8 research outputs found
Spatiotemporal Fusion in Remote Sensing
Remote sensing images and techniques are powerful tools to investigate earth’s surface. Data quality is the key to enhance remote sensing applications and obtaining clear and noise-free set of data is very difficult in most situations due to the varying acquisition (e.g., atmosphere and season), sensor and platform (e.g., satellite angles and sensor characteristics) conditions. With the increasing development of satellites, nowadays Terabytes of remote sensing images can be acquired every day. Therefore, information and data fusion can be particularly important in the remote sensing community. The fusion integrates data from various sources acquired asynchronously for information extraction, analysis, and quality improvement. In this chapter, we aim to discuss the theory of spatiotemporal fusion by investigating previous works, in addition to describing the basic concepts and some of its applications by summarizing our prior and ongoing works
Fine-tuning deep learning models for stereo matching using results from semi-global matching
Deep learning (DL) methods are widely investigated for stereo image matching
tasks due to their reported high accuracies. However, their
transferability/generalization capabilities are limited by the instances seen
in the training data. With satellite images covering large-scale areas with
variances in locations, content, land covers, and spatial patterns, we expect
their performances to be impacted. Increasing the number and diversity of
training data is always an option, but with the ground-truth disparity being
limited in remote sensing due to its high cost, it is almost impossible to
obtain the ground-truth for all locations. Knowing that classical stereo
matching methods such as Census-based semi-global-matching (SGM) are widely
adopted to process different types of stereo data, we therefore, propose a
finetuning method that takes advantage of disparity maps derived from SGM on
target stereo data. Our proposed method adopts a simple scheme that uses the
energy map derived from the SGM algorithm to select high confidence disparity
measurements, at the same utilizing the images to limit these selected
disparity measurements on texture-rich regions. Our approach aims to
investigate the possibility of improving the transferability of current DL
methods to unseen target data without having their ground truth as a
requirement. To perform a comprehensive study, we select 20 study-sites around
the world to cover a variety of complexities and densities. We choose
well-established DL methods like geometric and context network (GCNet), pyramid
stereo matching network (PSMNet), and LEAStereo for evaluation. Our results
indicate an improvement in the transferability of the DL methods across
different regions visually and numerically.Comment: 6 figure
A Review of Mobile Mapping Systems: From Sensors to Applications
The evolution of mobile mapping systems (MMSs) has gained more attention in
the past few decades. MMSs have been widely used to provide valuable assets in
different applications. This has been facilitated by the wide availability of
low-cost sensors, the advances in computational resources, the maturity of the
mapping algorithms, and the need for accurate and on-demand geographic
information system (GIS) data and digital maps. Many MMSs combine hybrid
sensors to provide a more informative, robust, and stable solution by
complementing each other. In this paper, we present a comprehensive review of
the modern MMSs by focusing on 1) the types of sensors and platforms, where we
discuss their capabilities, limitations, and also provide a comprehensive
overview of recent MMS technologies available in the market, 2) highlighting
the general workflow to process any MMS data, 3) identifying the different use
cases of mobile mapping technology by reviewing some of the common
applications, and 4) presenting a discussion on the benefits, challenges, and
share our views on the potential research directions.Comment: 5 table
Spatial Temporal Analysis of Traffic Patterns during the COVID-19 Epidemic by Vehicle Detection Using Planet Remote-Sensing Satellite Images
The spread of the COVID-19 since the end of 2019 has reached an epidemic level and has quickly become a global public health crisis. During this period, the responses for COVID-19 were highly diverse and decentralized across countries and regions. Understanding the dynamics of human mobility change at high spatial temporal resolution is critical for assessing the impacts of non-pharmaceutical interventions (such as stay-at-home orders, regional lockdowns and travel restrictions) during the pandemic. However, this requires collecting traffic data at scale, which is time-consuming, cost-prohibitive and often not available (e.g., in underdeveloped countries). Therefore, spatiotemporal analysis through processing periodical remote-sensing images is very beneficial to enable efficient monitoring at the global scale. In this paper, we present a novel study that utilizes high temporal Planet multispectral images (from November 2019 to September 2020, on average 7.1 days of frequency) to detect traffic density in multiple cities through a proposed morphology-based vehicle detection method and evaluate how the traffic data collected in such a manner reflect mobility pattern changes in response to COVID-19. Our experiments at city-scale detection, demonstrate that our proposed vehicle detection method over this 3 m resolution data is able to achieve a detection level at an accuracy of 68.26% in most of the images, and the observations’ trends coincide with existing public data of where available (lockdown duration, traffic volume, etc.), further suggesting that such high temporal Planet data with global coverage (although not with the best resolution), with well-devised detection algorithms, can sufficiently provide traffic details for trend analysis to better facilitate informed decision making for extreme events at the global level
Spatial Temporal Analysis of Traffic Patterns during the COVID-19 Epidemic by Vehicle Detection Using Planet Remote-Sensing Satellite Images
The spread of the COVID-19 since the end of 2019 has reached an epidemic level and has quickly become a global public health crisis. During this period, the responses for COVID-19 were highly diverse and decentralized across countries and regions. Understanding the dynamics of human mobility change at high spatial temporal resolution is critical for assessing the impacts of non-pharmaceutical interventions (such as stay-at-home orders, regional lockdowns and travel restrictions) during the pandemic. However, this requires collecting traffic data at scale, which is time-consuming, cost-prohibitive and often not available (e.g., in underdeveloped countries). Therefore, spatiotemporal analysis through processing periodical remote-sensing images is very beneficial to enable efficient monitoring at the global scale. In this paper, we present a novel study that utilizes high temporal Planet multispectral images (from November 2019 to September 2020, on average 7.1 days of frequency) to detect traffic density in multiple cities through a proposed morphology-based vehicle detection method and evaluate how the traffic data collected in such a manner reflect mobility pattern changes in response to COVID-19. Our experiments at city-scale detection, demonstrate that our proposed vehicle detection method over this 3 m resolution data is able to achieve a detection level at an accuracy of 68.26% in most of the images, and the observations’ trends coincide with existing public data of where available (lockdown duration, traffic volume, etc.), further suggesting that such high temporal Planet data with global coverage (although not with the best resolution), with well-devised detection algorithms, can sufficiently provide traffic details for trend analysis to better facilitate informed decision making for extreme events at the global level