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 $\sim$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