175 research outputs found
Treepedia 2.0: Applying Deep Learning for Large-scale Quantification of Urban Tree Cover
Recent advances in deep learning have made it possible to quantify urban
metrics at fine resolution, and over large extents using street-level images.
Here, we focus on measuring urban tree cover using Google Street View (GSV)
images. First, we provide a small-scale labelled validation dataset and propose
standard metrics to compare the performance of automated estimations of street
tree cover using GSV. We apply state-of-the-art deep learning models, and
compare their performance to a previously established benchmark of an
unsupervised method. Our training procedure for deep learning models is novel;
we utilize the abundance of openly available and similarly labelled
street-level image datasets to pre-train our model. We then perform additional
training on a small training dataset consisting of GSV images. We find that
deep learning models significantly outperform the unsupervised benchmark
method. Our semantic segmentation model increased mean intersection-over-union
(IoU) from 44.10% to 60.42% relative to the unsupervised method and our
end-to-end model decreased Mean Absolute Error from 10.04% to 4.67%. We also
employ a recently developed method called gradient-weighted class activation
map (Grad-CAM) to interpret the features learned by the end-to-end model. This
technique confirms that the end-to-end model has accurately learned to identify
tree cover area as key features for predicting percentage tree cover. Our paper
provides an example of applying advanced deep learning techniques on a
large-scale, geo-tagged and image-based dataset to efficiently estimate
important urban metrics. The results demonstrate that deep learning models are
highly accurate, can be interpretable, and can also be efficient in terms of
data-labelling effort and computational resources.Comment: Accepted and will appear in IEEE BigData Congress 2018 Conference
Proceeding
A Glimpse at Quasar Host Galaxy Far-UV Emission, Using DLAs as Natural Coronagraphs
In merger-driven models of massive galaxy evolution, the luminous quasar
phase is expected to be accompanied by vigorous star formation in quasar host
galaxies. In this paper, we use high column density Damped Lyman Alpha (DLA)
systems along quasar sight lines as natural coronagraphs to directly study the
far-UV (FUV) radiation from the host galaxies of luminous background quasars.
We have stacked the spectra of 2,000 DLA systems (N_HI>10^{20.6} cm^{-2})
with a median absorption redshift = 2.6 selected from quasars observed in
the SDSS-III Baryon Oscillation Spectroscopic Survey. We detect residual flux
in the dark troughs of the composite DLA spectra. The level of this residual
flux significantly exceeds systematic errors in the SDSS fiber sky subtraction;
furthermore, the residual flux is strongly correlated with the continuum
luminosity of the background quasar, while uncorrelated with DLA column density
or metallicity. We conclude that the flux could be associated with the average
FUV radiation from the background quasar host galaxies (with medium redshift <
z > = 3.1) that is not blocked by the intervening DLA. Assuming all of the
detected flux originates from quasar hosts, for the highest quasar luminosity
bin (= 2.5x 10^{13} L_sun), the host galaxy has a FUV intensity of 1.5 +/-
0.2 x 10^{40} erg s^{-1} A^{-1}; this corresponds to an unobscured UV star
formation rate of 9 M_sun/yr.Comment: 15 pages, 10 figures, Accepted for publication in Ap
Comparison of Quarterly and Yearly Calibration Data for Propensity Score Adjusted Web Survey Estimates
While web surveys have become increasingly popular as a method of data collection, there is
concern that estimates obtained from web surveys may not reflect the target population of interest.
Web survey estimates can be calibrated to existing national surveys using a propensity score
adjustment, although requirements for the size and collection timeline of the reference data set
have not been investigated. We evaluate health outcomes estimates from the National Center for
Health Statistics’ Research and Development web survey. In our study, the 2016 National Health
Interview Survey as well as its quarterly subsets are considered as reference datasets for the web
data. It is demonstrated that the calibrated health estimates overall vary little when using the
quarterly or yearly data, suggesting that there is flexibility in selecting the reference dataset. This
finding has many practical implications for constructing reference data, including the reduced cost
and burden of a smaller sample size and a more flexible timeline
Interactive Text Generation
Users interact with text, image, code, or other editors on a daily basis.
However, machine learning models are rarely trained in the settings that
reflect the interactivity between users and their editor. This is
understandable as training AI models with real users is not only slow and
costly, but what these models learn may be specific to user interface design
choices. Unfortunately, this means most of the research on text, code, and
image generation has focused on non-interactive settings, whereby the model is
expected to get everything right without accounting for any input from a user
who may be willing to help.
We introduce a new Interactive Text Generation task that allows training
generation models interactively without the costs of involving real users, by
using user simulators that provide edits that guide the model towards a given
target text. We train our interactive models using Imitation Learning, and our
experiments against competitive non-interactive generation models show that
models trained interactively are superior to their non-interactive
counterparts, even when all models are given the same budget of user inputs or
edits.Comment: EMNLP 202
A glimpse at quasar host galaxy Far-UV emission using damped Lyα's as natural coronagraphs
In merger-driven models of massive galaxy evolution, the luminous quasar phase is expected to be accompanied by vigorous star formation in quasar host galaxies. In this paper, we use high column density damped Lyα (DLA) systems along quasar sight lines
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