map covering the continental United States. We also predict human population density from satellite imagery using convolutional neural networks, then aggregate model features over administrative boundaries to learn zone level models for predicting population counts. (2) With human migration data. We develop a new loss function for training neural networks to predict human migrations between US counties. We further couple human migration and sea level rise models in a general framework for predicting population distributions under different future sea level rise scenarios. Our results highlight how sea level rise could have farther reaching indirect effects (through changing migration patterns) in addition to the direct effects along coastlines.
dc.description.degree	Ph.D.
dc.format.mimetype	application/pdf
dc.identifier.uri	http://hdl.handle.net/1853/63583
dc.language.iso	en_US
dc.publisher	Georgia Institute of Technology
dc.subject	Computational sustainability
dc.subject	Deep learning
dc.subject	Land cover mapping
dc.subject	Human population estimation
dc.subject	Machine learning
dc.title	Large scale machine learning for geospatial problems in computational sustainability
dc.type	Text
dc.type.genre	Dissertation
dspace.entity.type	Publication
local.contributor.corporatename	College of Computing
local.contributor.corporatename	School of Computational Science and Engineering
relation.isOrgUnitOfPublication	c8892b3c-8db6-4b7b-a33a-1b67f7db2021
relation.isOrgUnitOfPublication	01ab2ef1-c6da-49c9-be98-fbd1d840d2b1
thesis.degree.level	Doctoral