Transfer learning in environmental remote sensing
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 …
increasing amounts of remote sensing data for environmental monitoring. Yet ML models …
Object detection and image segmentation with deep learning on Earth observation data: A review—Part II: Applications
In Earth observation (EO), large-scale land-surface dynamics are traditionally analyzed by
investigating aggregated classes. The increase in data with a very high spatial resolution …
investigating aggregated classes. The increase in data with a very high spatial resolution …
Fibre-optic sensor and deep learning-based structural health monitoring systems for civil structures: A review
UMN Jayawickrema, H Herath, NK Hettiarachchi… - Measurement, 2022 - Elsevier
Structural health monitoring (SHM) systems in civil engineering structures have been a
growing focus of research and practice. Over the last few decades, optical fibre sensor (OFS) …
growing focus of research and practice. Over the last few decades, optical fibre sensor (OFS) …
Scanning electron microscopy (SEM) image segmentation for microstructure analysis of concrete using U-net convolutional neural network
Scanning electron microscopy (SEM) images are used to evaluate the microstructure of the
concrete, there still remains challenges as the current methods are semi-automated, non …
concrete, there still remains challenges as the current methods are semi-automated, non …
UAV-based structural damage mapping: A review
Structural disaster damage detection and characterization is one of the oldest remote
sensing challenges, and the utility of virtually every type of active and passive sensor …
sensing challenges, and the utility of virtually every type of active and passive sensor …
A survey of change detection methods based on remote sensing images for multi-source and multi-objective scenarios
Y You, J Cao, W Zhou - Remote Sensing, 2020 - mdpi.com
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 …
exploring the urban change in the long term. However, diverse multi-source features and …
Deep learning for post‐hurricane aerial damage assessment of buildings
CS Cheng, AH Behzadan… - Computer‐Aided Civil …, 2021 - Wiley Online Library
This study aims to improve post‐disaster preliminary damage assessment (PDA) using
artificial intelligence (AI) and unmanned aerial vehicle (UAV) imagery. In particular, a …
artificial intelligence (AI) and unmanned aerial vehicle (UAV) imagery. In particular, a …
MultEYE: Monitoring system for real-time vehicle detection, tracking and speed estimation from UAV imagery on edge-computing platforms
We present MultEYE, a traffic monitoring system that can detect, track, and estimate the
velocity of vehicles in a sequence of aerial images. The presented solution has been …
velocity of vehicles in a sequence of aerial images. The presented solution has been …
Post‐disaster damage classification based on deep multi‐view image fusion
AB Khajwal, CS Cheng… - Computer‐Aided Civil …, 2023 - Wiley Online Library
This study aims to facilitate a more reliable automated postdisaster assessment of damaged
buildings based on the use of multiple view imagery. Toward this, a Multi‐View …
buildings based on the use of multiple view imagery. Toward this, a Multi‐View …
Bdanet: Multiscale convolutional neural network with cross-directional attention for building damage assessment from satellite images
Fast and effective responses are required when a natural disaster (eg, earthquake and
hurricane) strikes. Building damage assessment from satellite imagery is critical before relief …
hurricane) strikes. Building damage assessment from satellite imagery is critical before relief …