Change detection based on remote sensing (RS) data is an important method of detecting
changes on the Earth's surface and has a wide range of applications in urban planning …

Deep Learning techniques have been increasingly used in flood management to overcome
the limitations of accurate, yet slow, numerical models, and to improve the results of …

Climate change and rapid urbanisation exacerbated multiple urban issues threatening
urban sustainability. Numerous studies integrated machine learning and remote sensing to …

Abstract Machine learning (ML) has proven to be a powerful tool for utilizing the rapidly
increasing amounts of remote sensing data for environmental monitoring. Yet ML models …

Urban physical environments are the physical settings and built environments in
neighbourhoods and cities which provide places for human activities. Evidence suggests …

Identification of flood-prone sites in urban environments is necessary, but there is insufficient
hydraulic information and time series data on surface runoff. To date, several attempts have …

Quantities of multi-temporal remote sensing (RS) images create favorable conditions for
exploring the urban change in the long term. However, diverse multi-source features and …

Identification of flood water extent from satellite images has historically relied on either
synthetic aperture radar (SAR) or multi-spectral (MS) imagery. MS sensors are limited to …

Although deep learning has received extensive attention and achieved excellent
performance in various scenarios, it suffers from adversarial examples to some extent. In …

Due to a changing climate and increased urbanization, an escalation of urban flooding
occurrences and its aftereffects are ever more dire. Notably, the frequency of extreme storms …