Recently, urban heat islands (UHIs) have emerged as a significant challenge for humanity due to the effects of urbanization and fast industrial growth. The primary factors contributing to UHI involve the substantial heat emitted by urban structures and human-made heat sources. These heat sources lead to an elevation in the temperature of urban areas compared to their surroundings. Various research methodologies have been used to study and analyze this effect and to correlate it to climate change. It has been found that integrating green spaces within cities would positively impact urban heat islands by lowering their land surface temperature (LST). The main objective of this study is to investigate the spatial and temporal changes in Dubai’s LST over the last twenty three years and correlate them with Land Cover Land Use (LCLU) data. The study reveals an inverse urban heat island effect in the Dubai area, wherein the summer day temperature in urban areas is lower than the surrounding regions. Conversely, winter nights exhibit higher temperatures in urban areas. Furthermore, regions experiencing significant industrial growth were identified as statistically significant using the Mann-Kendall test.

A key aspect of a sustainable urban design is reducing expo-sure to air pollution by enhancing airflow and pollution dis-persal. Few research has been conducted to standardize the management of urban development procedures that account for air quality and human exposure to different gaseous pollutants. In this study, a time-space statistical hotspot analysis was carried out using high-resolution imagery of concentration for various air pollutants over the Emirate of Dubai. Spatial and temporal air quality patterns and their correlation with land-cover land-use (LCLU) were analyzed to map and quantify risks connected to air pollution and poor urban planning strategies. Afterward, using a Geographic Information System (GIS), a theoretical structure for urban management zones accounting for urban air circulation and less human exposure to air pollution was implemented, to identify areas with fewer ventilation processes. The methodology of this study can be implemented over different urban areas to identify poor air quality areas that require comprehensive investigation and to assist the development of urban planning strategies.

Autonomous vehicles (AVs) offer several benefits, such as improving road safety, mitigating traffic congestion, and reducing fuel consumption and gas emissions. Despite these benefits, their adoption rate remains limited due to various factors influencing users’ decisions. While previous studies have identified numerous factors influencing AV adoption using various adoption frameworks, the factors have not been comprehensively analyzed and synthesized. Thus, this systematic review aims to bridge this gap by identifying and classifying the factors influencing the adoption of AVs. Out of 3,532 collected research papers, 71 empirical studies were analyzed thoroughly. The findings demonstrated that the technology acceptance model (TAM) was the most widely used model for investigating AV adoption. The identified factors in the analyzed studies were classified into distinct categories: psychological and behavioral factors, technological factors, social factors, environmental factors, security and privacy factors, AV-related factors, risky and negative factors, conditional factors, and monetary factors. We have proposed an AV adoption framework grounded in this taxonomy to direct subsequent empirical research. We have also highlighted numerous agendas to serve as a blueprint for future AV adoption studies. This review offers various theoretical insights and actionable recommendations for multiple AV research, development, and implementation stakeholders.

Accurate flood mapping plays a critical role in disaster management, allowing for effective response and mitigation efforts. Thus, researchers seek to boost the accuracy of flood mapping algorithms, especially in terms of generalization capability and minimizing False Positive and False Negative detection. This paper presents a robust flood mapping algorithm from SAR images via Deep Convolutional Neural Network (DCNN) that follows encoder-decoder scheme. By introducing Bidirectional Convolutional LSTM (ConvLSTM) layers into its architecture, the proposed Temporal-Spatial Encoder-Decoder Network (TSEDN) network is able to extract temporal information and produce more accurate change maps. The training and testing are carried using OMBRIA dataset, which is known to be challenging to train. The proposed network is evaluated and compared to other state-of-the-art approaches in terms of Overall Accuracy (OA), Precision, Recall, and mean Intersection over Union (mIoU).

Mangroves are coastal ecosystems with enormous ecological benefits. These coastal protectors provide a living environment to many marine organisms, and it is considered a unique contributor against climate change in their carbon storage and sequestration process. Mangroves experience severe losses due to natural factors and intensive anthropogenic activities. Therefore, mapping, monitoring, and obtaining consistent recent information about these valuable resources is essential for conservation and protection. The United Arab Emirates (UAE) is the home of sixty million mangroves covering an area of more than 180 km2and storing 43,000 tons of carbon dioxide yearly [1]. The mangrove area located in the coastal region of UAE provides various benefits to the region and is considered a protective shield against the risk of erosion and sea intrusion. Therefore, UAE promised in the Conference of the Parties 2026 (COP26) to plant 100 million mangroves by the year 2030 [1]. Remote sensing and digital image processing techniques had proven to understand the mangrove ecosystem dynamics. Therefore, this study aims to investigate the changes in the Mangrove area and the effect of these changes on coastal erosion hazards over the last 20 years. The first step is to use a pixel-based machine learning (ML) classifiers along with multi-temporal, medium-resolution Landsat satellite images within Google Earth Engine (GEE) cloud computing platform, to create multi-temporal mangrove distribution maps of the UAE coastal area during the last 20 years. Second, qualitative and quantitative evaluations are conducted using ground truth data to validate the robustness of the proposed methodology and the accuracy of the results. Finally, coastline analysis is carried out using open-source tools, such as Digital Shoreline Analysis System (DSAS) [2] to estimate coastline changes and analyze coastal erosion risk. This method allows the identification of the mangrove gains and losses, as well as measurement of the change of coastline (accretion and erosion) over the past 20 years. The generated maps can lead to improvements in the ecosystems’ management and protection procedures. This study presents an effective workflow for mangrove detection and temporal mapping, using open-source medium-resolution satellite images, big data processing platforms, such as GEE, and open-source tools, such as DSAS.

