Remote Sensing

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Dear Colleagues,

Remote sensing is a vital data source for the monitoring of urban system dynamics such as urban growth, suburban sprawl, slum development, urban ecosystem services, land surface temperature, and damaged infrastructures due to extreme events. While our call for papers on monitoring urban systems using remotely sensed data will consider submissions from a broad range of related topics (listed below), this Special Issue particularly welcomes contributions that use AI methods for the exploration of remote sensing big data. Our aim is to provide a forum for the exchange of ideas and information about the uses of RS data and technology in understanding urban systems. The overarching goal of this Special Issue is, therefore, to generate new hypotheses and knowledge to build a robust problem-solving capacity for urban research.

Areas of interest include, but are not necessarily restricted to:

Big data and deep learning;
AI for image classification;
Google Earth Engine applications in urban studies;
Monitoring and predicting land use/cover change using remote sensing data;
Monitoring urban green and blue infrastructure using remote sensing data;
Unmanned aerial system (drone) applications in urban studies;
Thermal remote sensing applications in land surface temperature;
Remote sensing open data policies and infrastructure.

Dr. Ahmed Mustafa
Prof. Dr. Andreas Rienow
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

land use/cover change
urban systems
image processing
google earth engine
thermal remote sensing
open data
big data
artificial intelligence

Published Papers (1 paper)

Research

19 pages, 5555 KiB

Open AccessArticle

Improved Deep Learning-Based Vehicle Detection for Urban Applications Using Remote Sensing Imagery

by Mahmoud Ragab, Hesham A. Abdushkour, Adil O. Khadidos, Abdulrhman M. Alshareef, Khaled H. Alyoubi and Alaa O. Khadidos

Remote Sens. 2023, 15(19), 4747; https://doi.org/10.3390/rs15194747 - 28 Sep 2023

Cited by 5 | Viewed by 983

Abstract

Remote sensing (RS) data can be attained from different sources, such as drones, satellites, aerial platforms, or street-level cameras. Each source has its own characteristics, including the spectral bands, spatial resolution, and temporal coverage, which may affect the performance of the vehicle detection algorithm. Vehicle detection for urban applications using remote sensing imagery (RSI) is a difficult but significant task with many real-time applications. Due to its potential in different sectors, including traffic management, urban planning, environmental monitoring, and defense, the detection of vehicles from RS data, such as aerial or satellite imagery, has received greater emphasis. Machine learning (ML), especially deep learning (DL), has proven to be effective in vehicle detection tasks. A convolutional neural network (CNN) is widely utilized to detect vehicles and automatically learn features from the input images. This study develops the Improved Deep Learning-Based Vehicle Detection for Urban Applications using Remote Sensing Imagery (IDLVD-UARSI) technique. The major aim of the IDLVD-UARSI method emphasizes the recognition and classification of vehicle targets on RSI using a hyperparameter-tuned DL model. To achieve this, the IDLVD-UARSI algorithm utilizes an improved RefineDet model for the vehicle detection and classification process. Once the vehicles are detected, the classification process takes place using the convolutional autoencoder (CAE) model. Finally, a Quantum-Based Dwarf Mongoose Optimization (QDMO) algorithm is applied to ensure an optimal hyperparameter tuning process, demonstrating the novelty of the work. The simulation results of the IDLVD-UARSI technique are obtained on a benchmark vehicle database. The simulation values indicate that the IDLVD-UARSI technique outperforms the other recent DL models, with maximum accuracy of 97.89% and 98.69% on the VEDAI and ISPRS Potsdam databases, respectively. Full article

(This article belongs to the Special Issue Applications of AI and Remote Sensing in Urban Systems II)

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