15,184 research outputs found
Mapping Informal Settlements in Developing Countries using Machine Learning and Low Resolution Multi-spectral Data
Informal settlements are home to the most socially and economically
vulnerable people on the planet. In order to deliver effective economic and
social aid, non-government organizations (NGOs), such as the United Nations
Children's Fund (UNICEF), require detailed maps of the locations of informal
settlements. However, data regarding informal and formal settlements is
primarily unavailable and if available is often incomplete. This is due, in
part, to the cost and complexity of gathering data on a large scale. To address
these challenges, we, in this work, provide three contributions. 1) A brand new
machine learning data-set, purposely developed for informal settlement
detection. 2) We show that it is possible to detect informal settlements using
freely available low-resolution (LR) data, in contrast to previous studies that
use very-high resolution (VHR) satellite and aerial imagery, something that is
cost-prohibitive for NGOs. 3) We demonstrate two effective classification
schemes on our curated data set, one that is cost-efficient for NGOs and
another that is cost-prohibitive for NGOs, but has additional utility. We
integrate these schemes into a semi-automated pipeline that converts either a
LR or VHR satellite image into a binary map that encodes the locations of
informal settlements.Comment: Published at the AAAI/ACM Conference on AI, ethics and society.
Extended results from our previous workshop: arXiv:1812.0081
Using Deep Learning and Google Street View to Estimate the Demographic Makeup of the US
The United States spends more than $1B each year on initiatives such as the
American Community Survey (ACS), a labor-intensive door-to-door study that
measures statistics relating to race, gender, education, occupation,
unemployment, and other demographic factors. Although a comprehensive source of
data, the lag between demographic changes and their appearance in the ACS can
exceed half a decade. As digital imagery becomes ubiquitous and machine vision
techniques improve, automated data analysis may provide a cheaper and faster
alternative. Here, we present a method that determines socioeconomic trends
from 50 million images of street scenes, gathered in 200 American cities by
Google Street View cars. Using deep learning-based computer vision techniques,
we determined the make, model, and year of all motor vehicles encountered in
particular neighborhoods. Data from this census of motor vehicles, which
enumerated 22M automobiles in total (8% of all automobiles in the US), was used
to accurately estimate income, race, education, and voting patterns, with
single-precinct resolution. (The average US precinct contains approximately
1000 people.) The resulting associations are surprisingly simple and powerful.
For instance, if the number of sedans encountered during a 15-minute drive
through a city is higher than the number of pickup trucks, the city is likely
to vote for a Democrat during the next Presidential election (88% chance);
otherwise, it is likely to vote Republican (82%). Our results suggest that
automated systems for monitoring demographic trends may effectively complement
labor-intensive approaches, with the potential to detect trends with fine
spatial resolution, in close to real time.Comment: 41 pages including supplementary material. Under review at PNA
Deep learning in remote sensing: a review
Standing at the paradigm shift towards data-intensive science, machine
learning techniques are becoming increasingly important. In particular, as a
major breakthrough in the field, deep learning has proven as an extremely
powerful tool in many fields. Shall we embrace deep learning as the key to all?
Or, should we resist a 'black-box' solution? There are controversial opinions
in the remote sensing community. In this article, we analyze the challenges of
using deep learning for remote sensing data analysis, review the recent
advances, and provide resources to make deep learning in remote sensing
ridiculously simple to start with. More importantly, we advocate remote sensing
scientists to bring their expertise into deep learning, and use it as an
implicit general model to tackle unprecedented large-scale influential
challenges, such as climate change and urbanization.Comment: Accepted for publication IEEE Geoscience and Remote Sensing Magazin
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