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Spatiotemporal crowd flow prediction is one of the key technologies in smart cities. Currently, there are two major pain points that plague related research and practitioners. Firstly, crowd flow is related to multiple domain knowledge…
Effective shadow removal is pivotal in enhancing the visual quality of images in various applications, ranging from computer vision to digital photography. During the last decades physics and machine learning -based methodologies have been…
The use of unmanned aerial systems (UASs) has increased tremendously in the current decade. They have significantly advanced remote sensing with the capability to deploy and image the terrain as per required spatial, spectral, temporal, and…
Recent deep learning methods have achieved promising results in image shadow removal. However, most of the existing approaches focus on working locally within shadow and non-shadow regions, resulting in severe artifacts around the shadow…
Rapid globalization and the interdependence of humanity that engender tremendous in-flow of human migration towards the urban spaces. With advent of high definition satellite images, high resolution data, computational methods such as deep…
While designing sustainable and resilient urban built environment is increasingly promoted around the world, significant data gaps have made research on pressing sustainability issues challenging to carry out. Pavements are known to have…
Understanding the relationship between spatial structures of cities and environmental hazard exposures (such as urban heat) is essential for urban health and sustainability planning. However, a critical knowledge gap exists in terms of the…
Image shadow removal is a typical low-level vision task. Shadows cause local brightness shifts, which reduce the performance of downstream vision tasks. Currently, Transformer-based shadow removal methods suffer from quadratic computational…
Estimating the heightmaps of buildings and vegetation in single remotely sensed images is a challenging problem. Effective solutions to this problem can comprise the stepping stone for solving complex and demanding problems that require 3D…
Within the burgeoning expansion of deep learning and computer vision across the different fields of science, when it comes to urban development, deep learning and computer vision applications are still limited towards the notions of smart…
Shadow detection in general photos is a nontrivial problem, due to the complexity of the real world. Though recent shadow detectors have already achieved remarkable performance on various benchmark data, their performance is still limited…
Microclimate models are essential for linking climate to ecological processes, yet most physically based frameworks estimate temperature independently for each spatial unit and rely on simplified representations of lateral heat exchange. As…
Shadow removal is a task aimed at erasing regional shadows present in images and reinstating visually pleasing natural scenes with consistent illumination. While recent deep learning techniques have demonstrated impressive performance in…
Recently, deep learning has emerged as a promising tool for statistical downscaling, the set of methods for generating high-resolution climate fields from coarse low-resolution variables. Nevertheless, their ability to generalize to climate…
In rapidly urbanizing regions, designing climate-responsive urban forms is crucial for sustainable development, especially in dry arid-climates where urban morphology has a significant impact on energy consumption and environmental…
Urban safety and security play a crucial role in improving life quality of citizen and the sustainable development of urban. Traditional urban crime research focused on leveraging demographic data, which is insufficient to capture the…
Deep neural networks are often used to implement powerful generative models for real-world data. Notable applications include image denoising, as well as other classical inverse problems like compressed sensing and super-resolution. To…
The representation of nonlinear sub-grid processes, especially clouds, has been a major source of uncertainty in climate models for decades. Cloud-resolving models better represent many of these processes and can now be run globally but…
Shadows are essential for realistic image compositing. Physics-based shadow rendering methods require 3D geometries, which are not always available. Deep learning-based shadow synthesis methods learn a mapping from the light information to…
A number of industrial applications, such as smart grids, power plant operation, hybrid system management or energy trading, could benefit from improved short-term solar forecasting, addressing the intermittent energy production from solar…