Hyperspectral Imagery (HSI) classification is an important research area in remote sensing community due to its high efficiency in accurately analyzing ground features by assigning a class label to each pixel. This paper explores the use of Band Subset selection (BSS) methods as Dimensionality Reduction (DR) pre-processing stage for HSI classification, and compares them to Principal Component Analysis (PCA) approach. BSS is the problem of selecting the most independent bands in HSI cube. Classification is then performed using a proposed multi-branch HydraNet model that combines 1D, 2D, and 3D convolution. HydraNet is trained and tested using the benchmark Pavia University dataset, and the results are evaluated using Kappa and Overall Accuracy. Experimental results show positive indications of the network’s performance, especially when compared to other state-of-the-art CNN networks.

Autonomous detection and counting of palm trees is a research field of interest to various countries around the world, including the UAE. Automating this task saves effort and resources by minimizing human intervention and reducing potential errors in counting. This paper introduces a new High Resolution (HR) remote sensing dataset for autonomous detection of palm trees in the UAE. The dataset is collected using Unmanned Aerial Vehicles (UAV), and it is labeled properly in PASCAL VOC and YOLO formats after preprocessing and visually inspecting its quality. A comparative evaluation between Faster-RCNN and YOLOv4 networks is then conducted to observe the usability of the dataset in addition to the strengths and weaknesses of each network. The dataset is publicly available at https://github.com/Nour093/Palm-Tree-Dataset.

Using DWT-SVD and SHA3 Hash function, this research aims to develop an ownership protection and image authentication technique that embeds the watermark information and hash authentication key in a hybrid domain. The experiment was conducted with multispectral images from the KhalifaSat. The Performance of the proposed method is evaluated using wavelet domain signal to noise ratio (WSNR), structural similarity index measurement (SSIM) and peak signal to noise ratio (PSNR). To analyse the efficacy of the recovered watermark, two metrics are used: Normalized Correlation (NC) and Image Quality Index (IQI). The method presented is robust against many intended and unintended attacks. Without sacrificing transparency, our proposed watermarking approach meets the objectives of imperceptibility and robustness. It accurately detects the manipulated locations on the satellite image and is sensitive to even small changes.

Unmanned aerial vehicle (UAV) detection of moving vehicles is becoming into a significant study area in traffic control, surveillance, and military applications. The challenge arises in keeping minimal computational complexity allowing the system to be real-time as well. Applications of vehicle detection from UAVs include traffic parameter estimation, violation detection, number plate reading, and parking lot monitoring. The one stage detection model, YOLOv5 is used in this research work to develop a deep neural model-based vehicle detection system on highways from UAVs. In our system, several improvised strategies are put forth that are appropriate for small vehicle recognition under an aerial view angle which can accomplish real-time detection and high accuracy by incorporating an optimal pooling approach and dense topology method. Tilting the orientation of aerial photographs can improve the system's effectiveness. Metrics like hit rate, accuracy, and precision values are used to assess the performance of the proposed hybrid model, and performance is compared to that of other state-of-the-art algorithms.

Accurate and precise spatial hydrologic information is essential for effective management of natural resources, planning, and disaster response. Very high-resolution images and precise digital elevation models (DEMs) are crucial to accurately predict overflow in urban and mountainous regions; however, available course resolution DEMs with insufficient details cannot provide reliable overflow models. In this context, unmanned aerial vehicles (UAVs) offer a competitive alternative over satellites or airplanes and provide high spatial details essential for significant improvement of hydrological modeling. In this study, photogrammetric processing that includes stereo images captured via a fixed-wing drone were processed to generate a high-resolution DEM for the area surrounding the Hatta Dam in the United Arab Emirates. Three levels of details were introduced: data collection, photogrammetric processing, and hydrologic modeling. This study determined that flow modeling based on the UAV DEMs resulted in accurate hydrological modeling.

KhalifaSat is the third United Arab Emirates Earth observation satellite and the first manufactured by the Emirati engineers in the clean room of Mohammed Bin Rashid Space Centre (MBRSC), UAE. It follows the successful series of DubaiSat-1 and DubaiSat-2. KhalifaSat was launched into a sun-synchronous orbit of 613 km nominal altitude on 29thof October 2018 from Japan's Tanegashima Space Center. The MBRSC ground station, based in Dubai, controls the satellite and its functionality. The spacecraft provides very high resolution optical images which renders KhalifaSat to be utilized in various applications. The objective of this paper is to highlight the potential applications and usages of KhalifaSat imagery in a variety of domains.

Atmospheric aerosols play an important role in both climate forces and air quality. Different algorithms have been developed using satellite images to describe their spatial dispersal and evolution over time. In this study, a high-resolution, multispectral, polarized aerosol retrieval algorithm (PARA) was developed. Landsat TM Land Surface Reflectance products, and aerosols' data from the Optical Properties of Aerosols and Clouds database were used to retrieve the aerosols' optical properties over two locations within the Arabian plate. The Aerosol Robotic Network (AERONET) ground-based sun photometer measurements were used to validate the PARA. Validations showed that PARA retrievals are well correlated with AERONET measurements (R2= 0.965 and 0.967), by mean absolute error of 0.04 and 0.07, and root mean square error of 0.085 and 0.080. The PARA can describe aerosol components over different locations and provides detailed spatial distributions of aerosol optical properties.

This paper presents an image compression technique for DubaiSat-2 based on Discrete Cosine Transform (DCT). Initially, the image is divided into non-overlapping sub-blocks and transformed to a frequency domain using DCT. Then the thresholding technique is applied to eliminate lower energy coefficients. The resultant coefficients are quantized at a user specified bit rates. The proposed method is tested on both gray scale and color images. The performance of the proposed method is analyzed using Peak Signal-to-Noise Ratio (PSNR).