7 research outputs found
Deep learning in urban analysis for health
The application of deep learning to urban health analysis is in its early stages, but offers new and promising capabilities in using large image-based datasets to better understand the built environment and its effects on human health. This chapter will introduce and explore some of these capabilities, providing the allied design fields with a roadmap of this emerging area of research, its potentials, and current challenges. The chapter begins with a brief overview of existing research related to urban morphology and health, in which precedent work using traditional methods as well as deep learning are introduced. Next, research is presented demonstrating methods for the use of discriminative and generative deep learning processes for both urban health estimation and analysis. The chapter then concludes with a discussion of key challenges and directions for future work in this emerging field of research
mHealth hyperspectral learning for instantaneous spatiospectral imaging of hemodynamics
Hyperspectral imaging acquires data in both the spatial and frequency domains
to offer abundant physical or biological information. However, conventional
hyperspectral imaging has intrinsic limitations of bulky instruments, slow data
acquisition rate, and spatiospectral tradeoff. Here we introduce hyperspectral
learning for snapshot hyperspectral imaging in which sampled hyperspectral data
in a small subarea are incorporated into a learning algorithm to recover the
hypercube. Hyperspectral learning exploits the idea that a photograph is more
than merely a picture and contains detailed spectral information. A small
sampling of hyperspectral data enables spectrally informed learning to recover
a hypercube from an RGB image. Hyperspectral learning is capable of recovering
full spectroscopic resolution in the hypercube, comparable to high spectral
resolutions of scientific spectrometers. Hyperspectral learning also enables
ultrafast dynamic imaging, leveraging ultraslow video recording in an
off-the-shelf smartphone, given that a video comprises a time series of
multiple RGB images. To demonstrate its versatility, an experimental model of
vascular development is used to extract hemodynamic parameters via statistical
and deep-learning approaches. Subsequently, the hemodynamics of peripheral
microcirculation is assessed at an ultrafast temporal resolution up to a
millisecond, using a conventional smartphone camera. This spectrally informed
learning method is analogous to compressed sensing; however, it further allows
for reliable hypercube recovery and key feature extractions with a transparent
learning algorithm. This learning-powered snapshot hyperspectral imaging method
yields high spectral and temporal resolutions and eliminates the spatiospectral
tradeoff, offering simple hardware requirements and potential applications of
various machine-learning techniques.Comment: This paper will appear in PNAS Nexu
A Review on Deep Learning in UAV Remote Sensing
Deep Neural Networks (DNNs) learn representation from data with an impressive
capability, and brought important breakthroughs for processing images,
time-series, natural language, audio, video, and many others. In the remote
sensing field, surveys and literature revisions specifically involving DNNs
algorithms' applications have been conducted in an attempt to summarize the
amount of information produced in its subfields. Recently, Unmanned Aerial
Vehicles (UAV) based applications have dominated aerial sensing research.
However, a literature revision that combines both "deep learning" and "UAV
remote sensing" thematics has not yet been conducted. The motivation for our
work was to present a comprehensive review of the fundamentals of Deep Learning
(DL) applied in UAV-based imagery. We focused mainly on describing
classification and regression techniques used in recent applications with
UAV-acquired data. For that, a total of 232 papers published in international
scientific journal databases was examined. We gathered the published material
and evaluated their characteristics regarding application, sensor, and
technique used. We relate how DL presents promising results and has the
potential for processing tasks associated with UAV-based image data. Lastly, we
project future perspectives, commentating on prominent DL paths to be explored
in the UAV remote sensing field. Our revision consists of a friendly-approach
to introduce, commentate, and summarize the state-of-the-art in UAV-based image
applications with DNNs algorithms in diverse subfields of remote sensing,
grouping it in the environmental, urban, and agricultural contexts.Comment: 38 pages, 10 figure